diff --git a/.vscode/launch.json b/.vscode/launch.json
deleted file mode 100644
index 71f1c8d..0000000
--- a/.vscode/launch.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
-    // Use IntelliSense to learn about possible attributes.
-    // Hover to view descriptions of existing attributes.
-    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
-    "version": "0.2.0",
-    "configurations": [
-        {
-            "name": "frontend",
-            "cwd": "frontend",
-            "request": "launch",
-            "type": "dart"
-        },
-        {
-            "name": "frontend (profile mode)",
-            "cwd": "frontend",
-            "request": "launch",
-            "type": "dart",
-            "flutterMode": "profile"
-        },
-        {
-            "name": "frontend (release mode)",
-            "cwd": "frontend",
-            "request": "launch",
-            "type": "dart",
-            "flutterMode": "release"
-        },
-    ]
-}
\ No newline at end of file
diff --git a/backend/.gitignore b/backend/.gitignore
index 2e722c1..446e6eb 100644
--- a/backend/.gitignore
+++ b/backend/.gitignore
@@ -1,5 +1,6 @@
 # osm-cache
 cache/
+apicache/
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
diff --git a/backend/Pipfile b/backend/Pipfile
index 62e84e8..2757979 100644
--- a/backend/Pipfile
+++ b/backend/Pipfile
@@ -10,5 +10,11 @@ fastapi = "*"
 osmpythontools = "*"
 pydantic = "*"
 shapely = "*"
+networkx = "*"
+geopy = "*"
+requests = ">=2.20.1"
+mwparserfromhell = ">=0.5.2"
+packaging = "*"
+pywikibot = "*"
 
