diff --git a/.gitea/workflows/backend_run_lint.yaml b/.gitea/workflows/backend_run_lint.yaml
index 51de7cc..e3fd418 100644
--- a/.gitea/workflows/backend_run_lint.yaml
+++ b/.gitea/workflows/backend_run_lint.yaml
@@ -30,5 +30,5 @@ jobs:
       working-directory: backend
 
     - name: Run linter
-      run: pipenv run pylint src
+      run: pipenv run pylint src --fail-under=9
       working-directory: backend
diff --git a/backend/.pylintrc b/backend/.pylintrc
index c6ee4d1..db084dd 100644
--- a/backend/.pylintrc
+++ b/backend/.pylintrc
@@ -439,7 +439,8 @@ disable=raw-checker-failed,
         use-symbolic-message-instead,
         use-implicit-booleaness-not-comparison-to-string,
         use-implicit-booleaness-not-comparison-to-zero,
-        import-error
+        import-error,
+        line-too-long
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option
diff --git a/backend/Pipfile b/backend/Pipfile
index 8d9b3da..4d06a94 100644
--- a/backend/Pipfile
+++ b/backend/Pipfile
@@ -23,3 +23,5 @@ osmpythontools = "*"
 pywikibot = "*"
 pymemcache = "*"
 fastapi-cli = "*"
+scikit-learn = "*"
+pyqt6 = "*"
diff --git a/backend/Pipfile.lock b/backend/Pipfile.lock
index b3b4f05..880b9a6 100644
--- a/backend/Pipfile.lock
+++ b/backend/Pipfile.lock
@@ -1,1928 +1,2047 @@
-{
-    "_meta": {
-        "hash": {
-            "sha256": "0af258788e2a8ae214f9af013cd7a7b19fb3adb941d4ea1de23902daf9062f5a"
-        },
-        "pipfile-spec": 6,
-        "requires": {},
-        "sources": [
-            {
-                "name": "pypi",
-                "url": "https://pypi.org/simple",
-                "verify_ssl": true
-            }
-        ]
-    },
-    "default": {
-        "annotated-types": {
-            "hashes": [
-                "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53",
-                "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==0.7.0"
-        },
-        "anyio": {
-            "hashes": [
-                "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c",
-                "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"
-            ],
-            "markers": "python_version >= '3.9'",
-            "version": "==4.6.2.post1"
-        },
-        "beautifulsoup4": {
-            "hashes": [
-                "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051",
-                "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"
-            ],
-            "markers": "python_full_version >= '3.6.0'",
-            "version": "==4.12.3"
-        },
-        "certifi": {
-            "hashes": [
-                "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8",
-                "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"
-            ],
-            "markers": "python_version >= '3.6'",
-            "version": "==2024.8.30"
-        },
-        "charset-normalizer": {
-            "hashes": [
-                "sha256:0099d79bdfcf5c1f0c2c72f91516702ebf8b0b8ddd8905f97a8aecf49712c621",
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-                "sha256:07afec21bbbbf8a5cc3651aa96b980afe2526e7f048fdfb7f1014d84acc8b6d8",
-                "sha256:0b309d1747110feb25d7ed6b01afdec269c647d382c857ef4663bbe6ad95a912",
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-                "sha256:0de7b687289d3c1b3e8660d0741874abe7888100efe14bd0f9fd7141bcbda92b",
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-                "sha256:1ffd9493de4c922f2a38c2bf62b831dcec90ac673ed1ca182fe11b4d8e9f2a64",
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diff --git a/backend/report.html b/backend/report.html
index c5a4bf3..d46b72d 100644
--- a/backend/report.html
+++ b/backend/report.html
@@ -328,7 +328,7 @@ div.media {
   </head>
   <body>
     <h1 id="title">Backend Testing Report</h1>
-    <p>Report generated on 30-Nov-2024 at 16:56:54 by <a href="https://pypi.python.org/pypi/pytest-html">pytest-html</a>
+    <p>Report generated on 13-Dec-2024 at 08:58:37 by <a href="https://pypi.python.org/pypi/pytest-html">pytest-html</a>
         v4.1.1</p>
     <div id="environment-header">
       <h2>Environment</h2>
@@ -382,7 +382,7 @@ div.media {
         <h2>Summary</h2>
         <div class="additional-summary prefix">
         </div>
-        <p class="run-count">10 tests took 00:00:11.</p>
+        <p class="run-count">12 tests took 865 ms.</p>
         <p class="filter">(Un)check the boxes to filter the results.</p>
         <div class="summary__reload">
           <div class="summary__reload__button hidden" onclick="location.reload()">
@@ -393,9 +393,9 @@ div.media {
         <div class="controls">
           <div class="filters">
             <input checked="true" class="filter" name="filter_checkbox" type="checkbox" data-test-result="failed" />
-            <span class="failed">1 Failed,</span>
+            <span class="failed">2 Failed,</span>
             <input checked="true" class="filter" name="filter_checkbox" type="checkbox" data-test-result="passed" />
-            <span class="passed">9 Passed,</span>
+            <span class="passed">10 Passed,</span>
             <input checked="true" class="filter" name="filter_checkbox" type="checkbox" data-test-result="skipped" disabled/>
             <span class="skipped">0 Skipped,</span>
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@@ -432,7 +432,7 @@ div.media {
     </table>
   </body>
   <footer>
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&#34;src/tests/test_invalid_input.py::test_input[start2-preferences2-422]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start2-preferences2-422]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start2-preferences2-422]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;2 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start3-preferences3-422]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start3-preferences3-422]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start3-preferences3-422]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;5 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start4-preferences4-423]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start4-preferences4-423]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start4-preferences4-423]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;3 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start5-preferences5-423]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start5-preferences5-423]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start5-preferences5-423]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;4 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start6-preferences6-423]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start6-preferences6-423]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start6-preferences6-423]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;2 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start7-preferences7-423]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start7-preferences7-423]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start7-preferences7-423]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;4 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_main.py::test_turckheim&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_main.py::test_turckheim&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_main.py::test_turckheim&lt;/td&gt;&#34;, &#34;&lt;td&gt;start (0 | 0) - 5 - Porte de France (257 | 5) - 5 - finish (0 | 0) - 0&lt;/td&gt;&#34;, &#34;&lt;td&gt;15 min&lt;/td&gt;&#34;, &#34;&lt;td&gt;15 min&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;21 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_main.py::test_bellecour&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Failed&#34;, &#34;testId&#34;: &#34;src/tests/test_main.py::test_bellecour&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Failed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_main.py::test_bellecour&lt;/td&gt;&#34;, &#34;&lt;td&gt;start (0 | 0) - 3 - M\u00e9morial Lyonnais du G\u00e9nocide Arm\u00e9nien (265 | 5) - 4 - \u00c9glise Saint-Fran\u00e7ois-de-Sales (211 | 5) - 4 - Chapelle des J\u00e9suites (161 | 5) - 7 - finish (0 | 0) - 0&lt;/td&gt;&#34;, &#34;&lt;td&gt;33 min&lt;/td&gt;&#34;, &#34;&lt;td&gt;30 min&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;00:00:11&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;client = &amp;lt;starlette.testclient.TestClient object at 0x770e13acd3d0&amp;gt;\nrequest = &amp;lt;FixtureRequest for &amp;lt;Function test_bellecour&amp;gt;&amp;gt;\n\n    def test_bellecour(client, request) :\n        &amp;quot;&amp;quot;&amp;quot;\n        Test n\u00b02 : Custom test in Lyon centre to ensure proper decision making in crowded area.\n    \n        Args:\n            client:\n            request:\n        &amp;quot;&amp;quot;&amp;quot;\n        duration_minutes = 30\n        response = client.post(\n            &amp;quot;/trip/new&amp;quot;,\n            json={\n                &amp;quot;preferences&amp;quot;: {&amp;quot;sightseeing&amp;quot;: {&amp;quot;type&amp;quot;: &amp;quot;sightseeing&amp;quot;, &amp;quot;score&amp;quot;: 5}, &amp;quot;nature&amp;quot;: {&amp;quot;type&amp;quot;: &amp;quot;nature&amp;quot;, &amp;quot;score&amp;quot;: 5}, &amp;quot;shopping&amp;quot;: {&amp;quot;type&amp;quot;: &amp;quot;shopping&amp;quot;, &amp;quot;score&amp;quot;: 5}, &amp;quot;max_time_minute&amp;quot;: duration_minutes, &amp;quot;detour_tolerance_minute&amp;quot;: 0},\n                &amp;quot;start&amp;quot;: [45.7576485, 4.8330241]\n                }\n            )\n        result = response.json()\n        landmarks = load_trip_landmarks(client, result[&amp;#x27;first_landmark_uuid&amp;#x27;])\n        osm_ids = landmarks_to_osmid(landmarks)\n    \n        # Add details to report\n        log_trip_details(request, landmarks, result[&amp;#x27;total_time&amp;#x27;], duration_minutes)\n    \n        # checks :\n        assert response.status_code == 200  # check for successful planning\n        assert duration_minutes*0.8 &amp;lt; int(result[&amp;#x27;total_time&amp;#x27;]) &amp;lt; duration_minutes*1.2\n&amp;gt;       assert 136200148 in osm_ids         # check for Cath\u00e9drale St. Jean in trip\nE       assert 136200148 in [0, 265922306, 82260098, 144904193, 0]\n\nsrc/tests/test_main.py:73: AssertionError\n&#34;}]}, &#34;renderCollapsed&#34;: [&#34;passed&#34;], &#34;initialSort&#34;: &#34;result&#34;, &#34;title&#34;: &#34;Backend Testing Report&#34;}"></div>
+    <div id="data-container" data-jsonblob="{&#34;environment&#34;: {&#34;Python&#34;: &#34;3.12.3&#34;, &#34;Platform&#34;: &#34;Linux-6.8.0-50-generic-x86_64-with-glibc2.39&#34;, &#34;Packages&#34;: {&#34;pytest&#34;: &#34;8.3.4&#34;, &#34;pluggy&#34;: &#34;1.5.0&#34;}, &#34;Plugins&#34;: {&#34;html&#34;: &#34;4.1.1&#34;, &#34;metadata&#34;: &#34;3.1.1&#34;, &#34;anyio&#34;: &#34;4.6.2.post1&#34;}}, &#34;tests&#34;: {&#34;src/tests/test_cache.py::test_cache&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_cache.py::test_cache&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_cache.py::test_cache&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;77 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start0-preferences0-422]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start0-preferences0-422]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start0-preferences0-422]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;2 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start1-preferences1-422]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start1-preferences1-422]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start1-preferences1-422]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;3 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start2-preferences2-422]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start2-preferences2-422]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start2-preferences2-422]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;3 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start3-preferences3-422]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start3-preferences3-422]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start3-preferences3-422]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;3 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start4-preferences4-422]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start4-preferences4-422]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start4-preferences4-422]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;4 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start5-preferences5-422]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start5-preferences5-422]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start5-preferences5-422]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;4 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start6-preferences6-422]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start6-preferences6-422]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start6-preferences6-422]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;2 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_invalid_input.py::test_input[start7-preferences7-422]&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_invalid_input.py::test_input[start7-preferences7-422]&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_invalid_input.py::test_input[start7-preferences7-422]&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td&gt;N/A&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;2 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_main.py::test_turckheim&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_main.py::test_turckheim&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_main.py::test_turckheim&lt;/td&gt;&#34;, &#34;&lt;td&gt;start (0 | 0) - 4 - La Fecht (99 | 5) - 4 - finish (0 | 0) - 0&lt;/td&gt;&#34;, &#34;&lt;td&gt;13 min&lt;/td&gt;&#34;, &#34;&lt;td&gt;15 min&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;19 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;No log output captured.&#34;}], &#34;src/tests/test_main.py::test_bellecour&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Failed&#34;, &#34;testId&#34;: &#34;src/tests/test_main.py::test_bellecour&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Failed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_main.py::test_bellecour&lt;/td&gt;&#34;, &#34;&lt;td&gt;start (0 | 0) - 3 - M\u00e9morial Lyonnais du G\u00e9nocide Arm\u00e9nien (265 | 5) - 4 - \u00c9glise Saint-Fran\u00e7ois-de-Sales (211 | 5) - 4 - Chapelle des J\u00e9suites (161 | 5) - 7 - finish (0 | 0) - 0&lt;/td&gt;&#34;, &#34;&lt;td&gt;33 min&lt;/td&gt;&#34;, &#34;&lt;td&gt;30 min&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;129 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;client = &amp;lt;starlette.testclient.TestClient object at 0x7eb8bb831310&amp;gt;\nrequest = &amp;lt;FixtureRequest for &amp;lt;Function test_bellecour&amp;gt;&amp;gt;\n\n    def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name\n        &amp;quot;&amp;quot;&amp;quot;\n        Test n\u00b02 : Custom test in Lyon centre to ensure proper decision making in crowded area.\n    \n        Args:\n            client:\n            request:\n        &amp;quot;&amp;quot;&amp;quot;\n        duration_minutes = 30\n        response = client.post(\n            &amp;quot;/trip/new&amp;quot;,\n            json={\n                &amp;quot;preferences&amp;quot;: {&amp;quot;sightseeing&amp;quot;: {&amp;quot;type&amp;quot;: &amp;quot;sightseeing&amp;quot;, &amp;quot;score&amp;quot;: 5},\n                                &amp;quot;nature&amp;quot;: {&amp;quot;type&amp;quot;: &amp;quot;nature&amp;quot;, &amp;quot;score&amp;quot;: 5},\n                                &amp;quot;shopping&amp;quot;: {&amp;quot;type&amp;quot;: &amp;quot;shopping&amp;quot;, &amp;quot;score&amp;quot;: 5},\n                                &amp;quot;max_time_minute&amp;quot;: duration_minutes,\n                                &amp;quot;detour_tolerance_minute&amp;quot;: 0},\n                &amp;quot;start&amp;quot;: [45.7576485, 4.8330241]\n                }\n            )\n        result = response.json()\n        landmarks = load_trip_landmarks(client, result[&amp;#x27;first_landmark_uuid&amp;#x27;])\n        osm_ids = landmarks_to_osmid(landmarks)\n    \n        # Add details to report\n        log_trip_details(request, landmarks, result[&amp;#x27;total_time&amp;#x27;], duration_minutes)\n    \n        # checks :\n        assert response.status_code == 200  # check for successful planning\n        assert duration_minutes*0.8 &amp;lt; int(result[&amp;#x27;total_time&amp;#x27;]) &amp;lt; duration_minutes*1.2\n&amp;gt;       assert 136200148 in osm_ids         # check for Cath\u00e9drale St. Jean in trip\nE       assert 136200148 in [0, 265922306, 82260098, 144904193, 0]\n\nsrc/tests/test_main.py:78: AssertionError\n&#34;}], &#34;src/tests/test_main.py::test_shopping&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Failed&#34;, &#34;testId&#34;: &#34;src/tests/test_main.py::test_shopping&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Failed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_main.py::test_shopping&lt;/td&gt;&#34;, &#34;&lt;td&gt;start (0 | 0) - 39 - P\u00f4le de Commerces et de Loisirs Confluence (237 | 30) - 47 - Cordeliers (554 | 45) - 8 - Grand H\u00f4tel-Dieu (519 | 30) - 25 - Muji (377 | 30) - 3 - Galeries Lafayette (267 | 30) - 2 - Cour Oxyg\u00e8ne (108 | 30) - 1 - HEMA (195 | 30) - 2 - Westfield La Part-Dieu (598 | 30) - 19 - Chinatown (106 | 45) - 16 - finish (0 | 0) - 0&lt;/td&gt;&#34;, &#34;&lt;td&gt;462 min&lt;/td&gt;&#34;, &#34;&lt;td&gt;600 min&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;616 ms&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;client = &amp;lt;starlette.testclient.TestClient object at 0x7eb8bb831310&amp;gt;\nrequest = &amp;lt;FixtureRequest for &amp;lt;Function test_shopping&amp;gt;&amp;gt;\n\n    def test_shopping(client, request) :   # pylint: disable=redefined-outer-name\n        &amp;quot;&amp;quot;&amp;quot;\n        Test n\u00b03 : Custom test in Lyon centre to ensure shopping clusters are found.\n    \n        Args:\n            client:\n            request:\n        &amp;quot;&amp;quot;&amp;quot;\n        duration_minutes = 600\n        response = client.post(\n            &amp;quot;/trip/new&amp;quot;,\n            json={\n                &amp;quot;preferences&amp;quot;: {&amp;quot;sightseeing&amp;quot;: {&amp;quot;type&amp;quot;: &amp;quot;sightseeing&amp;quot;, &amp;quot;score&amp;quot;: 0},\n                                &amp;quot;nature&amp;quot;: {&amp;quot;type&amp;quot;: &amp;quot;nature&amp;quot;, &amp;quot;score&amp;quot;: 0},\n                                &amp;quot;shopping&amp;quot;: {&amp;quot;type&amp;quot;: &amp;quot;shopping&amp;quot;, &amp;quot;score&amp;quot;: 5},\n                                &amp;quot;max_time_minute&amp;quot;: duration_minutes,\n                                &amp;quot;detour_tolerance_minute&amp;quot;: 0},\n                &amp;quot;start&amp;quot;: [45.7576485, 4.8330241]\n                }\n            )\n        result = response.json()\n        landmarks = load_trip_landmarks(client, result[&amp;#x27;first_landmark_uuid&amp;#x27;])\n        # osm_ids = landmarks_to_osmid(landmarks)\n    \n        # Add details to report\n        log_trip_details(request, landmarks, result[&amp;#x27;total_time&amp;#x27;], duration_minutes)\n    \n        # checks :\n        assert response.status_code == 200  # check for successful planning\n&amp;gt;       assert duration_minutes*0.8 &amp;lt; int(result[&amp;#x27;total_time&amp;#x27;]) &amp;lt; duration_minutes*1.2\nE       assert (600 * 0.8) &amp;lt; 462\nE        +  where 462 = int(462)\n\nsrc/tests/test_main.py:110: AssertionError\n&#34;}]}, &#34;renderCollapsed&#34;: [&#34;passed&#34;], &#34;initialSort&#34;: &#34;result&#34;, &#34;title&#34;: &#34;Backend Testing Report&#34;}"></div>
     <script>
       (function(){function r(e,n,t){function o(i,f){if(!n[i]){if(!e[i]){var c="function"==typeof require&&require;if(!f&&c)return c(i,!0);if(u)return u(i,!0);var a=new Error("Cannot find module '"+i+"'");throw a.code="MODULE_NOT_FOUND",a}var p=n[i]={exports:{}};e[i][0].call(p.exports,function(r){var n=e[i][1][r];return o(n||r)},p,p.exports,r,e,n,t)}return n[i].exports}for(var u="function"==typeof require&&require,i=0;i<t.length;i++)o(t[i]);return o}return r})()({1:[function(require,module,exports){
 const { getCollapsedCategory, setCollapsedIds } = require('./storage.js')
diff --git a/backend/src/main.py b/backend/src/main.py
index c6117cb..a2efbf1 100644
--- a/backend/src/main.py
+++ b/backend/src/main.py
@@ -1,13 +1,14 @@
 """Main app for backend api"""
 
