diff --git a/backend/report.html b/backend/report.html
index c5a4bf3..78e7bac 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 04-Dec-2024 at 18:33:41 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">11 tests took 00:00:04.</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()">
@@ -393,7 +393,7 @@ 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>
             <input checked="true" class="filter" name="filter_checkbox" type="checkbox" data-test-result="skipped" disabled/>
@@ -432,7 +432,7 @@ div.media {
     </table>
   </body>
   <footer>
-    <div id="data-container" data-jsonblob="{&#34;environment&#34;: {&#34;Python&#34;: &#34;3.12.3&#34;, &#34;Platform&#34;: &#34;Linux-6.8.0-49-generic-x86_64-with-glibc2.39&#34;, &#34;Packages&#34;: {&#34;pytest&#34;: &#34;8.3.3&#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_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;14 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;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[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-6.8.0-49-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_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;67 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;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[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;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[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;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-preferences5-422]&#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;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[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;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[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;23 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;130 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 0x71cef536ca10&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;00:00:04&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 0x71cef536ca10&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/tests/test_main.py b/backend/src/tests/test_main.py
index 86753a9..1ca03d4 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/utils/cluster_processing.py b/backend/src/utils/cluster_processing.py
index 568703f..59bf8b8 100644
--- a/backend/src/utils/cluster_processing.py
+++ b/backend/src/utils/cluster_processing.py
@@ -1,165 +1,208 @@
-import logging, yaml
-from typing import Optional, Literal
+import logging
+from typing import Literal
 
 import numpy as np
 from sklearn.cluster import DBSCAN
-from sklearn.decomposition import PCA
 from pydantic import BaseModel
 from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
 from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
 
 from ..structs.landmark import Landmark
-from ..structs.preferences import Preferences
 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):
     type: Literal['street', 'area']
     importance: int
     centroid: tuple
-    start: Optional[list] = None
-    end: Optional[list] = None
-
+    # 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) -> None:
-
-        with OPTIMIZER_PARAMETERS_PATH.open('r') as f:
-                parameters = yaml.safe_load(f)
-                self.walking_speed = parameters['average_walking_speed']
-                self.detour_factor = parameters['detour_factor']
+    def __init__(self, bbox: tuple) -> None:
+        """
+        Upon intialization, generate the list of shops used for cluster points.
+        """
 
+        # 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'
+        )
 
-    def generate_landmarks_list(self, center_coordinates: tuple[float, float], preferences: Preferences) :
+        try:
+            result = self.overpass.query(query)
+        except Exception as e:
+            self.logger.error(f"Error fetching landmarks: {e}")
 
-        max_walk_dist = (preferences.max_time_minute/2)/60*self.walking_speed*1000/self.detour_factor
-        reachable_bbox_side = min(max_walk_dist, self.max_bbox_side)
+        if len(result.elements()) > 0 :
 
-        # use set to avoid duplicates, this requires some __methods__ to be set in Landmark
-        shopping_landmarks = set()
+            points = []
+            for elem in result.elements() :
+                points.append(tuple((elem.lat(), elem.lon())))
 
-        # 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
+            self.all_points = np.array(points)
+            self.valid = True
+
+        else : 
+            self.valid = False
 
 
-def get_clusters(points: list) -> tuple:
-    """
-    Apply DBSCAN to find clusters.
-    """
-    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
+    def generate_shopping_landmarks(self) -> list[Landmark]:
 
-    labels = dbscan.fit_predict(points)
+        # First generate the clusters
+        self.generate_clusters()
 
-    # Separate clustered points and noise points
-    clustered_points = points[labels != -1]
-    clustered_labels = labels[labels != -1]
+        # Then generate the shopping locations
+        self.generate_shopping_locations()
 
-    return clustered_points, clustered_labels
+        # 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 filter_clusters(cluster_points, cluster_labels):
-    """
-    Remove clusters of less importance.
-    """
-    label_counts = np.bincount(cluster_labels)
+    def generate_clusters(self) :
 
-    # 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),
-            )
+        # 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 :
-            loc = ShoppingLocation(
-                type='street',
-                centroid=tuple((centroid[1], centroid[0])),
-                importance = len(cluster_points),
-                start=start_point,
-                end=end_point
+            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) :
+
+        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:
+
+        # 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'
             )
 
-        locations.append(loc)
+            try:
+                result = self.overpass.query(query)
+            except Exception as e:
+                raise Exception("query unsuccessful")
 
-    xmin = min(all_x)
-    xmax = max(all_x)
-    ymin = min(all_y)
-    ymax = max(all_y)
-    corners = (xmin, xmax, ymin, ymax)
+            for elem in result.elements():
 
-    return corners, locations
+                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):
+        """
+        Remove clusters of lesser importance.
+        """
+        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/landmarks_manager.py b/backend/src/utils/landmarks_manager.py
index 23074bf..310692f 100644
--- a/backend/src/utils/landmarks_manager.py
+++ b/backend/src/utils/landmarks_manager.py
@@ -5,6 +5,7 @@ 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
 
@@ -94,10 +95,19 @@ 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 = 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.')
@@ -353,7 +363,6 @@ 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.