 [dev-packages]
diff --git a/backend/Pipfile.lock b/backend/Pipfile.lock
index 2e5aac3..d3b6eef 100644
--- a/backend/Pipfile.lock
+++ b/backend/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "0f88c01cde3be9a6332acec33fa0ccf13b6e122a6df8ee5cfefa52ba1e98034f"
+            "sha256": "b089081defe09c4ffad85fd22c5de336c76b31e7f6f67e5969b731bdaa58e528"
         },
         "pipfile-spec": 6,
         "requires": {},
@@ -40,11 +40,107 @@
         },
         "certifi": {
             "hashes": [
-                "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516",
-                "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"
+                "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b",
+                "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"
             ],
             "markers": "python_version >= '3.6'",
-            "version": "==2024.6.2"
+            "version": "==2024.7.4"
+        },
+        "charset-normalizer": {
+            "hashes": [
+                "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027",
+                "sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087",
+                "sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786",
+                "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8",
+                "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09",
+                "sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185",
+                "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574",
+                "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e",
+                "sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519",
+                "sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898",
+                "sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269",
+                "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3",
+                "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f",
+                "sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6",
+                "sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8",
+                "sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a",
+                "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73",
+                "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc",
+                "sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714",
+                "sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2",
+                "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc",
+                "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce",
+                "sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d",
+                "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e",
+                "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6",
+                "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269",
+                "sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96",
+                "sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d",
+                "sha256:6463effa3186ea09411d50efc7d85360b38d5f09b870c48e4600f63af490e56a",
+                "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4",
+                "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77",
+                "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d",
+                "sha256:68d1f8a9e9e37c1223b656399be5d6b448dea850bed7d0f87a8311f1ff3dabb0",
+                "sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed",
+                "sha256:6b3251890fff30ee142c44144871185dbe13b11bab478a88887a639655be1068",
+                "sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac",
+                "sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25",
+                "sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8",
+                "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab",
+                "sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26",
+                "sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2",
+                "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db",
+                "sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f",
+                "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5",
+                "sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99",
+                "sha256:87d1351268731db79e0f8e745d92493ee2841c974128ef629dc518b937d9194c",
+                "sha256:8bdb58ff7ba23002a4c5808d608e4e6c687175724f54a5dade5fa8c67b604e4d",
+                "sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811",
+                "sha256:8d756e44e94489e49571086ef83b2bb8ce311e730092d2c34ca8f7d925cb20aa",
+                "sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a",
+                "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03",
+                "sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b",
+                "sha256:923c0c831b7cfcb071580d3f46c4baf50f174be571576556269530f4bbd79d04",
+                "sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c",
+                "sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001",
+                "sha256:9f96df6923e21816da7e0ad3fd47dd8f94b2a5ce594e00677c0013018b813458",
+                "sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389",
+                "sha256:a50aebfa173e157099939b17f18600f72f84eed3049e743b68ad15bd69b6bf99",
+                "sha256:a981a536974bbc7a512cf44ed14938cf01030a99e9b3a06dd59578882f06f985",
+                "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537",
+                "sha256:ae5f4161f18c61806f411a13b0310bea87f987c7d2ecdbdaad0e94eb2e404238",
+                "sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f",
+                "sha256:b01b88d45a6fcb69667cd6d2f7a9aeb4bf53760d7fc536bf679ec94fe9f3ff3d",