 import logging
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Query
 
-from .structs.landmark import Landmark
+from .structs.landmark import Landmark, Toilets
 from .structs.preferences import Preferences
 from .structs.linked_landmarks import LinkedLandmarks
 from .structs.trip import Trip
 from .utils.landmarks_manager import LandmarkManager
+from .utils.toilets_manager import ToiletsManager
 from .utils.optimizer import Optimizer
 from .utils.refiner import Refiner
 from .persistence import client as cache_client
@@ -36,19 +37,15 @@ def new_trip(preferences: Preferences,
         (uuid) : The uuid of the first landmark in the optimized route
     """
     if preferences is None:
-        raise HTTPException(status_code=406,
-                            detail="Preferences not provided or incomplete.")
+        raise HTTPException(status_code=406, detail="Preferences not provided or incomplete.")
     if (preferences.shopping.score == 0 and
         preferences.sightseeing.score == 0 and
         preferences.nature.score == 0) :
-        raise HTTPException(status_code=406,
-                            detail="All preferences are 0.")
+        raise HTTPException(status_code=406, detail="All preferences are 0.")
     if start is None:
-        raise HTTPException(status_code=406,
-                            detail="Start coordinates not provided")
+        raise HTTPException(status_code=406, detail="Start coordinates not provided")
     if not (-90 <= start[0] <= 90 or -180 <= start[1] <= 180):
-        raise HTTPException(status_code=423,
-                            detail="Start coordinates not in range")
+        raise HTTPException(status_code=422, detail="Start coordinates not in range")
     if end is None:
         end = start
         logger.info("No end coordinates provided. Using start=end.")
@@ -61,7 +58,7 @@ def new_trip(preferences: Preferences,
                               attractiveness=0,
                               must_do=True,
                               n_tags = 0)
-    
+
     end_landmark = Landmark(name='finish',
                             type='finish',
                             location=(end[0], end[1]),
@@ -69,7 +66,7 @@ def new_trip(preferences: Preferences,
                             osm_id=0,
                             attractiveness=0,
                             must_do=True,
-                            n_tags = 0)
+                            n_tags=0)
 
     # Generate the landmarks from the start location
     landmarks, landmarks_short = manager.generate_landmarks_list(
@@ -134,5 +131,32 @@ def get_landmark(landmark_uuid: str) -> Landmark:
         landmark = cache_client.get(f"landmark_{landmark_uuid}")
         return landmark
     except KeyError as exc:
-        raise HTTPException(status_code=404,
-                            detail="Landmark not found") from exc
+        raise HTTPException(status_code=404, detail="Landmark not found") from exc
+
+
+@app.post("/toilets/new")
+def get_toilets(location: tuple[float, float] = Query(...), radius: int = 500) -> list[Toilets] :
+    """
+    Endpoint to find toilets within a specified radius from a given location.
+
+    This endpoint expects the `location` and `radius` as **query parameters**, not in the request body.
+
+    Args:
+        location (tuple[float, float]): The latitude and longitude of the location to search from.
+        radius (int, optional): The radius (in meters) within which to search for toilets. Defaults to 500 meters.
+
+    Returns:
+        list[Toilets]: A list of Toilets objects that meet the criteria.
+    """
+    if location is None:
+        raise HTTPException(status_code=406, detail="Coordinates not provided or invalid")
+    if not (-90 <= location[0] <= 90 or -180 <= location[1] <= 180):
+        raise HTTPException(status_code=422, detail="Start coordinates not in range")
+    
+    toilets_manager = ToiletsManager(location, radius)
+
+    try :
+        toilets_list = toilets_manager.generate_toilet_list()
+        return toilets_list
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail="No toilets found") from exc
diff --git a/backend/src/persistence.py b/backend/src/persistence.py
index 97bfaf0..97f9bb7 100644
--- a/backend/src/persistence.py
+++ b/backend/src/persistence.py
@@ -1,5 +1,5 @@
 """Module used for handling cache"""
-
+from pymemcache import serde
 from pymemcache.client.base import Client
 