+                "sha256:b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796",
+                "sha256:b2b0a0c0517616b6869869f8c581d4eb2dd83a4d79e0ebcb7d373ef9956aeb0a",
+                "sha256:b4a23f61ce87adf89be746c8a8974fe1c823c891d8f86eb218bb957c924bb143",
+                "sha256:bd8f7df7d12c2db9fab40bdd87a7c09b1530128315d047a086fa3ae3435cb3a8",
+                "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c",
+                "sha256:c002b4ffc0be611f0d9da932eb0f704fe2602a9a949d1f738e4c34c75b0863d5",
+                "sha256:c083af607d2515612056a31f0a8d9e0fcb5876b7bfc0abad3ecd275bc4ebc2d5",
+                "sha256:c180f51afb394e165eafe4ac2936a14bee3eb10debc9d9e4db8958fe36afe711",
+                "sha256:c235ebd9baae02f1b77bcea61bce332cb4331dc3617d254df3323aa01ab47bd4",
+                "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6",
+                "sha256:d0eccceffcb53201b5bfebb52600a5fb483a20b61da9dbc885f8b103cbe7598c",
+                "sha256:d965bba47ddeec8cd560687584e88cf699fd28f192ceb452d1d7ee807c5597b7",
+                "sha256:db364eca23f876da6f9e16c9da0df51aa4f104a972735574842618b8c6d999d4",
+                "sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b",
+                "sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae",
+                "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12",
+                "sha256:e27ad930a842b4c5eb8ac0016b0a54f5aebbe679340c26101df33424142c143c",
+                "sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae",
+                "sha256:eb00ed941194665c332bf8e078baf037d6c35d7c4f3102ea2d4f16ca94a26dc8",
+                "sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887",
+                "sha256:eb8821e09e916165e160797a6c17edda0679379a4be5c716c260e836e122f54b",
+                "sha256:efcb3f6676480691518c177e3b465bcddf57cea040302f9f4e6e191af91174d4",
+                "sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f",
+                "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5",
+                "sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33",
+                "sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519",
+                "sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561"
+            ],
+            "markers": "python_full_version >= '3.7.0'",
+            "version": "==3.3.2"
         },
         "click": {
             "hashes": [
@@ -130,20 +226,20 @@
         },
         "exceptiongroup": {
             "hashes": [
-                "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad",
-                "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"
+                "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b",
+                "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"
             ],
             "markers": "python_version < '3.11'",
-            "version": "==1.2.1"
+            "version": "==1.2.2"
         },
         "fastapi": {
             "hashes": [
-                "sha256:97ecbf994be0bcbdadedf88c3150252bed7b2087075ac99735403b1b76cc8fc0",
-                "sha256:b9db9dd147c91cb8b769f7183535773d8741dd46f9dc6676cd82eab510228cd7"
+                "sha256:4f51cfa25d72f9fbc3280832e84b32494cf186f50158d364a8765aabf22587bf",
+                "sha256:ddd1ac34cb1f76c2e2d7f8545a4bcb5463bce4834e81abf0b189e0c359ab2413"
             ],
             "index": "pypi",
             "markers": "python_version >= '3.8'",
-            "version": "==0.111.0"
+            "version": "==0.111.1"
         },
         "fastapi-cli": {
             "hashes": [
@@ -155,51 +251,59 @@
         },
         "fonttools": {
             "hashes": [
-                "sha256:099634631b9dd271d4a835d2b2a9e042ccc94ecdf7e2dd9f7f34f7daf333358d",
-                "sha256:0c555e039d268445172b909b1b6bdcba42ada1cf4a60e367d68702e3f87e5f64",
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-                "sha256:ef50ec31649fbc3acf6afd261ed89d09eb909b97cc289d80476166df8438524d",
-                "sha256:fa1f3e34373aa16045484b4d9d352d4c6b5f9f77ac77a178252ccbc851e8b2ee",
-                "sha256:fca66d9ff2ac89b03f5aa17e0b21a97c21f3491c46b583bb131eb32c7bab33af"
+                "sha256:02569e9a810f9d11f4ae82c391ebc6fb5730d95a0657d24d754ed7763fb2d122",
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@@ -209,6 +313,15 @@
             "markers": "python_version >= '3.7'",
             "version": "==3.1.0"
         },
+        "geopy": {
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@@ -624,38 +737,38 @@
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@@ -665,6 +778,48 @@
             "markers": "python_version >= '3.7'",
             "version": "==0.1.2"
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+            "markers": "python_version >= '3.8'",
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@@ -717,58 +872,6 @@
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-            "markers": "python_version >= '3.8'",
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         "osmpythontools": {
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@@ -781,6 +884,7 @@
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+            "index": "pypi",
             "markers": "python_version >= '3.8'",
             "version": "==24.1"
         },
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+                "sha256:e97fdf088d4b31ff4ba35db26d9cc472ac7ef4a2ff2badeabf8d727b3377fc52",
+                "sha256:e9fa4c9bf273ca41f940bceb86922a7667cd5bf90e95dbb157cbb8441008482c",
+                "sha256:eaad4ff2de1c3823fddf82f41121bdf453d922e9a238642b1dedb33c4e4f98ad",
+                "sha256:f1f62b2413c3a0e846c3b838b2ecd6c7a19ec6793b2a522745b0869e37ab5bc1",
+                "sha256:f6d6cff3538391e8486a431569b77921adfcdef14eb18fbf19b7c0a5294d4e6a",
+                "sha256:f9aa05d09ecf4c75157197f27cdc9cfaeb7c5f15021c6373932bf3e124af029f",
+                "sha256:fa2fddcb7107e0d1808086ca306dcade7df60a13a6c347a7acf1ec139aa6789a",
+                "sha256:faa6b09ee09433b87992fb5a2859efd1c264ddc37280d2dd5db502126d0e7f27"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==2.18.4"
+            "version": "==2.20.1"
         },
         "pygments": {
             "hashes": [
@@ -1034,6 +1159,15 @@
             ],
             "version": "==2024.1"
         },
+        "pywikibot": {
+            "hashes": [
+                "sha256:3f4fbc57f1765aa0fa1ccf84125bcfa475cae95b9cc0291867b751f3d4ac8fa2",
+                "sha256:a26d918cf88ef56fdb1421b65b09def200cc28031cdc922d72a4198fbfddd225"
+            ],
+            "index": "pypi",
+            "markers": "python_full_version >= '3.7.0'",
+            "version": "==9.2.1"
+        },
         "pyyaml": {
             "hashes": [
                 "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5",
@@ -1090,6 +1224,15 @@
             ],
             "version": "==6.0.1"
         },
+        "requests": {
+            "hashes": [
+                "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760",
+                "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"
+            ],
+            "index": "pypi",
+            "markers": "python_version >= '3.8'",
+            "version": "==2.32.3"
+        },
         "rich": {
             "hashes": [
                 "sha256:4edbae314f59eb482f54e9e30bf00d33350aaa94f4bfcd4e9e3110e64d0d7222",
@@ -1100,83 +1243,78 @@
         },
         "scipy": {
             "hashes": [
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+                "sha256:6d056a8709ccda6cf36cdd2eac597d13bc03dba38360f418560a93050c76a16e",
+                "sha256:7d3da42fbbbb860211a811782504f38ae7aaec9de8764a9bef6b262de7a2b50f",
+                "sha256:7e911933d54ead4d557c02402710c2396529540b81dd554fc1ba270eb7308484",
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+                "sha256:a01cc03bcdc777c9da3cfdcc74b5a75caffb48a6c39c8450a9a05f82c4250a14",
+                "sha256:a7d46c3e0aea5c064e734c3eac5cf9eb1f8c4ceee756262f2c7327c4c2691c86",
+                "sha256:ad36af9626d27a4326c8e884917b7ec321d8a1841cd6dacc67d2a9e90c2f0359",
+                "sha256:b5923f48cb840380f9854339176ef21763118a7300a88203ccd0bdd26e58527b",
+                "sha256:bbc0471b5f22c11c389075d091d3885693fd3f5e9a54ce051b46308bc787e5d4",
+                "sha256:bff2438ea1330e06e53c424893ec0072640dac00f29c6a43a575cbae4c99b2b9",
+                "sha256:c40003d880f39c11c1edbae8144e3813904b10514cd3d3d00c277ae996488cdb",
+                "sha256:d91db2c41dd6c20646af280355d41dfa1ec7eead235642178bd57635a3f82209",
+                "sha256:f0a50da861a7ec4573b7c716b2ebdcdf142b66b756a0d392c236ae568b3a93fb"
             ],
             "index": "pypi",
-            "markers": "python_version >= '3.9'",
-            "version": "==1.13.1"
+            "markers": "python_version >= '3.10'",
+            "version": "==1.14.0"
         },
         "shapely": {
             "hashes": [
-                "sha256:011b77153906030b795791f2fdfa2d68f1a8d7e40bce78b029782ade3afe4f2f",
-                "sha256:03152442d311a5e85ac73b39680dd64a9892fa42bb08fd83b3bab4fe6999bfa0",
-                "sha256:05ffd6491e9e8958b742b0e2e7c346635033d0a5f1a0ea083547fcc854e5d5cf",
-                "sha256:0776c92d584f72f1e584d2e43cfc5542c2f3dd19d53f70df0900fda643f4bae6",
-                "sha256:263bcf0c24d7a57c80991e64ab57cba7a3906e31d2e21b455f493d4aab534aaa",
-                "sha256:2fbdc1140a7d08faa748256438291394967aa54b40009f54e8d9825e75ef6113",
-                "sha256:30982f79f21bb0ff7d7d4a4e531e3fcaa39b778584c2ce81a147f95be1cd58c9",
-                "sha256:31c19a668b5a1eadab82ff070b5a260478ac6ddad3a5b62295095174a8d26398",
-                "sha256:3f9103abd1678cb1b5f7e8e1af565a652e036844166c91ec031eeb25c5ca8af0",
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-                "sha256:4310b5494271e18580d61022c0857eb85d30510d88606fa3b8314790df7f367d",
-                "sha256:464157509ce4efa5ff285c646a38b49f8c5ef8d4b340f722685b09bb033c5ccf",
-                "sha256:485246fcdb93336105c29a5cfbff8a226949db37b7473c89caa26c9bae52a242",
-                "sha256:489c19152ec1f0e5c5e525356bcbf7e532f311bff630c9b6bc2db6f04da6a8b9",
-                "sha256:4f2ab0faf8188b9f99e6a273b24b97662194160cc8ca17cf9d1fb6f18d7fb93f",
-                "sha256:55a38dcd1cee2f298d8c2ebc60fc7d39f3b4535684a1e9e2f39a80ae88b0cea7",
-                "sha256:58b0ecc505bbe49a99551eea3f2e8a9b3b24b3edd2a4de1ac0dc17bc75c9ec07",
-                "sha256:5af4cd0d8cf2912bd95f33586600cac9c4b7c5053a036422b97cfe4728d2eb53",
-                "sha256:5bbd974193e2cc274312da16b189b38f5f128410f3377721cadb76b1e8ca5328",
-                "sha256:5c4849916f71dc44e19ed370421518c0d86cf73b26e8656192fcfcda08218fbd",
-                "sha256:5dc736127fac70009b8d309a0eeb74f3e08979e530cf7017f2f507ef62e6cfb8",
-                "sha256:63f3a80daf4f867bd80f5c97fbe03314348ac1b3b70fb1c0ad255a69e3749879",
-                "sha256:674d7baf0015a6037d5758496d550fc1946f34bfc89c1bf247cabdc415d7747e",
-                "sha256:6cd4ccecc5ea5abd06deeaab52fcdba372f649728050c6143cc405ee0c166679",
-                "sha256:790a168a808bd00ee42786b8ba883307c0e3684ebb292e0e20009588c426da47",
-                "sha256:7d56ce3e2a6a556b59a288771cf9d091470116867e578bebced8bfc4147fbfd7",
-                "sha256:841f93a0e31e4c64d62ea570d81c35de0f6cea224568b2430d832967536308e6",
-                "sha256:8de4578e838a9409b5b134a18ee820730e507b2d21700c14b71a2b0757396acc",
-                "sha256:92a41d936f7d6743f343be265ace93b7c57f5b231e21b9605716f5a47c2879e7",
-                "sha256:9831816a5d34d5170aa9ed32a64982c3d6f4332e7ecfe62dc97767e163cb0b17",
-                "sha256:994c244e004bc3cfbea96257b883c90a86e8cbd76e069718eb4c6b222a56f78b",
-                "sha256:9dab4c98acfb5fb85f5a20548b5c0abe9b163ad3525ee28822ffecb5c40e724c",