 from .constants import MEMCACHED_HOST_PATH
@@ -70,5 +70,6 @@ else:
         MEMCACHED_HOST_PATH,
         timeout=1,
         allow_unicode_keys=True,
-        encoding='utf-8'
+        encoding='utf-8', 
+        serde=serde.pickle_serde
     )
diff --git a/backend/src/sandbox/bandung_data.json b/backend/src/sandbox/bandung_data.json
new file mode 100644
index 0000000..0576b59
--- /dev/null
+++ b/backend/src/sandbox/bandung_data.json
@@ -0,0 +1,4442 @@
+{
+    "type": "FeatureCollection",
+    "generator": "overpass-turbo",
+    "copyright": "The data included in this document is from www.openstreetmap.org. The data is made available under ODbL.",
+    "timestamp": "2024-12-01T16:57:45Z",
+    "features": [
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/119437950",
+          "addr:city": "Bandung",
+          "addr:housenumber": "113",
+          "addr:postcode": "40132",
+          "addr:street": "Jalan Ir. H. Juanda",
+          "amenity": "restaurant",
+          "building": "yes",
+          "building:levels": "1",
+          "building:roof": "tile",
+          "building:structure": "reinforced_masonry",
+          "building:use": "commercial",
+          "building:walls": "brick",
+          "name": "Babakaran x Taichi \"Goreng\"",
+          "opening_hours": "Mo-Fr 08:00-15:00",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.6121703,
+                -6.8925273
+              ],
+              [
+                107.6121626,
+                -6.8926664
+              ],
+              [
+                107.612662,
+                -6.8926936
+              ],
+              [
+                107.6126697,
+                -6.8925545
+              ],
+              [
+                107.6121703,
+                -6.8925273
+              ]
+            ]
+          ]
+        },
+        "id": "way/119437950"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/119438626",
+          "building": "yes",
+          "building:levels": "1",
+          "building:roof": "tile",
+          "building:structure": "reinforced_masonry",
+          "building:use": "commercial",
+          "building:walls": "brick",
+          "name": "Donatello",
+          "opening_hours": "Mo-Su 09:00-21:00",
+          "shop": "shoes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.6133702,
+                -6.8929284
+              ],
+              [
+                107.6133658,
+                -6.8931467
+              ],
+              [
+                107.6136889,
+                -6.893153
+              ],
+              [
+                107.6136932,
+                -6.8929347
+              ],
+              [
+                107.6133702,
+                -6.8929284
+              ]
+            ]
+          ]
+        },
+        "id": "way/119438626"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/119441849",
+          "building": "yes",
+          "building:levels": "2",
+          "building:roof": "tile",
+          "building:structure": "confined_masonry",
+          "building:use": "commercial",
+          "building:walls": "brick",
+          "name": "Morning Dew factory outlet",
+          "opening_hours": "Mo-Su 08:30-20:00",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.6120737,
+                -6.8980245
+              ],
+              [
+                107.6124941,
+                -6.8980706
+              ],
+              [
+                107.6125117,
+                -6.8979131
+              ],
+              [
+                107.6120912,
+                -6.897867
+              ],
+              [
+                107.6120737,
+                -6.8980245
+              ]
+            ]
+          ]
+        },
+        "id": "way/119441849"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/119442272",
+          "building": "yes",
+          "building:levels": "1",
+          "building:roof": "tile",
+          "building:structure": "reinforced_masonry",
+          "building:use": "commercial",
+          "building:walls": "brick",
+          "name": "FO Outlet",
+          "opening_hours": "Mo-Su 08:30-21:00",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.612726,
+                -6.8969386
+              ],
+              [
+                107.6123397,
+                -6.896938
+              ],
+              [
+                107.6123395,
+                -6.8970606
+              ],
+              [
+                107.6127258,
+                -6.8970612
+              ],
+              [
+                107.612726,
+                -6.8969386
+              ]
+            ]
+          ]
+        },
+        "id": "way/119442272"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/119444369",
+          "building": "yes",
+          "building:levels": "1",
+          "building:roof": "tile",
+          "building:structure": "reinforced_masonry",
+          "building:use": "commercial",
+          "building:walls": "brick",
+          "name": "Arella Big Size",
+          "opening_hours": "Mo-Su 09:30-21:00",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.613241,
+                -6.8964107
+              ],
+              [
+                107.6132368,
+                -6.8965313
+              ],
+              [
+                107.613517,
+                -6.8965408
+              ],
+              [
+                107.6135211,
+                -6.8964202
+              ],
+              [
+                107.613241,
+                -6.8964107
+              ]
+            ]
+          ]
+        },
+        "id": "way/119444369"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/119445948",
+          "building": "yes",
+          "building:levels": "1",
+          "building:roof": "tile",
+          "building:structure": "reinforced_masonry",
+          "building:use": "commercial",
+          "building:walls": "brick",
+          "name": "Runway FO",
+          "opening_hours": "Mo-Su 09:30-21:00",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.6135073,
+                -6.8956973
+              ],
+              [
+                107.6131882,
+                -6.8956941
+              ],
+              [
+                107.6131859,
+                -6.8959227
+              ],
+              [
+                107.6135049,
+                -6.8959259
+              ],
+              [
+                107.6135073,
+                -6.8956973
+              ]
+            ]
+          ]
+        },
+        "id": "way/119445948"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/119445949",
+          "building": "yes",
+          "building:levels": "1",
+          "building:roof": "tile",
+          "building:structure": "reinforced_masonry",
+          "building:use": "commercial",
+          "building:walls": "brick",
+          "name": "Fashion Galery",
+          "opening_hours": "Mo-Su 08:30-21:00",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.613165,
+                -6.8960427
+              ],
+              [
+                107.6131606,
+                -6.8961644
+              ],
+              [
+                107.6135099,
+                -6.8961648
+              ],
+              [
+                107.6135143,
+                -6.8960379
+              ],
+              [
+                107.613165,
+                -6.8960427
+              ]
+            ]
+          ]
+        },
+        "id": "way/119445949"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/119760178",
+          "addr:street": "Jalan Merdeka",
+          "building": "yes",
+          "building:levels": "2",
+          "building:roof": "tin",
+          "building:structure": "reinforced_masonry",
+          "building:use": "commercial",
+          "building:walls": "brick",
+          "name": "Merdeka Outlet",
+          "opening_hours": "Mo-Su 08:30-20:00",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.6104768,
+                -6.9085338
+              ],
+              [
+                107.6095601,
+                -6.9085285
+              ],
+              [
+                107.6095585,
+                -6.9087948
+              ],
+              [
+                107.6104752,
+                -6.9088001
+              ],
+              [
+                107.6104768,
+                -6.9085338
+              ]
+            ]
+          ]
+        },
+        "id": "way/119760178"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/119760343",
+          "building": "yes",
+          "building:levels": "3",
+          "building:roof": "concrete",
+          "building:structure": "reinforced_masonry",
+          "building:use": "shop",
+          "building:walls": "brick",
+          "name": "Istana Sepatu",
+          "shop": "shoes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.6104827,
+                -6.9094061
+              ],
+              [
+                107.6102143,
+                -6.9094076
+              ],
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+                -6.9095088
+              ],
+              [
+                107.6104833,
+                -6.9095073
+              ],
+              [
+                107.6104827,
+                -6.9094061
+              ]
+            ]
+          ]
+        },
+        "id": "way/119760343"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/120331565",
+          "building": "yes",
+          "building:levels": "1",
+          "building:roof": "tile",
+          "building:structure": "reinforced_masonry",
+          "building:use": "commercial",
+          "building:walls": "brick",
+          "name": "Level FO",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.6138225,
+                -6.8884867
+              ],
+              [
+                107.6138055,
+                -6.8887257
+              ],
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+                -6.8884957
+              ],
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+                -6.8884867
+              ]
+            ]
+          ]
+        },
+        "id": "way/120331565"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/120331567",
+          "building": "yes",
+          "building:levels": "1",
+          "building:roof": "asbestos",
+          "building:structure": "reinforced_masonry",
+          "building:use": "commercial",
+          "building:walls": "brick",
+          "name": "Glamour FO",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.6133772,
+                -6.892449
+              ],
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+              ],
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+                -6.892449
+              ]
+            ]
+          ]
+        },
+        "id": "way/120331567"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/120331568",
+          "building": "yes",
+          "building:levels": "1",
+          "building:roof": "asbestos",
+          "building:structure": "reinforced_masonry",
+          "building:use": "commercial",
+          "building:walls": "brick",
+          "name": "Blossom Factory Outlet",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.6134349,
+                -6.8920245
+              ],
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+              ],
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+                -6.8920245
+              ]
+            ]
+          ]
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+      },
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+        "type": "Feature",
+        "properties": {
+          "@id": "way/124283574",
+          "building": "yes",
+          "building:levels": "1",
+          "building:roof": "tile",
+          "building:structure": "unreinforced_masonry",
+          "building:use": "shop",
+          "building:walls": "brick",
+          "name": "Dusky store",
+          "name:en": "Dusky",
+          "shop": "shoes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
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+              ]
+            ]
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+        "id": "way/124283574"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/199373955",
+          "building": "yes",
+          "name": "B'scout",
+          "opening_hours": "24/7",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
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+        "id": "way/199373955"
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+        "type": "Feature",
+        "properties": {
+          "@id": "way/376352101",
+          "building": "yes",
+          "name": "Rabbani",
+          "shop": "clothes"
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+          "type": "Polygon",
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+          "@id": "way/377299637",
+          "building": "yes",
+          "name": "Haji Furniture",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.6142272,
+                -6.8839414
+              ],
+              [
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+                -6.8839843
+              ],
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+            ]
+          ]
+        },
+        "id": "way/377299637"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "way/377299639",
+          "building": "yes",
+          "name": "INGGRID Batik",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Polygon",
+          "coordinates": [
+            [
+              [
+                107.6140656,
+                -6.8843909
+              ],
+              [
+                107.6140514,
+                -6.8844406
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+                107.6142151,
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diff --git a/backend/src/sandbox/colmar_data.json b/backend/src/sandbox/colmar_data.json
new file mode 100644
index 0000000..7564a95
--- /dev/null
+++ b/backend/src/sandbox/colmar_data.json
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+        "geometry": {
+          "type": "Point",
+          "coordinates": [
+            7.3571756,
+            48.0772657
+          ]
+        },
+        "id": "node/10151865029"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "node/10862176110",
+          "name": "L'hexagone",
+          "shop": "bag"
+        },
+        "geometry": {
+          "type": "Point",
+          "coordinates": [
+            7.3808571,
+            48.0814138
+          ]
+        },
+        "id": "node/10862176110"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "node/11150877331",
+          "brand": "Punt Roma",
+          "brand:wikidata": "Q101423290",
+          "clothes": "women",
+          "name": "Punt Roma",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Point",
+          "coordinates": [
+            7.3571859,
+            48.0779406
+          ]
+        },
+        "id": "node/11150877331"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "node/11150959880",
+          "name": "Caroll",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Point",
+          "coordinates": [
+            7.3579354,
+            48.0779291
+          ]
+        },
+        "id": "node/11150959880"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "node/11302242094",
+          "branch": "Wintzenheim",
+          "name": "Label Fripe",
+          "opening_hours": "Mo-Sa 09:00-18:45",
+          "phone": "+33 3 89 27 39 25",
+          "second_hand": "only",
+          "shop": "clothes",
+          "website": "https://labelfripe.fr/label-fripe-wintzenheim/"
+        },
+        "geometry": {
+          "type": "Point",
+          "coordinates": [
+            7.3109899,
+            48.0850362
+          ]
+        },
+        "id": "node/11302242094"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "node/11392247003",
+          "name": "Lingerie Sipp",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Point",
+          "coordinates": [
+            7.3111507,
+            48.0841835
+          ]
+        },
+        "id": "node/11392247003"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "node/11778819781",
+          "addr:city": "Colmar",
+          "addr:housenumber": "10",
+          "addr:postcode": "68000",
+          "addr:street": "Rue des Têtes",
+          "clothes": "suits;hats;men",
+          "name": "Phillipe",
+          "phone": "0389411983",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Point",
+          "coordinates": [
+            7.3559389,
+            48.0789064
+          ]
+        },
+        "id": "node/11778819781"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "node/11799215969",
+          "brand": "Petit Bateau",
+          "brand:wikidata": "Q3377090",
+          "name": "Petit Bateau",
+          "opening_hours": "Mo-Sa 10:00-19:00; Su 10:00-18:00",
+          "phone": "+33 3 89 24 97 85",
+          "shop": "clothes",
+          "website": "https://stores.petit-bateau.com/france/colmar/9-rue-des-boulangers"
+        },
+        "geometry": {
+          "type": "Point",
+          "coordinates": [
+            7.355149,
+            48.0780213
+          ]
+        },
+        "id": "node/11799215969"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "node/11816704669",
+          "addr:housenumber": "10",
+          "addr:street": "Rue des Boulangers",
+          "name": "des petits hauts",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Point",
+          "coordinates": [
+            7.3555001,
+            48.0780768
+          ]
+        },
+        "id": "node/11816704669"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "node/12320343534",
+          "addr:city": "Colmar",
+          "addr:housenumber": "44",
+          "addr:postcode": "68000",
+          "addr:street": "Rue des Clefs",
+          "brand": "Un Jour Ailleurs",
+          "brand:wikidata": "Q105106211",
+          "clothes": "women",
+          "name": "Un Jour Ailleurs",
+          "opening_hours": "Mo-Fr 10:00-19:00; Sa 10:00-18:30",
+          "phone": "+33368318572",
+          "shop": "clothes",
+          "website": "https://boutique.unjourailleurs.com/fr/mode-femme/boutique-colmar-76"
+        },
+        "geometry": {
+          "type": "Point",
+          "coordinates": [
+            7.35897,
+            48.0789807
+          ]
+        },
+        "id": "node/12320343534"
+      },
+      {
+        "type": "Feature",
+        "properties": {
+          "@id": "node/12320343536",
+          "addr:city": "Colmar",
+          "addr:housenumber": "38",
+          "addr:postcode": "68000",
+          "addr:street": "Rue des Clefs",
+          "brand": "Timberland",
+          "brand:wikidata": "Q1539185",
+          "name": "Timberland",
+          "opening_hours": "Mo-Sa 10:00-19:00",
+          "phone": "+33389298650",
+          "shop": "clothes"
+        },
+        "geometry": {
+          "type": "Point",
+          "coordinates": [
+            7.3592409,
+            48.0788785
+          ]
+        },
+        "id": "node/12320343536"
+      }
+    ]
+  }
\ No newline at end of file
diff --git a/backend/src/sandbox/get_streets.py b/backend/src/sandbox/get_streets.py
new file mode 100644
index 0000000..7940017
--- /dev/null
+++ b/backend/src/sandbox/get_streets.py
@@ -0,0 +1,350 @@
+# pylint: skip-file
+
+import numpy as np
+import json
+import os
+from typing import Optional, Literal
+from sklearn.cluster import DBSCAN
+from sklearn.decomposition import PCA
+import matplotlib.pyplot as plt
+from pydantic import BaseModel
+from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
+from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
+from math import sin, cos, sqrt, atan2, radians
+
+
+EARTH_RADIUS_KM = 6373
+
+
+class ShoppingLocation(BaseModel):
+    type: Literal['street', 'area']
+    importance: int
+    centroid: tuple
+    start: Optional[list] = None
+    end: Optional[list] = None
+
+
+# Output to frontend
+class Landmark(BaseModel) :
+    # Properties of the landmark
+    name : str
+    type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
+    location : tuple
+    osm_type : str
+    osm_id : int
+    attractiveness : int
+    n_tags : int
+    image_url : Optional[str] = None
+    website_url : Optional[str] = None
+    description : Optional[str] = None                          # TODO future
+    duration : Optional[int] = 0
+    name_en : Optional[str] = None
+
+    # Additional properties depending on specific tour
+    must_do : Optional[bool] = False
+    must_avoid : Optional[bool] = False
+    is_secondary : Optional[bool] = False
+
+    time_to_reach_next : Optional[int] = 0
+    next_uuid : Optional[str] = None
+
+
+def extract_points(filestr: str) :
+    """
+    Extract points from geojson file.
+
+    Returns :
+        np.array containing the points
+    """
+    points = []
+
+    with open(os.path.dirname(__file__) + '/' + filestr, 'r') as f:
+        geojson = json.load(f)
+
+        for feature in geojson['features']:
+            if feature['geometry']['type'] == 'Point':
+                centroid = feature['geometry']['coordinates']
+                points.append(centroid)
+
+            elif feature['geometry']['type'] == 'Polygon':
+                centroid = np.array(feature['geometry']['coordinates'][0][0])
+                points.append(centroid)
+    
+    # Convert the list of points to a NumPy array
+    return np.array(points)
+
+
+def get_distance(p1: tuple[float, float], p2: tuple[float, float]) -> int:
+    """
+    Calculate the time in minutes to travel from one location to another.
+
+    Args:
+        p1 (tuple[float, float]): Coordinates of the starting location.
+        p2 (tuple[float, float]): Coordinates of the destination.
+
+        Returns:
+        int: Time to travel from p1 to p2 in minutes.
+    """
+
+
+    if p1 == p2:
+        return 0
+    else:
+        # Compute the distance in km along the surface of the Earth
+        # (assume spherical Earth)
+        # this is the haversine formula, stolen from stackoverflow
+        # in order to not use any external libraries
+        lat1, lon1 = radians(p1[0]), radians(p1[1])
+        lat2, lon2 = radians(p2[0]), radians(p2[1])
+
+        dlon = lon2 - lon1
+        dlat = lat2 - lat1
+
+        a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
+        c = 2 * atan2(sqrt(a), sqrt(1 - a))
+
+        return EARTH_RADIUS_KM * c
+
+def filter_clusters(cluster_points, cluster_labels):
+    """
+    Remove clusters of less importance.
+    """
+    label_counts = np.bincount(cluster_labels)
+
+    # Step 3: Get the indices (labels) of the 5 largest clusters
+    top_5_labels = np.argsort(label_counts)[-5:]  # Get the largest 5 clusters
+
+    # Step 4: Filter points to keep only the points in the top 5 clusters
+    filtered_cluster_points = []
+    filtered_cluster_labels = []
+
+    for label in top_5_labels:
+        filtered_cluster_points.append(cluster_points[cluster_labels == label])
+        filtered_cluster_labels.append(np.full((label_counts[label],), label))  # Replicate the label
+
+    # Concatenate filtered clusters into a single array
+    return np.vstack(filtered_cluster_points), np.concatenate(filtered_cluster_labels)
+
+
+def fit_lines(points, labels):
+    """
+    Fit lines to identified clusters.
+    """
+    all_x = []
+    all_y = []
+    lines = []
+    locations = []
+
+    for label in set(labels):
+        cluster_points = points[labels == label]
+
+        # If there's not enough points, skip
+        if len(cluster_points) < 2:
+            continue
+
+        # Apply PCA to find the principal component (i.e., the line of best fit)
+        pca = PCA(n_components=1)
+        pca.fit(cluster_points)
+
+        direction = pca.components_[0]
+        centroid = pca.mean_
+
+        # Project the cluster points onto the principal direction (line direction)
+        projections = np.dot(cluster_points - centroid, direction)
+
+        # Get the range of the projections to find the approximate length of the cluster
+        cluster_length = projections.max() - projections.min()
+
+        # Now adjust `t` so that it scales with the cluster length
+        t = np.linspace(-cluster_length / 2.75, cluster_length / 2.75, 10)
+
+        # Calculate the start and end of the line based on min/max projections
+        start_point = centroid[0] + t*direction[0]
+        end_point = centroid[1] + t*direction[1]
+        
+        # Store the line
+        lines.append((start_point, end_point))
+
+        # For visualization, store the points
+        all_x.append(min(start_point))
+        all_x.append(max(start_point))
+        all_y.append(min(end_point))
+        all_y.append(max(end_point))
+
+        if np.linalg.norm(t) <= 0.0045 :
+            loc = ShoppingLocation(
+                type='area',
+                centroid=tuple((centroid[1], centroid[0])),
+                importance = len(cluster_points),
+            )
+        else :
+            loc = ShoppingLocation(
+                type='street',
+                centroid=tuple((centroid[1], centroid[0])),
+                importance = len(cluster_points),
+                start=start_point,
+                end=end_point
+            )
+
+        locations.append(loc)
+
+    xmin = min(all_x)
+    xmax = max(all_x)
+    ymin = min(all_y)
+    ymax = max(all_y)
+    corners = (xmin, xmax, ymin, ymax)
+
+    return corners, locations
+
+
+
+def create_landmark(shopping_location: ShoppingLocation):
+
+    # Define the bounding box for a given radius around the coordinates
+    lat, lon = shopping_location.centroid
+    bbox = ("around:1000", str(lat), str(lon))
+    
+    overpass = Overpass()
+    # CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
+
+    # Query neighborhoods and shopping malls
+    selectors = ['"place"~"^(suburb|neighborhood|neighbourhood|quarter|city_block)$"', '"shop"="mall"']
+
+    min_dist = float('inf')
+    new_name = 'Shopping Area'
+    new_name_en = None
+    osm_id = 0
+    osm_type = 'node'
+
+    for sel in selectors : 
+        query = overpassQueryBuilder(
+            bbox = bbox,
+            elementType = ['node', 'way', 'relation'],
+            selector = sel,
+            includeCenter = True,
+            out = 'center'
+        )
+
+        try:
+            result = overpass.query(query)
+        except Exception as e:
+            raise Exception("query unsuccessful")
+
+        for elem in result.elements():
+
+            location = (elem.centerLat(), elem.centerLon())
+
+            if location[0] is None : 
+                location = (elem.lat(), elem.lon())
+                if location[0] is None : 
+                    # print(f"Fetching coordinates failed with {elem.type()}/{elem.id()}")
+                    continue
+
+            # print(f"Distance : {get_distance(shopping_location.centroid, location)}")
+            d = get_distance(shopping_location.centroid, location)
+            if  d < min_dist :
+                min_dist = d
+                new_name = elem.tag('name')
+                osm_type = elem.type()              # Add type: 'way' or 'relation'
+                osm_id = elem.id()                  # Add OSM id 
+
+                # add english name if it exists
+                try :
+                    new_name_en = elem.tag('name:en')
+                except:
+                    pass 
+    
+    return Landmark(
+        name=new_name,
+        type='shopping',
+        location=shopping_location.centroid,              # TODO: use the fact the we can also recognize streets.
+        attractiveness=shopping_location.importance,
+        n_tags=0,
+        osm_id=osm_id,
+        osm_type=osm_type,
+        name_en=new_name_en
+    )
+
+
+# Extract points
+points = extract_points('vienna_data.json')
+
+# print(len(points))
+
+######## Create a figure with 1 row and 3 columns for side-by-side plots
+fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+# Plot Raw data points
+axes[0].set_title('Raw Data')
+axes[0].scatter(points[:, 0], points[:, 1], color='blue', s=20)
+
+
+# Apply DBSCAN to find clusters. Choose different settings for different cities.
+if len(points) > 400 :
+    dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree')  # for large cities
+else :
+    dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree')  # for small cities
+
+labels = dbscan.fit_predict(points)
+
+# Separate clustered points and noise points
+clustered_points = points[labels != -1]
+clustered_labels = labels[labels != -1]
+noise_points = points[labels == -1] 
+
+######## Plot n°1: DBSCAN Clustering Results
+axes[1].set_title('DBSCAN Clusters')
+axes[1].scatter(clustered_points[:, 0], clustered_points[:, 1], c=clustered_labels, cmap='rainbow', s=20)
+axes[1].scatter(noise_points[:, 0], noise_points[:, 1], c='blue', s=7, label='Noise')
+
+# Keep the 5 biggest clusters
+clustered_points, clustered_labels = filter_clusters(clustered_points, clustered_labels)
+
+# Fit lines
+corners, locations = fit_lines(clustered_points, clustered_labels)
+(xmin, xmax, ymin, ymax) = corners
+
+
+######## Plot clustered points in normal size and noise points separately
+axes[2].scatter(clustered_points[:, 0], clustered_points[:, 1], c=clustered_labels, cmap='rainbow', s=30)
+axes[2].set_title('PCA Fitted Lines on Clusters')
+
+# Create a list of Landmarks for the shopping things
+shopping_landmarks = []
+for loc in locations :
+    axes[2].scatter(loc.centroid[1], loc.centroid[0], color='red', marker='x', s=200, linewidth=3)
+    landmark = create_landmark(loc)
+    shopping_landmarks.append(landmark)
+    axes[2].text(loc.centroid[1], loc.centroid[0], landmark.name, 
+             ha='center', va='top', fontsize=6, 
+             bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.2'),
+             zorder=3)
+    
+    
+
+####### Plot the detected lines in the final plot #######
+# for loc in locations:
+#     if loc.type == 'street' :
+#         line_x = loc.start
+#         line_y = loc.end
+#         axes[2].plot(line_x, line_y, color='lime', linewidth=3)
+#     else :
+
+
+
+axes[0].set_xlim(xmin-0.01, xmax+0.01)
+axes[0].set_ylim(ymin-0.01, ymax+0.01)
+
+axes[1].set_xlim(xmin-0.01, xmax+0.01)
+axes[1].set_ylim(ymin-0.01, ymax+0.01)
+
+axes[2].set_xlim(xmin-0.01, xmax+0.01)
+axes[2].set_ylim(ymin-0.01, ymax+0.01)
+
+
+print("\n\n\n")
+for landmark in shopping_landmarks :
+    print(f"{landmark.name} is a shopping area with a score of {landmark.attractiveness}")
+
+
+plt.tight_layout()
+plt.show()
diff --git a/backend/src/sandbox/lyon_data.json b/backend/src/sandbox/lyon_data.json
new file mode 100644
index 0000000..a73e70b
--- /dev/null
+++ b/backend/src/sandbox/lyon_data.json
@@ -0,0 +1,17824 @@
+{
+  "type": "FeatureCollection",
+  "generator": "overpass-turbo",
+  "copyright": "The data included in this document is from www.openstreetmap.org. The data is made available under ODbL.",
+  "timestamp": "2024-12-02T21:03:57Z",
+  "features": [
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/95836772",
+        "name": "KL Destockage",
+        "shop": "shoes",
+        "survey:date": "2020-12-26"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          4.8442915,
+          45.7445962
+        ]
+      },
+      "id": "node/95836772"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/273204887",
+        "brand": "Zadig & Voltaire",
+        "brand:wikidata": "Q3574548",
+        "brand:wikipedia": "fr:Zadig & Voltaire",
+        "name": "Zadig & Voltaire",
+        "opening_hours": "Mo-Sa 10:00-19:00",
+        "shop": "clothes",
+        "survey:date": "2023-06-18",
+        "wheelchair": "limited"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          4.8342714,
+          45.7624072
+        ]
+      },
+      "id": "node/273204887"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/273209129",
+        "name": "By Johanne",
+        "opening_hours": "Mo-Sa 10:00-19:00",
+        "ref:FR:SIRET": "50062971200075",
+        "shop": "clothes",
+        "website": "http://www.by-johanne.com"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          4.8322113,
+          45.7595615
+        ]
+      },
+      "id": "node/273209129"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/539430205",
+        "addr:city": "Lyon",
+        "addr:country": "FR",
+        "addr:postcode": "69003",
+        "check_date:opening_hours": "2024-04-19",
+        "name": "Milpau",
+        "opening_hours:signed": "no",
+        "ref:FR:SIRET": "33764419900024",
+        "shop": "clothes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          4.8410213,
+          45.7583034
+        ]
+      },
+      "id": "node/539430205"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/730495372",
+        "brand": "Intersport",
+        "clothes": "sports",
+        "opening_hours": "Mo-Sa 10:00-19:00",
+        "shop": "clothes",
+        "wheelchair": "yes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          4.879155,
+          45.7693615
+        ]
+      },
+      "id": "node/730495372"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/832463266",
+        "name": "Liberty's",
+        "ref:FR:SIRET": "43424030500012",
+        "shop": "clothes",
+        "source": "cadastre-dgi-fr source : Direction Générale des Impôts - Cadastre. Mise à jour : 2010"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          4.8117884,
+          45.7578022
+        ]
+      },
+      "id": "node/832463266"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/1088477421",
+        "name": "D'une Idée à l'Autre",
+        "opening_hours": "Tu-Sa 10:00-12:30,15:00-19:00; Su 10:00-12:30",
+        "shop": "clothes",
+        "wheelchair": "no"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          4.8170133,
+          45.8143381
+        ]
+      },
+      "id": "node/1088477421"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/1391026568",
+        "check_date:opening_hours": "2024-02-03",
+        "contact:facebook": "kanopeestore",
+        "name": "Kanopee",
+        "opening_hours": "Mo-Fr 10:00-19:00; Sa 10:00-19:30",
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+}
\ No newline at end of file
diff --git a/backend/src/sandbox/newyork_data.json b/backend/src/sandbox/newyork_data.json
new file mode 100644
index 0000000..e40935c
--- /dev/null
+++ b/backend/src/sandbox/newyork_data.json
@@ -0,0 +1,42085 @@
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+          "payment:visa": "yes",
+          "shop": "bag",
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+        "id": "node/12393975438"
+      }
+    ]
+  }
\ No newline at end of file
diff --git a/backend/src/sandbox/paris_data.json b/backend/src/sandbox/paris_data.json
new file mode 100644
index 0000000..1977820
--- /dev/null
+++ b/backend/src/sandbox/paris_data.json
@@ -0,0 +1,83615 @@
+{
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+        "contact:postcode": "75001",
+        "contact:street": "Avenue de l'Opéra",
+        "contact:website": "https://www.antoine1745.com",
+        "name": "Antoine",
+        "opening_hours": "Tu-Sa 10:30-13:00,14:00-18:30",
+        "shop": "clothes",
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+        "name": "Jonas et Cie (Jean-Claude et Laurent Touboul)",
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+}
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diff --git a/backend/src/sandbox/strasbourg_data.json b/backend/src/sandbox/strasbourg_data.json
new file mode 100644
index 0000000..7bef5b0
--- /dev/null
+++ b/backend/src/sandbox/strasbourg_data.json
@@ -0,0 +1,4947 @@
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diff --git a/backend/src/sandbox/vienna_data.json b/backend/src/sandbox/vienna_data.json
new file mode 100644
index 0000000..c0aa5d6
--- /dev/null
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+}
\ No newline at end of file
diff --git a/backend/src/sandbox/winterthur_data.json b/backend/src/sandbox/winterthur_data.json
new file mode 100644
index 0000000..3abfef7
--- /dev/null
+++ b/backend/src/sandbox/winterthur_data.json
@@ -0,0 +1,2844 @@
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+  }
\ No newline at end of file
diff --git a/backend/src/sandbox/zurich_data.json b/backend/src/sandbox/zurich_data.json
new file mode 100644
index 0000000..63e072b
--- /dev/null
+++ b/backend/src/sandbox/zurich_data.json
@@ -0,0 +1,16070 @@
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+        "shop": "shoes",
+        "website": "https://www.bencibrothers.com/",
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+        "website": "https://www.schuhhaus-graeb.ch/",
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+        "shop": "shoes",
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+        "shop": "clothes"
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+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.5526341,
+          47.3606051
+        ]
+      },
+      "id": "node/12037362411"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12051020438",
+        "addr:city": "Zürich",
+        "addr:housenumber": "1",
+        "addr:postcode": "8001",
+        "addr:street": "Limmatquai",
+        "check_date": "2024-07-13",
+        "clothes": "women",
+        "name": "Panta Rheï",
+        "opening_hours": "Mo-Fr 10:00-18:30; Sa 10:00-17:00; Su off; PH off",
+        "operator": "Panta Rheï Textil GmbH",
+        "payment:cash": "yes",
+        "payment:coins": "yes",
+        "payment:debit_cards": "yes",
+        "shop": "clothes",
+        "wheelchair": "yes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.5447811,
+          47.3676052
+        ]
+      },
+      "id": "node/12051020438"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12051469483",
+        "level": "0",
+        "name": "Esprit",
+        "shop": "clothes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.4684522,
+          47.4313855
+        ]
+      },
+      "id": "node/12051469483"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12051469488",
+        "level": "1",
+        "name": "Blackout",
+        "shop": "clothes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.4699772,
+          47.4310065
+        ]
+      },
+      "id": "node/12051469488"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12051469489",
+        "level": "1",
+        "name": "Marc O'Polo",
+        "shop": "clothes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.4699405,
+          47.4309327
+        ]
+      },
+      "id": "node/12051469489"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12051469490",
+        "level": "1",
+        "name": "Street One | Cecil",
+        "shop": "clothes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.4699092,
+          47.4308696
+        ]
+      },
+      "id": "node/12051469490"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12051524070",
+        "level": "1",
+        "name": "Boutique Insieme",
+        "shop": "clothes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.448897,
+          47.3967958
+        ]
+      },
+      "id": "node/12051524070"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12068099083",
+        "level": "2",
+        "name": "Karma Mode",
+        "shop": "clothes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.4485387,
+          47.396848
+        ]
+      },
+      "id": "node/12068099083"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12090984371",
+        "check_date": "2024-08-03",
+        "description": "Schneiderei, Secondhand, Reinigung",
+        "name": "Jiki",
+        "second_hand": "only",
+        "shop": "clothes",
+        "website": "https://jiki.ch",
+        "wheelchair": "no"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.5417698,
+          47.3866482
+        ]
+      },
+      "id": "node/12090984371"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12090984375",
+        "check_date": "2024-08-03",
+        "name": "JDM Outlet",
+        "shop": "clothes",
+        "wheelchair": "no"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.541808,
+          47.3865865
+        ]
+      },
+      "id": "node/12090984375"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12092402109",
+        "check_date": "2024-08-04",
+        "description": "Atelierladen, Kurse, Kunsttherapie, Events, Designbude",
+        "name": "Atelier Kornhaus",
+        "shop": "clothes",
+        "wheelchair": "no"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.5360257,
+          47.3897622
+        ]
+      },
+      "id": "node/12092402109"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12137530092",
+        "name": "Front Rowolution",
+        "second_hand": "only",
+        "shop": "clothes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.5142342,
+          47.3712828
+        ]
+      },
+      "id": "node/12137530092"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12138506366",
+        "check_date": "2024-08-24",
+        "name": "Saint James",
+        "shop": "clothes",
+        "wheelchair": "no"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.5288391,
+          47.3751392
+        ]
+      },
+      "id": "node/12138506366"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12138506369",
+        "check_date": "2024-08-24",
+        "name": "Gardenia",
+        "shop": "clothes",
+        "wheelchair": "no"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.5290311,
+          47.3749882
+        ]
+      },
+      "id": "node/12138506369"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12169958444",
+        "addr:housenumber": "31",
+        "addr:street": "Dufourstrasse",
+        "check_date": "2024-09-11",
+        "name": "IVY",
+        "shop": "clothes",
+        "website": "https://yvy.ch/pages/visit-us-yvy-store",
+        "wheelchair": "yes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.5483357,
+          47.3632617
+        ]
+      },
+      "id": "node/12169958444"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12253900004",
+        "addr:city": "Zürich",
+        "addr:housenumber": "757",
+        "addr:postcode": "8048",
+        "addr:street": "Badenerstrasse",
+        "description": "Begleitetes Nähen, Stilberatung, Couture-Aenderungen & textile Projekte auf Termin",
+        "email": "denicola.adriana@gmail.com",
+        "name": "Lavoratorio Tessile c/o Offcut Zürich",
+        "operator": "Adriana de Nicola",
+        "repair": "yes",
+        "shop": "clothes",
+        "website": "https://www.adrianadenicola.ch/"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.4779515,
+          47.3914529
+        ]
+      },
+      "id": "node/12253900004"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12253939400",
+        "addr:city": "Zürich",
+        "addr:housenumber": "52",
+        "addr:postcode": "8003",
+        "addr:street": "Zentralstrasse",
+        "description": "Modedesignshop und Atelier\nSeit 2005 führe ich den Laden an der Zentralstrasse 52. Hier bringe ich meine Ideen auf Papier, zeichne die Schnittmuster und nähe die ersten Prototypen.",
+        "email": "kathrinwegmann@elfe11.ch",
+        "name": "Elfe 11",
+        "opening_hours": "Do/Fr 9:30-18:30, Sa 10:00-17:00",
+        "operator": "Kathrin Wegmann",
+        "phone": "+41 49 408 40 83",
+        "shop": "clothes",
+        "website": "https://www.elfe11.ch/"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.519232,
+          47.3721562
+        ]
+      },
+      "id": "node/12253939400"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12263097143",
+        "addr:city": "Zürich",
+        "addr:housenumber": "10",
+        "addr:postcode": "8004",
+        "addr:street": "Lagerstrasse",
+        "clothes:repair": "yes",
+        "description": "REWORK formt die textile Kette in einen Kreislauf um. Unsere Kollektionen werden umweltschonend aus Secondhandkleidern hergestellt, die wir auseinander schneiden und neu zusammennähen.",
+        "description:2": "Aktion am Repair Thursday<br />bunte Patches für kleine Löcher/Flecken aufsticken lassen oder selbst aufsticken.<br />Ganzer Tag: Schneider Rabie ist da für kostenpflichtige Reparaturen.<br />https://zuerich.repair/wp-content/uploads/2024/11/REWORK.pdf",
+        "email": "contact@rework.ch",
+        "name": "Rework Zürich Europaallee",
+        "opening_hours": "Mo-Fr 10:00-19:00, Sa 10:00-18:00",
+        "opening_hours:description": "Mo-Fr 10-19 Uhr, Sa 10-18 Uhr",
+        "operator": "Rework AG",
+        "phone": "+41 44 545 12 12",
+        "repair": "yes",
+        "shop": "clothes",
+        "website": "https://rework.ch"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.5340216,
+          47.3772532
+        ]
+      },
+      "id": "node/12263097143"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12288912536",
+        "addr:city": "Zürich",
+        "addr:housenumber": "23",
+        "addr:postcode": "8005",
+        "addr:street": "Geroldstrasse",
+        "branch": "WE ARE ZRCL Brand Store Frau Gerold",
+        "clothes:repair": "only_sold",
+        "email": "fraugerold@wearezrcl.com",
+        "level": "0",
+        "name": "WE ARE ZRCL",
+        "not:brand:wikidata": "Q1987861",
+        "opening_hours": "Mo-Fr 12:00-19:00,Sa 11:00-18:00",
+        "phone": "+41 44 283 67 67",
+        "shop": "clothes",
+        "website": "https://wearezrcl.com"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.5192422,
+          47.3854316
+        ]
+      },
+      "id": "node/12288912536"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12290351590",
+        "check_date": "2024-10-26",
+        "name": "REmember Vintage",
+        "second_hand": "only",
+        "shop": "clothes",
+        "wheelchair": "no"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.5434315,
+          47.371263
+        ]
+      },
+      "id": "node/12290351590"
+    },
+    {
+      "type": "Feature",
+      "properties": {
+        "@id": "node/12366999974",
+        "description": "Second hand outdoor shop",
+        "name": "2nd Peak",
+        "opening_hours": "Mo-Fr 10:00-19:00; Sa 11:00-17:00",
+        "shop": "clothes"
+      },
+      "geometry": {
+        "type": "Point",
+        "coordinates": [
+          8.533501,
+          47.3780343
+        ]
+      },
+      "id": "node/12366999974"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/backend/src/structs/landmark.py b/backend/src/structs/landmark.py
index b5c1070..a494896 100644
--- a/backend/src/structs/landmark.py
+++ b/backend/src/structs/landmark.py
@@ -73,8 +73,6 @@ class Landmark(BaseModel) :
         t_to_next_str = f", time_to_next={self.time_to_reach_next}" if self.time_to_reach_next else ""
         is_secondary_str = ", secondary" if self.is_secondary else ""
         type_str = '(' + self.type + ')'
-        if self.type in ["start", "finish", "nature", "shopping"] :
-            type_str += '\t '
 