-                "sha256:b79bbd648664aa6f44ef018474ff958b6b296fed5c2d42db60078de3cffbc8aa",
-                "sha256:c3e700abf4a37b7b8b90532fa6ed5c38a9bfc777098bc9fbae5ec8e618ac8f30",
-                "sha256:c52ed79f683f721b69a10fb9e3d940a468203f5054927215586c5d49a072de8d",
-                "sha256:c75c98380b1ede1cae9a252c6dc247e6279403fae38c77060a5e6186c95073ac",
-                "sha256:d2b4431f522b277c79c34b65da128029a9955e4481462cbf7ebec23aab61fc58",
-                "sha256:ddf4a9bfaac643e62702ed662afc36f6abed2a88a21270e891038f9a19bc08fc",
-                "sha256:de0205cb21ad5ddaef607cda9a3191eadd1e7a62a756ea3a356369675230ac35",
-                "sha256:ec555c9d0db12d7fd777ba3f8b75044c73e576c720a851667432fabb7057da6c",
-                "sha256:fb5cdcbbe3080181498931b52a91a21a781a35dcb859da741c0345c6402bf00c"
+                "sha256:03bd7b5fa5deb44795cc0a503999d10ae9d8a22df54ae8d4a4cd2e8a93466195",
+                "sha256:06efe39beafde3a18a21dde169d32f315c57da962826a6d7d22630025200c5e6",
+                "sha256:0f8e71bb9a46814019f6644c4e2560a09d44b80100e46e371578f35eaaa9da1c",
+                "sha256:1b65365cfbf657604e50d15161ffcc68de5cdb22a601bbf7823540ab4918a98d",
+                "sha256:1e5cb5ee72f1bc7ace737c9ecd30dc174a5295fae412972d3879bac2e82c8fae",
+                "sha256:21f64e647a025b61b19585d2247137b3a38a35314ea68c66aaf507a1c03ef6fe",
+                "sha256:2e119444bc27ca33e786772b81760f2028d930ac55dafe9bc50ef538b794a8e1",
+                "sha256:2ff9521991ed9e201c2e923da014e766c1aa04771bc93e6fe97c27dcf0d40ace",
+                "sha256:30e8737983c9d954cd17feb49eb169f02f1da49e24e5171122cf2c2b62d65c95",
+                "sha256:35110e80070d664781ec7955c7de557456b25727a0257b354830abb759bf8311",
+                "sha256:3ac7dc1350700c139c956b03d9c3df49a5b34aaf91d024d1510a09717ea39199",
+                "sha256:401cb794c5067598f50518e5a997e270cd7642c4992645479b915c503866abed",
+                "sha256:4461509afdb15051e73ab178fae79974387f39c47ab635a7330d7fee02c68a3f",
+                "sha256:45211276900c4790d6bfc6105cbf1030742da67594ea4161a9ce6812a6721e68",
+                "sha256:49b299b91557b04acb75e9732645428470825061f871a2edc36b9417d66c1fc5",
+                "sha256:4c83a36f12ec8dee2066946d98d4d841ab6512a6ed7eb742e026a64854019b5f",
+                "sha256:5bbfb048a74cf273db9091ff3155d373020852805a37dfc846ab71dde4be93ec",
+                "sha256:6c6b78c0007a34ce7144f98b7418800e0a6a5d9a762f2244b00ea560525290c9",
+                "sha256:7545a39c55cad1562be302d74c74586f79e07b592df8ada56b79a209731c0219",
+                "sha256:798090b426142df2c5258779c1d8d5734ec6942f778dab6c6c30cfe7f3bf64ff",
+                "sha256:7e8cf5c252fac1ea51b3162be2ec3faddedc82c256a1160fc0e8ddbec81b06d2",
+                "sha256:7fed9dbfbcfec2682d9a047b9699db8dcc890dfca857ecba872c42185fc9e64e",
+                "sha256:8203a8b2d44dcb366becbc8c3d553670320e4acf0616c39e218c9561dd738d92",
+                "sha256:89d34787c44f77a7d37d55ae821f3a784fa33592b9d217a45053a93ade899375",
+                "sha256:89e640c2cd37378480caf2eeda9a51be64201f01f786d127e78eaeff091ec897",
+                "sha256:8af6f7260f809c0862741ad08b1b89cb60c130ae30efab62320bbf4ee9cc71fa",
+                "sha256:93be600cbe2fbaa86c8eb70656369f2f7104cd231f0d6585c7d0aa555d6878b8",
+                "sha256:9a4492a2b2ccbeaebf181e7310d2dfff4fdd505aef59d6cb0f217607cb042fb3",
+                "sha256:b5870633f8e684bf6d1ae4df527ddcb6f3895f7b12bced5c13266ac04f47d231",
+                "sha256:b714a840402cde66fd7b663bb08cacb7211fa4412ea2a209688f671e0d0631fd",
+                "sha256:bff2366bc786bfa6cb353d6b47d0443c570c32776612e527ee47b6df63fcfe32",
+                "sha256:d5251c28a29012e92de01d2e84f11637eb1d48184ee8f22e2df6c8c578d26760",
+                "sha256:e91ee179af539100eb520281ba5394919067c6b51824e6ab132ad4b3b3e76dd0",
+                "sha256:f5456dd522800306ba3faef77c5ba847ec30a0bd73ab087a25e0acdd4db2514f",
+                "sha256:ff7731fea5face9ec08a861ed351734a79475631b7540ceb0b66fb9732a5f529",
+                "sha256:ff9e520af0c5a578e174bca3c18713cd47a6c6a15b6cf1f50ac17dc8bb8db6a2"
             ],
             "index": "pypi",
             "markers": "python_version >= '3.7'",
-            "version": "==2.0.4"
+            "version": "==2.0.5"
         },
         "shellingham": {
             "hashes": [
@@ -1326,6 +1464,14 @@
             "markers": "python_version >= '3.8'",
             "version": "==5.10.0"
         },
+        "urllib3": {
+            "hashes": [
+                "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472",
+                "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==2.2.2"
+        },
         "uvicorn": {
             "extras": [
                 "standard"
diff --git a/backend/src/amenities/nature.am b/backend/src/amenities/nature.am
index dcc4061..5102c02 100644
--- a/backend/src/amenities/nature.am
+++ b/backend/src/amenities/nature.am
@@ -8,4 +8,5 @@ geological
 'tourism'='alpine_hut'
 'tourism'='viewpoint'
 'tourism'='zoo'
+'tourism'='artwork'
 'waterway'='waterfall'
\ No newline at end of file
diff --git a/backend/src/amenities/sightseeing.am b/backend/src/amenities/sightseeing.am
index 33bcb1a..053f9c9 100644
--- a/backend/src/amenities/sightseeing.am
+++ b/backend/src/amenities/sightseeing.am
@@ -1,8 +1,11 @@
 'tourism'='museum'
 'tourism'='attraction'
 'tourism'='gallery'
+'tourism'='artwork'
 historic
 'amenity'='planetarium'
 'amenity'='place_of_worship'
 'amenity'='fountain'
-'water'='reflecting_pool'
\ No newline at end of file
+'water'='reflecting_pool'
+# 'tourism'='attraction' might be a bit too broad
+# historic as well
\ No newline at end of file
diff --git a/backend/src/landmarks_manager.py b/backend/src/landmarks_manager.py
index 618e169..9d4ce9b 100644
--- a/backend/src/landmarks_manager.py
+++ b/backend/src/landmarks_manager.py
@@ -3,6 +3,10 @@ import json, os
 
 from typing import List, Tuple, Optional
 from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
+from pywikibot import ItemPage, Site
+from pywikibot import config
+config.put_throttle = 0
+config.maxlag = 0
 
 from structs.landmarks import Landmark, LandmarkType
 from structs.preferences import Preferences, Preference
@@ -43,31 +47,6 @@ def generate_landmarks(preferences: Preferences, coordinates: Tuple[float, float
     return L, take_most_important(L)
 
 
-"""def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None) -> Tuple[List[Landmark], List[Landmark]] :
-
-    l_sights, l_nature, l_shop = get_amenities()
-    L = []
-
-    # List for sightseeing
-    if preferences.sightseeing.score != 0 :
-        L1 = get_landmarks(l_sights, SIGHTSEEING, city_country=city_country, coordinates=coordinates)
-        correct_score(L1, preferences.sightseeing)
-        L += L1
-    
-    # List for nature
-    if preferences.nature.score != 0 :
-        L2 = get_landmarks(l_nature, NATURE, city_country=city_country, coordinates=coordinates)
-        correct_score(L2, preferences.nature)
-        L += L2
-    
-    # List for shopping
-    if preferences.shopping.score != 0 :
-        L3 = get_landmarks(l_shop, SHOPPING, city_country=city_country, coordinates=coordinates)
-        correct_score(L3, preferences.shopping)
-        L += L3
-
-    return remove_duplicates(L), take_most_important(L)
-"""
 # Helper function to gather the amenities list
 def get_amenities() -> List[List[str]] :
     