         return (f'Landmark{type_str}: [{self.name} @{self.location}, '
                 f'score={self.attractiveness}{t_to_next_str}{is_secondary_str}]')
@@ -117,3 +115,28 @@ class Landmark(BaseModel) :
         return (self.uuid == value.uuid or
                 self.osm_id == value.osm_id or
                 (self.name == value.name and self.distance(value) < 0.001))
+
+
+class Toilets(BaseModel) :
+    """
+    Model for toilets. When false/empty the information is either false either not known.
+    """
+    location : tuple
+    wheelchair : Optional[bool] = False
+    changing_table : Optional[bool] = False
+    fee : Optional[bool] = False
+    opening_hours : Optional[str] = ""
+
+
+    def __str__(self) -> str:
+        """
+        String representation of the Toilets object.
+
+        Returns:
+            str: A formatted string with the toilets location.
+        """
+        return f'Toilets @{self.location}'
+    
+    class Config:
+        # This allows us to easily convert the model to and from dictionaries
+        orm_mode = True
\ No newline at end of file
diff --git a/backend/src/tests/test_cache.py b/backend/src/tests/test_cache.py
new file mode 100644
index 0000000..d2cfaca
--- /dev/null
+++ b/backend/src/tests/test_cache.py
@@ -0,0 +1,42 @@
+"""Collection of tests to ensure correct handling of invalid input."""
+
+from fastapi.testclient import TestClient
+import pytest
+
+from .test_utils import load_trip_landmarks
+from ..main import app
+
+
+@pytest.fixture(scope="module")
+def client():
+    """Client used to call the app."""
+    return TestClient(app)
+
+
+def test_cache(client, request):    # pylint: disable=redefined-outer-name
+    """
+    Test n°1 : Custom test in Turckheim to ensure small villages are also supported.
+
+    Args:
+        client:
+        request:
+    """
+    duration_minutes = 15
+    response = client.post(
+        "/trip/new",
+        json={
+            "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
+            "nature": {"type": "nature", "score": 5},
+            "shopping": {"type": "shopping", "score": 5},
+            "max_time_minute": duration_minutes,
+            "detour_tolerance_minute": 0},
+            "start": [48.084588, 7.280405]
+            }
+        )
+    result = response.json()
+    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
+    landmarks_cached = load_trip_landmarks(client, result['first_landmark_uuid'], True)
+
+    # checks :
+    assert response.status_code == 200  # check for successful planning
+    assert landmarks_cached == landmarks
diff --git a/backend/src/tests/test_invalid_input.py b/backend/src/tests/test_invalid_input.py
index 4330337..5184445 100644
--- a/backend/src/tests/test_invalid_input.py
+++ b/backend/src/tests/test_invalid_input.py
@@ -33,19 +33,19 @@ def invalid_client():
         ([91, 181], {"sightseeing": {"type": "nature", "score": 5},
                      "nature": {"type": "nature", "score": 5},
                      "shopping": {"type": "shopping", "score": 5},
-                    }, 423),
+                    }, 422),
         ([-91, 181], {"sightseeing": {"type": "nature", "score": 5},
                      "nature": {"type": "nature", "score": 5},
                      "shopping": {"type": "shopping", "score": 5},
-                    }, 423),
+                    }, 422),
         ([91, -181], {"sightseeing": {"type": "nature", "score": 5},
                      "nature": {"type": "nature", "score": 5},
                      "shopping": {"type": "shopping", "score": 5},
-                    }, 423),
+                    }, 422),
         ([-91, -181], {"sightseeing": {"type": "nature", "score": 5},
                      "nature": {"type": "nature", "score": 5},
                      "shopping": {"type": "shopping", "score": 5},
-                    }, 423),
+                    }, 422),
     ]
 )
 def test_input(invalid_client, start, preferences, status_code):   # pylint: disable=redefined-outer-name
diff --git a/backend/src/tests/test_main.py b/backend/src/tests/test_main.py
index 86753a9..8fe7436 100644
--- a/backend/src/tests/test_main.py
+++ b/backend/src/tests/test_main.py
@@ -78,6 +78,36 @@ def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     assert 136200148 in osm_ids         # check for Cathédrale St. Jean in trip
 