@@ -87,7 +66,8 @@ def get_list(path: str) -> List[str] :
 
         amenities = []
         for line in content :
-            amenities.append(line.strip('\n'))
+            if not line.startswith('#') :
+                amenities.append(line.strip('\n'))
 
     return amenities
 
@@ -173,7 +153,7 @@ def correct_score(L: List[Landmark], preference: Preference) :
         raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}")
 
     for elem in L :
-        elem.attractiveness = int(elem.attractiveness*preference.score/500)     # arbitrary computation
+        elem.attractiveness = int(elem.attractiveness*preference.score/5)     # arbitrary computation
 
 
 # Function to count elements within a certain radius of a location
@@ -192,20 +172,24 @@ def count_elements_within_radius(coordinates: Tuple[float, float], radius: int)
     try : 
         overpass = Overpass()
         radius_result = overpass.query(radius_query)
-        return radius_result.countElements()
+        N_elem = radius_result.countWays() + radius_result.countRelations()
+        #print(f"There are {N_elem} ways/relations within 50m")
+        if N_elem is None :
+            return 0
+        return N_elem
     
     except :
-        return None
+        return 0
 
 
-# Creates a bounding box around given coordinates
+# Creates a bounding box around given coordinates, side_length in meters
 def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[float, float, float, float]:
-
+    
     lat = coordinates[0]
     lon = coordinates[1]
 
     # Half the side length in km (since it's a square bbox)
-    half_side_length_km = side_length / 2.0
+    half_side_length_km = side_length / 2 / 1000
 
     # Convert distance to degrees
     lat_diff = half_side_length_km / 111  # 1 degree latitude is approximately 111 km
@@ -260,106 +244,65 @@ def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: T
             # skip if part of another building
             if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
                 continue
-            
+        
             else :
                 osm_type = elem.type()              # Add type : 'way' or 'relation'
                 osm_id = elem.id()                  # Add OSM id 
-                elem_type = landmarktype            # Add the landmark type as 'sightseeing
+                elem_type = landmarktype            # Add the landmark type as 'sightseeing, 
                 n_tags = len(elem.tags().keys())    # Add number of tags
-                
+
+                # remove specific tags
+                skip = False
+                for tag in elem.tags().keys() :
+                    if "pay" in tag :
+                        n_tags -= 1             # discard payment options for tags
+
+                    if "disused" in tag :
+                        skip = True             # skip disused amenities
+                        break
+
+                    if "wikipedia" in tag :
+                        n_tags += 3             # wikipedia entries count more
+
+                    if tag == "wikidata" :
+                        Q = elem.tag('wikidata')
+                        site = Site("wikidata", "wikidata")
+                        item = ItemPage(site, Q)
+                        item.get()
+                        n_languages = len(item.labels)
+                        n_tags += n_languages/10
+
+                    if elem_type != LandmarkType(landmark_type="nature") :
+                        if "leisure" in tag and elem.tag('leisure') == "park":
+                            elem_type = LandmarkType(landmark_type="nature")
+
+                    if amenity not in ["'shop'='department_store'", "'shop'='mall'"] :
+                        if "shop" in tag :
+                            skip = True
+                            break
+
+                        if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
+                            skip = True
+                            break
+
+                if skip:
+                    continue
+
                 # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
                 if amenity == "'amenity'='place_of_worship'" :
-                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)  
+                    #score = int((count_elements_within_radius(location, radius) + (n_tags*tag_coeff) )*church_coeff)  
+                    score = int((count_elements_within_radius(location, radius) + ((n_tags**1.2)*tag_coeff) )*church_coeff)  
                 elif amenity == "'leisure'='park'" :
-                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)  
+                    score = int((count_elements_within_radius(location, radius) + ((n_tags**1.2)*tag_coeff) )*park_coeff)  
                 else :
-                    score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
+                    score = int(count_elements_within_radius(location, radius) + ((n_tags**1.2)*tag_coeff))
 
                 if score is not None :
                     # Generate the landmark and append it to the list
-                    landmark = Landmark(name=name, type=elem_type, location=location, osm_type=osm_type, osm_id=osm_id, attractiveness=score, must_do=False, n_tags=n_tags)
+                    #print(f"There are {n_tags} tags on this Landmark. Total score : {score}\n")
+                    landmark = Landmark(name=name, type=elem_type, location=location, osm_type=osm_type, osm_id=osm_id, attractiveness=score, must_do=False, n_tags=int(n_tags))
                     L.append(landmark)
 