 
+def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
+    """
+    Test n°3 : Custom test in Lyon centre to ensure shopping clusters are found.
+    
+    Args:
+        client:
+        request:
+    """
+    duration_minutes = 600
+    response = client.post(
+        "/trip/new",
+        json={
+            "preferences": {"sightseeing": {"type": "sightseeing", "score": 0},
+                            "nature": {"type": "nature", "score": 0},
+                            "shopping": {"type": "shopping", "score": 5},
+                            "max_time_minute": duration_minutes,
+                            "detour_tolerance_minute": 0},
+            "start": [45.7576485, 4.8330241]
+            }
+        )
+    result = response.json()
+    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
+    # osm_ids = landmarks_to_osmid(landmarks)
+
+    # Add details to report
+    log_trip_details(request, landmarks, result['total_time'], duration_minutes)
+
+    # checks :
+    assert response.status_code == 200  # check for successful planning
+    assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
 
 # def test_new_trip_single_prefs(client):
 #     response = client.post(
diff --git a/backend/src/tests/test_toilets.py b/backend/src/tests/test_toilets.py
new file mode 100644
index 0000000..29d9f06
--- /dev/null
+++ b/backend/src/tests/test_toilets.py
@@ -0,0 +1,102 @@
+"""Collection of tests to ensure correct implementation and track progress. """
+
+from fastapi.testclient import TestClient
+import pytest
+
+from ..structs.landmark import Toilets
+from ..main import app
+
+@pytest.fixture(scope="module")
+def client():
+    """Client used to call the app."""
+    return TestClient(app)
+
+@pytest.mark.parametrize(
+    "location,radius,status_code",
+    [
+        ({}, None, 422),                # Invalid case: no location at all.
+        ([443], None, 422),             # Invalid cases: invalid location.
+        ([443, 433], None, 422),             # Invalid cases: invalid location.
+    ]
+)
+def test_invalid_input(client, location, radius, status_code):    # pylint: disable=redefined-outer-name
+    """
+    Test n°1 : Verify handling of invalid input.
+
+    Args:
+        client:
+        request:
+    """
+    response = client.post(
+        "/toilets/new",
+        params={
+            "location": location,
+            "radius": radius
+        }
+    )
+
+    # checks :
+    assert response.status_code == status_code
+
+
+
+
+@pytest.mark.parametrize(
+    "location,status_code",
+    [
+        ([48.2270, 7.4370], 200), # Orschwiller.
+        ([10.2012, 10.123], 200), # Nigerian desert.
+        ([63.989, -19.677], 200), # Hekla volcano, Iceland
+    ]
+)
+def test_no_toilets(client, location, status_code):    # pylint: disable=redefined-outer-name
+    """
+    Test n°3 : Verify the code finds some toilets in big cities.
+
+    Args:
+        client:
+        request:
+    """
+    response = client.post(
+        "/toilets/new",
+        params={
+            "location": location
+        }
+    )
+    toilets_list = [Toilets.model_validate(toilet) for toilet in response.json()]
+
+    # checks :
+    assert response.status_code == 200  # check for successful planning
+    assert isinstance(toilets_list, list)  # check that the return type is a list
+
+
+
+@pytest.mark.parametrize(
+    "location,status_code",
+    [
+        ([45.7576485, 4.8330241], 200), # Lyon, Bellecour.
+        ([-6.913795,  107.60278], 200), # Bandung, train station
+        ([-22.970140, -43.18181], 200), # Rio de Janeiro, Copacabana
+    ]
+)
+def test_toilets(client, location, status_code):    # pylint: disable=redefined-outer-name
+    """
+    Test n°3 : Verify the code finds some toilets in big cities.
+
+    Args:
+        client:
+        request:
+    """
+    response = client.post(
+        "/toilets/new",
+        params={
+            "location": location,
+            "radius" : 600
+        }
+    )
+    toilets_list = [Toilets.model_validate(toilet) for toilet in response.json()]
+
+    # checks :
+    assert response.status_code == 200  # check for successful planning
+    assert isinstance(toilets_list, list)  # check that the return type is a list
+    assert len(toilets_list) > 0
\ No newline at end of file
diff --git a/backend/src/tests/test_utils.py b/backend/src/tests/test_utils.py
index c3cec6a..d5b69ad 100644
--- a/backend/src/tests/test_utils.py
+++ b/backend/src/tests/test_utils.py
@@ -1,11 +1,13 @@
 """Helper methods for testing."""
-from typing import List
+import logging
 from fastapi import HTTPException
+from pydantic import ValidationError
 
 from ..structs.landmark import Landmark
+from ..persistence import client as cache_client
 
 
-def landmarks_to_osmid(landmarks: List[Landmark]) -> List[int] :
+def landmarks_to_osmid(landmarks: list[Landmark]) -> list[int] :
     """
     Convert the list of landmarks into a list containing their osm ids for quick landmark checking.
     
@@ -31,22 +33,68 @@ def fetch_landmark(client, landmark_uuid: str):
     Returns:
         dict: Landmark data fetched from the API.
     """
+    logger = logging.getLogger(__name__)
     response = client.get(f"/landmark/{landmark_uuid}")
 
     if response.status_code != 200:
-        raise HTTPException(status_code=999,
+        raise HTTPException(status_code=500,
                             detail=f"Failed to fetch landmark with UUID {landmark_uuid}: {response.status_code}")
 
-    json_data = response.json()
+    try:
+        json_data = response.json()
+        logger.info(f"API Response: {json_data}")
+    except ValueError as e:
+        logger.error(f"Failed to parse response as JSON: {response.text}")
+        raise HTTPException(status_code=500, detail="Invalid response format from API")
+    
+    # Try validating against the Landmark model here to ensure consistency
+    try:
+        landmark = Landmark(**json_data)
+    except ValidationError as ve:
+        logging.error(f"Validation error: {ve}")
+        raise HTTPException(status_code=500, detail="Invalid data format received from API")
+    
 
     if "detail" in json_data:
-        raise HTTPException(status_code=999, detail=json_data["detail"])
+        raise HTTPException(status_code=500, detail=json_data["detail"])
+
+    return Landmark(**json_data)
 
 
-    return json_data
+def fetch_landmark_cache(landmark_uuid: str):
+    """
+    Fetch landmark data from the cache based on the landmark UUID.
+
+    Args:
+        landmark_uuid (str): The UUID of the landmark.
+
+    Returns:
+        dict: Landmark data fetched from the cache or raises an HTTP exception.
+    """
+    logger = logging.getLogger(__name__)
+
+    # Try to fetch the landmark data from the cache
+    try:
+        landmark = cache_client.get(f"landmark_{landmark_uuid}")
+        if not landmark :
+            logger.warning(f"Cache miss for landmark UUID: {landmark_uuid}")
+            raise HTTPException(status_code=404, detail=f"Landmark with UUID {landmark_uuid} not found in cache.")
+        
+        # Validate that the fetched data is a dictionary
+        if not isinstance(landmark, Landmark):
+            logger.error(f"Invalid cache data format for landmark UUID: {landmark_uuid}. Expected dict, got {type(landmark).__name__}.")
+            raise HTTPException(status_code=500, detail="Invalid cache data format.")
+
+        return landmark
+    
+    except Exception as exc:
+        logger.error(f"Unexpected error occurred while fetching landmark UUID {landmark_uuid}: {exc}")
+        raise HTTPException(status_code=500, detail="An unexpected error occurred while fetching the landmark from the cache") from exc
+    
+    
 
 
-def load_trip_landmarks(client, first_uuid: str) -> List[Landmark]:
+def load_trip_landmarks(client, first_uuid: str, from_cache=None) -> list[Landmark]:
     """
     Load all landmarks for a trip using the response from the API.
 
@@ -60,19 +108,18 @@ def load_trip_landmarks(client, first_uuid: str) -> List[Landmark]:
     next_uuid = first_uuid
 
     while next_uuid is not None:
-        landmark_data = fetch_landmark(client, next_uuid)
-        # # Convert UUIDs to strings explicitly
-        # landmark_data = {
-        #     key: str(value) if isinstance(value, UUID) else value
-        #     for key, value in landmark_data.items()
-        # }
-        landmarks.append(Landmark(**landmark_data)) # Create Landmark objects
-        next_uuid = landmark_data.get('next_uuid')  # Prepare for the next iteration
+        if from_cache :
+            landmark = fetch_landmark_cache(next_uuid)
+        else :
+            landmark = fetch_landmark(client, next_uuid)
+
+        landmarks.append(landmark)
+        next_uuid = landmark.next_uuid  # Prepare for the next iteration
 
     return landmarks
 
 
-def log_trip_details(request, landmarks: List[Landmark], duration: int, target_duration: int) :
+def log_trip_details(request, landmarks: list[Landmark], duration: int, target_duration: int) :
     """
     Allows to show the detailed trip in the html test report.
     
diff --git a/backend/src/utils/cluster_processing.py b/backend/src/utils/cluster_processing.py
new file mode 100644
index 0000000..2114bcb
--- /dev/null
+++ b/backend/src/utils/cluster_processing.py
@@ -0,0 +1,283 @@
+import logging
+from typing import Literal
+
+import numpy as np
+from sklearn.cluster import DBSCAN
+from pydantic import BaseModel
+from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
+from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
+
+from ..structs.landmark import Landmark
+from ..utils.get_time_separation import get_distance
+from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
+
+
+class ShoppingLocation(BaseModel):
+    """"
+    A classe representing an interesting area for shopping.
+    
+    It can represent either a general area or a specifc route with start and end point.
+    The importance represents the number of shops found in this cluster.
+    
+    Attributes:
+        type :       either a 'street' or 'area' (representing a denser field of shops).
+        importance : size of the cluster (number of points).
+        centroid :   center of the cluster.
+        start :      if the type is a street it goes from here...
+        end :        ...to here
+    """
+    type: Literal['street', 'area']
+    importance: int
+    centroid: tuple
+    # start: Optional[list] = None      # for later use if we want to have streets as well
+    # end: Optional[list] = None
+
+
+class ShoppingManager:
+
+    logger = logging.getLogger(__name__)
+
+    # NOTE: all points are in (lat, lon) format
+    valid: bool             # Ensure the manager is valid (ie there are some clusters to be found) 
+    all_points: list
+    cluster_points: list
+    cluster_labels: list
+    shopping_locations: list[ShoppingLocation]
+
+    def __init__(self, bbox: tuple) -> None:
+        """
+        Upon intialization, generate the point cloud used for cluster detection.
+        The points represent bag/clothes shops and general boutiques.
+
+        Args: 
+            bbox: The bounding box coordinates (around:radius, center_lat, center_lon).
+        """
+
+        # Initialize overpass and cache
+        self.overpass = Overpass()
+        CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
+
+        # Initialize the points for cluster detection
+        query = overpassQueryBuilder(
+            bbox = bbox,
+            elementType = ['node'],
+            selector = ['"shop"~"^(bag|boutique|clothes)$"'],
+            includeCenter = True,
+            out = 'skel'
+        )
+
+        try:
+            result = self.overpass.query(query)
+        except Exception as e:
+            self.logger.error(f"Error fetching landmarks: {e}")
+
+        if len(result.elements()) == 0 :
+            self.valid = False
+        
+        else :
+            points = []
+            for elem in result.elements() :
+                points.append(tuple((elem.lat(), elem.lon())))
+
+            self.all_points = np.array(points)
+            self.valid = True            
+
+
+    def generate_shopping_landmarks(self) -> list[Landmark]:
+        """
+        Generate shopping landmarks based on clustered locations.
+
+        This method first generates clusters of locations and then  extracts shopping-related 
+        locations from these clusters. It transforms each shopping location into a `Landmark` object.
+
+        Returns:
+            list[Landmark]: A list of `Landmark` objects representing shopping locations.
+                            Returns an empty list if no clusters are found.
+        """
+
+        self.generate_clusters()
+
+        if len(set(self.cluster_labels)) == 0 :
+            return []       # Return empty list if no clusters were found
+
+        # Then generate the shopping locations
+        self.generate_shopping_locations()
+
+        # Transform the locations in landmarks and return the list
+        shopping_landmarks = []
+        for location in self.shopping_locations :
+            shopping_landmarks.append(self.create_landmark(location))
+
+        return shopping_landmarks
+
+
+
+    def generate_clusters(self) :
+        """
+        Generate clusters of points using DBSCAN.
+
+        This method applies the DBSCAN clustering algorithm with different
+        parameters depending on the size of the city (number of points). 
+        It filters out noise points and keeps only the largest clusters.
+
+        The method updates:
+            - `self.cluster_points`: The points belonging to clusters.
+            - `self.cluster_labels`: The labels for the points in clusters.
+        
+        The method also calls `filter_clusters()` to retain only the largest clusters.
+        """
+
+        # Apply DBSCAN to find clusters. Choose different settings for different cities.
+        if len(self.all_points) > 200 :
+            dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree')  # for large cities
+        else :
+            dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree')  # for small cities
+
+        labels = dbscan.fit_predict(self.all_points)
+
+        # Separate clustered points and noise points
+        self.cluster_points = self.all_points[labels != -1]
+        self.cluster_labels = labels[labels != -1]
+
+        # filter the clusters to keep only the largest ones
+        self.filter_clusters()
+
+
+    def generate_shopping_locations(self) :
+        """
+        Generate shopping locations based on clustered points.
+
+        This method iterates over the different clusters, calculates the centroid 
+        (as the mean of the points within each cluster), and assigns an importance 
+        based on the size of the cluster.
+
+        The generated shopping locations are stored in `self.shopping_locations` 
+        as a list of `ShoppingLocation` objects, each with:
+            - `type`: Set to 'area'.
+            - `centroid`: The calculated centroid of the cluster.
+            - `importance`: The number of points in the cluster.
+        """
+
+        locations = []
+
+        # loop through the different clusters
+        for label in set(self.cluster_labels):
+
+            # Extract points belonging to the current cluster
+            current_cluster = self.cluster_points[self.cluster_labels == label]
+            
+            # Calculate the centroid as the mean of the points
+            centroid = np.mean(current_cluster, axis=0)
+
+            locations.append(ShoppingLocation(
+                type='area',
+                centroid=centroid,
+                importance = len(current_cluster)
+            ))
+
+        self.shopping_locations = locations
+
+
+    def create_landmark(self, shopping_location: ShoppingLocation) -> Landmark:
+        """
+        Create a Landmark object based on the given shopping location.
+
+        This method queries the Overpass API for nearby neighborhoods and shopping malls 
+        within a 1000m radius around the shopping location centroid. It selects the closest 
+        result and creates a landmark with the associated details such as name, type, and OSM ID.
+
+        Parameters:
+            shopping_location (ShoppingLocation): A ShoppingLocation object containing 
+            the centroid and importance of the area.
+
+        Returns:
+            Landmark: A Landmark object containing details such as the name, type, 
+            location, attractiveness, and OSM details.
+        """
+
+        # Define the bounding box for a given radius around the coordinates
+        lat, lon = shopping_location.centroid
+        bbox = ("around:1000", str(lat), str(lon))
+
+        # Query neighborhoods and shopping malls
+        selectors = ['"place"~"^(suburb|neighborhood|neighbourhood|quarter|city_block)$"', '"shop"="mall"']
+
+        min_dist = float('inf')
+        new_name = 'Shopping Area'
+        new_name_en = None
+        osm_id = 0
+        osm_type = 'node'
+
+        for sel in selectors : 
+            query = overpassQueryBuilder(
+                bbox = bbox,
+                elementType = ['node', 'way', 'relation'],
+                selector = sel,
+                includeCenter = True,
+                out = 'center'
+            )
+
+            try:
+                result = self.overpass.query(query)
+            except Exception as e:
+                self.logger.error(f"Error fetching landmarks: {e}")
+                continue
+
+            for elem in result.elements():
+                location = (elem.centerLat(), elem.centerLon())
+
+                if location[0] is None : 
+                    location = (elem.lat(), elem.lon())
+                    if location[0] is None : 
+                        continue
+
+                d = get_distance(shopping_location.centroid, location)
+                if  d < min_dist :
+                    min_dist = d
+                    new_name = elem.tag('name')
+                    osm_type = elem.type()      # Add type: 'way' or 'relation'
+                    osm_id = elem.id()          # Add OSM id 
+
+                    # Add english name if it exists
+                    try :
+                        new_name_en = elem.tag('name:en')
+                    except:
+                        pass 
+        
+        return Landmark(
+            name=new_name,
+            type='shopping',
+            location=shopping_location.centroid,              # TODO: use the fact the we can also recognize streets.
+            attractiveness=shopping_location.importance,
+            n_tags=0,
+            osm_id=osm_id,
+            osm_type=osm_type,
+            name_en=new_name_en
+        )
+
+
+    def filter_clusters(self):
+        """
+        Filter clusters to retain only the 5 largest clusters by point count.
+
+        This method calculates the size of each cluster and filters out all but the 
+        5 largest clusters. It then updates the cluster points and labels to reflect 
+        only those from the top 5 clusters.
+        """
+        label_counts = np.bincount(self.cluster_labels)
+
+        # Step 3: Get the indices (labels) of the 5 largest clusters
+        top_5_labels = np.argsort(label_counts)[-5:]  # Get the largest 5 clusters
+
+        # Step 4: Filter points to keep only the points in the top 5 clusters
+        filtered_cluster_points = []
+        filtered_cluster_labels = []
+
+        for label in top_5_labels:
+            filtered_cluster_points.append(self.cluster_points[self.cluster_labels == label])
+            filtered_cluster_labels.append(np.full((label_counts[label],), label))  # Replicate the label
+
+        # update the cluster points and labels with the filtered data
+        self.cluster_points = np.vstack(filtered_cluster_points)
+        self.cluster_labels = np.concatenate(filtered_cluster_labels)
+
diff --git a/backend/src/utils/get_time_separation.py b/backend/src/utils/get_time_separation.py
index aae848c..c8bd509 100644
--- a/backend/src/utils/get_time_separation.py
+++ b/backend/src/utils/get_time_separation.py
@@ -15,8 +15,8 @@ def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
     Calculate the time in minutes to travel from one location to another.
 
     Args:
-        p1 (Tuple[float, float]): Coordinates of the starting location.
-        p2 (Tuple[float, float]): Coordinates of the destination.
+        p1 (tuple[float, float]): Coordinates of the starting location.
+        p2 (tuple[float, float]): Coordinates of the destination.
 
         Returns:
         int: Time to travel from p1 to p2 in minutes.
@@ -48,3 +48,35 @@ def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
     walk_time = walk_distance / AVERAGE_WALKING_SPEED * 60
 
     return round(walk_time)
+
+
+def get_distance(p1: tuple[float, float], p2: tuple[float, float]) -> int:
+    """
+    Calculate the time in minutes to travel from one location to another.
+
+    Args:
+        p1 (tuple[float, float]): Coordinates of the starting location.
+        p2 (tuple[float, float]): Coordinates of the destination.
+
+        Returns:
+        int: Time to travel from p1 to p2 in minutes.
+    """
+
+
+    if p1 == p2:
+        return 0
+    else:
+        # Compute the distance in km along the surface of the Earth
+        # (assume spherical Earth)
+        # this is the haversine formula, stolen from stackoverflow
+        # in order to not use any external libraries
+        lat1, lon1 = radians(p1[0]), radians(p1[1])
+        lat2, lon2 = radians(p2[0]), radians(p2[1])
+
+        dlon = lon2 - lon1
+        dlat = lat2 - lat1
+
+        a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
+        c = 2 * atan2(sqrt(a), sqrt(1 - a))
+
+        return EARTH_RADIUS_KM * c
\ No newline at end of file
diff --git a/backend/src/utils/landmarks_manager.py b/backend/src/utils/landmarks_manager.py
index e6ccf57..c5e6091 100644
--- a/backend/src/utils/landmarks_manager.py
+++ b/backend/src/utils/landmarks_manager.py
@@ -1,13 +1,11 @@
-import math
-import yaml
-import logging
-
+import math, yaml, logging
 from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
 from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
 
 from ..structs.preferences import Preferences
 from ..structs.landmark import Landmark
 from .take_most_important import take_most_important
+from .cluster_processing import ShoppingManager
 
 from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
 
@@ -79,7 +77,9 @@ class LandmarkManager:
         # use set to avoid duplicates, this requires some __methods__ to be set in Landmark
         all_landmarks = set()
 
-        bbox = self.create_bbox(center_coordinates, reachable_bbox_side)
+        # Create a bbox using the around technique
+        bbox = tuple((f"around:{reachable_bbox_side/2}", str(center_coordinates[0]), str(center_coordinates[1])))
+        
         # list for sightseeing
         if preferences.sightseeing.score != 0:
             score_function = lambda score: score * 10 * preferences.sightseeing.score / 5
@@ -96,10 +96,19 @@ class LandmarkManager:
         if preferences.shopping.score != 0:
             score_function = lambda score: score * 10 * preferences.shopping.score / 5
             current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
+            
             # set time for all shopping activites :
-            for landmark in current_landmarks : landmark.duration = 45
+            for landmark in current_landmarks : landmark.duration = 30
             all_landmarks.update(current_landmarks)
 
+            # special pipeline for shopping malls
+            shopping_manager = ShoppingManager(bbox)
+            if shopping_manager.valid :
+                shopping_clusters = shopping_manager.generate_shopping_landmarks()
+                for landmark in shopping_clusters : landmark.duration = 45
+                all_landmarks.update(shopping_clusters)
+            
+
 
         landmarks_constrained = take_most_important(all_landmarks, self.N_important)
         self.logger.info(f'Generated {len(all_landmarks)} landmarks around {center_coordinates}, and constrained to {len(landmarks_constrained)} most important ones.')
@@ -151,36 +160,24 @@ class LandmarkManager:
             return 0
 
 
-    def create_bbox(self, coordinates: tuple[float, float], reachable_bbox_side: int) -> tuple[float, float, float, float]:
-        """
-        Create a bounding box around the given coordinates.
+    # def create_bbox(self, coordinates: tuple[float, float], reachable_bbox_side: int) -> tuple[float, float, float, float]:
+    #     """
+    #     Create a bounding box around the given coordinates.
 
-        Args:
-            coordinates (tuple[float, float]): The latitude and longitude of the center of the bounding box.
-            reachable_bbox_side (int): The side length of the bounding box in meters.
+    #     Args:
+    #         coordinates (tuple[float, float]): The latitude and longitude of the center of the bounding box.
+    #         reachable_bbox_side (int): The side length of the bounding box in meters.
 
-        Returns:
-            tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
-                                                defining the bounding box.
-        """
-        
-        lat = coordinates[0]
-        lon = coordinates[1]
+    #     Returns:
+    #         tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
+    #                                             defining the bounding box.
+    #     """
 
-        # Half the side length in km (since it's a square bbox)
-        half_side_length_km = reachable_bbox_side / 2 / 1000
+    #     # Half the side length in m (since it's a square bbox)
+    #     half_side_length_m = reachable_bbox_side / 2
 
-        # Convert distance to degrees
-        lat_diff = half_side_length_km / 111  # 1 degree latitude is approximately 111 km
-        lon_diff = half_side_length_km / (111 * math.cos(math.radians(lat)))  # Adjust for longitude based on latitude
+    #     return tuple((f"around:{half_side_length_m}", str(coordinates[0]), str(coordinates[1])))
 
-        # Calculate bbox
-        min_lat = lat - lat_diff
-        max_lat = lat + lat_diff
-        min_lon = lon - lon_diff
-        max_lon = lon + lon_diff
-
-        return min_lat, min_lon, max_lat, max_lon
 
 
     def fetch_landmarks(self, bbox: tuple, amenity_selector: dict, landmarktype: str, score_function: callable) -> list[Landmark]:
@@ -188,7 +185,7 @@ class LandmarkManager:
         Fetches landmarks of a specified type from OpenStreetMap (OSM) within a bounding box centered on given coordinates.
 
         Args:
-            bbox (tuple[float, float, float, float]): The bounding box coordinates (min_lat, min_lon, max_lat, max_lon).
+            bbox (tuple[float, float, float, float]): The bounding box coordinates (around:radius, center_lat, center_lon).
             amenity_selector (dict): The Overpass API query selector for the desired landmark type. 
             landmarktype (str): The type of the landmark (e.g., 'sightseeing', 'nature', 'shopping').
             score_function (callable): The function to compute the score of the landmark based on its attributes.
@@ -212,7 +209,9 @@ class LandmarkManager:
         for sel in dict_to_selector_list(amenity_selector):
             self.logger.debug(f"Current selector: {sel}")
 
-            query_conditions = ['count_tags()>5']
+            # query_conditions = ['count_tags()>5']
+            # if landmarktype == 'shopping' :       # use this later for shopping clusters
+            #     element_types = ['node']
             element_types = ['way', 'relation']
 
             if 'viewpoint' in sel :
@@ -228,7 +227,7 @@ class LandmarkManager:
                 selector = sel,
                 conditions = query_conditions,        # except for nature....
                 includeCenter = True,
-                out = 'body'
+                out = 'center'
                 )
             self.logger.debug(f"Query: {query}")
 
@@ -365,7 +364,6 @@ class LandmarkManager:
         return return_list
 
 
-
 def dict_to_selector_list(d: dict) -> list:
     """
     Convert a dictionary of key-value pairs to a list of Overpass query strings.
diff --git a/backend/src/utils/optimizer.py b/backend/src/utils/optimizer.py
index e20aa65..d7f354e 100644
--- a/backend/src/utils/optimizer.py
+++ b/backend/src/utils/optimizer.py
@@ -44,7 +44,7 @@ class Optimizer:
             resx (list[float]): List of edge weights.
 
         Returns:
-            Tuple[list[int], list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
+            tuple[list[int], list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
         """
         
         for i, elem in enumerate(resx):
@@ -79,7 +79,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
+            tuple[np.ndarray, list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
         """
 
         l1 = [0]*L*L
@@ -107,7 +107,7 @@ class Optimizer:
             resx (list): List of edge weights.
 
         Returns:
-            Tuple[list[int], Optional[list[list[int]]]]: A tuple containing the visit order and a list of any detected circles.
+            tuple[list[int], Optional[list[list[int]]]]: A tuple containing the visit order and a list of any detected circles.
         """
 
         # first round the results to have only 0-1 values
@@ -180,7 +180,7 @@ class Optimizer:
             max_time (int): Maximum time of visit allowed.
 
         Returns:
-            Tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
+            tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
         """
         
         # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
@@ -212,7 +212,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         ones = [1]*L
@@ -239,7 +239,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         upper_ind = np.triu_indices(L,0,L)
@@ -270,7 +270,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[list[np.ndarray], list[int]]: Equality constraint coefficients and the right-hand side of the equality constraints.
+            tuple[list[np.ndarray], list[int]]: Equality constraint coefficients and the right-hand side of the equality constraints.
         """
 
         l = [0]*L*L
@@ -293,7 +293,7 @@ class Optimizer:
             landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_do'.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         L = len(landmarks)
@@ -319,7 +319,7 @@ class Optimizer:
             landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_avoid'.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         L = len(landmarks)
@@ -346,7 +346,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         l_start = [1]*L + [0]*L*(L-1)   # sets departures only for start (horizontal ones)
@@ -374,7 +374,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         A = [0]*L*L
diff --git a/backend/src/utils/refiner.py b/backend/src/utils/refiner.py
index 28227a6..1f9c755 100644
--- a/backend/src/utils/refiner.py
+++ b/backend/src/utils/refiner.py
@@ -2,7 +2,6 @@ import yaml, logging
 
 from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
 from math import pi
-from typing import List
 
 from ..structs.landmark import Landmark
 from . import take_most_important, get_time_separation
@@ -135,7 +134,7 @@ class Refiner :
     
         return tour
     
-    def integrate_landmarks(self, sub_list: List[Landmark], main_list: List[Landmark]) :
+    def integrate_landmarks(self, sub_list: list[Landmark], main_list: list[Landmark]) :
         """
         Inserts 'sub_list' of Landmarks inside the 'main_list' by leaving the ends untouched.
         
diff --git a/backend/src/utils/toilets_manager.py b/backend/src/utils/toilets_manager.py
new file mode 100644
index 0000000..210d00b
--- /dev/null
+++ b/backend/src/utils/toilets_manager.py
@@ -0,0 +1,78 @@
+import logging, yaml
+from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
+from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
+
+from ..structs.landmark import Toilets
+from ..constants import LANDMARK_PARAMETERS_PATH, OSM_CACHE_DIR
+
+
+# silence the overpass logger
+logging.getLogger('OSMPythonTools').setLevel(level=logging.CRITICAL)
+
+class ToiletsManager:
+
+    logger = logging.getLogger(__name__)
+
+    location: tuple[float, float]
+    radius: int    # radius in meters
+
+
+    def __init__(self, location: tuple[float, float], radius : int) -> None:
+
+        self.radius = radius
+        self.location = location
+        self.overpass = Overpass()
+        CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
+
+
+    def generate_toilet_list(self) -> list[Toilets] :
+
+        
+        # Create a bbox using the around technique
+        bbox = tuple((f"around:{self.radius}", str(self.location[0]), str(self.location[1])))
+        toilets_list = []
+
+        query = overpassQueryBuilder(
+                bbox = bbox,
+                elementType = ['node', 'way', 'relation'],
+                # selector can in principle be a list already,
+                # but it generates the intersection of the queries
+                # we want the union
+                selector = ['"amenity"="toilets"'],
+                includeCenter = True,
+                out = 'center'
+                )
+        self.logger.debug(f"Query: {query}")
+
+        try:
+            result = self.overpass.query(query)
+        except Exception as e:
+            self.logger.error(f"Error fetching landmarks: {e}")
+            return None
+
+        for elem in result.elements():
+            location = (elem.centerLat(), elem.centerLon())
+
+            # handle unprecise and no-name locations
+            if location[0] is None:
+                    location = (elem.lat(), elem.lon())
+            else : 
+                continue
+            
+            toilets = Toilets(location=location)
+            
+            if 'wheelchair' in elem.tags().keys() and elem.tag('wheelchair') == 'yes':
+                toilets.wheelchair = True
+
+            if 'changing_table' in elem.tags().keys() and elem.tag('changing_table') == 'yes':
+                toilets.changing_table = True
+
+            if 'fee' in elem.tags().keys() and elem.tag('fee') == 'yes':
+                toilets.fee = True
+
+            if 'opening_hours' in elem.tags().keys() :
+                toilets.opening_hours = elem.tag('opening_hours')
+
+            toilets_list.append(toilets)
+
+        return toilets_list