     return L
 
 
-
-"""def get_landmarks(list_amenity: list, landmarktype: LandmarkType, city_country: str = None, coordinates: Tuple[float, float] = None) -> List[Landmark] :
-    
-    if city_country is None and coordinates is None :
-        raise ValueError("Either one of 'city_country' and 'coordinates' arguments must be specified")
-
-    if city_country is not None and coordinates is not None :
-        raise ValueError("Cannot specify both 'city_country' and 'coordinates' at the same time, please choose either one")
-
-    # Read the parameters from the file
-    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
-        parameters = json.loads(f.read())
-        tag_coeff = parameters['tag coeff']
-        park_coeff = parameters['park coeff']
-        church_coeff = parameters['church coeff']
-        radius = parameters['radius close to']
-        bbox_side = parameters['city bbox side']
-    
-    # If city_country is specified :
-    if city_country is not None :
-        nominatim = Nominatim()
-        areaId = nominatim.query(city_country).areaId()
-        bbox = None
-
-    # If coordinates are specified :
-    elif coordinates is not None :
-        bbox = create_bbox(coordinates, bbox_side)
-        areaId = None
-
-    else :
-        raise ValueError("Argument number is not corresponding.")
-    
-    # Initialize some variables
-    N = 0
-    L = []
-    overpass = Overpass()
-
-    for amenity in list_amenity :
-        query = overpassQueryBuilder(area=areaId, bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
-        result = overpass.query(query)
-        N += result.countElements()
-
-        for elem in result.elements():
-
-            name = elem.tag('name')                             # Add name
-            location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
-
-            # skip if unprecise location
-            if name is None or location[0] is None:
-                continue
-
-            # skip if unused
-            if 'disused:leisure' in elem.tags().keys():
-                continue
-            
-            # skip if part of another building
-            if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
-                continue
-            
-            else :
-                osm_type = elem.type()              # Add type : 'way' or 'relation'
-                osm_id = elem.id()                  # Add OSM id 
-                elem_type = landmarktype            # Add the landmark type as 'sightseeing
-                n_tags = len(elem.tags().keys())    # Add number of tags
-                
-                # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
-                if amenity == "'amenity'='place_of_worship'" :
-                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)  
-                elif amenity == "'leisure'='park'" :
-                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)  
-                else :
-                    score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
-
-                if score is not None :
-                    # Generate the landmark and append it to the list
-                    landmark = Landmark(name=name, type=elem_type, location=location, osm_type=osm_type, osm_id=osm_id, attractiveness=score, must_do=False, n_tags=n_tags)
-                    L.append(landmark)
-
-    return L
-"""
\ No newline at end of file
diff --git a/backend/src/main_example.py b/backend/src/main_example.py
deleted file mode 100644
index 1a49305..0000000
--- a/backend/src/main_example.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import fastapi
-from dataclasses import dataclass
-
-
-@dataclass
-class Destination:
-    name: str
-    location: tuple
-    attractiveness: int
-
-
-
-d = Destination()
-
-
-
-def get_route() -> list[Destination]:
-    return {"route": "Hello World"}
-
-endpoint = ("/get_route", get_route)
-end
-if __name__ == "__main__":
-    fastapi.run()
diff --git a/backend/src/optimizer.py b/backend/src/optimizer.py
index b346a8a..3d2ac29 100644
--- a/backend/src/optimizer.py
+++ b/backend/src/optimizer.py
@@ -344,6 +344,8 @@ def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
         elem.next_uuid = next.uuid
         d = get_distance(elem.location, next.location, detour_factor, speed)[1]
         elem.time_to_reach_next = d
+        if elem.name not in ['start', 'finish'] :
+            elem.must_do = True
         L.append(elem)
         j += 1
         total_dist += d
diff --git a/backend/src/optimizer_v4.py b/backend/src/optimizer_v4.py
new file mode 100644
index 0000000..0eecb97
--- /dev/null
+++ b/backend/src/optimizer_v4.py
@@ -0,0 +1,446 @@
+import numpy as np
+import json, os
+
+from typing import List, Tuple
+from scipy.optimize import linprog
+from collections import defaultdict, deque
+from geopy.distance import geodesic
+
+from structs.landmarks import Landmark
+
+    
+# Function to print the result
+def print_res(L: List[Landmark]):
+
+    print('The following order is suggested : ')
+
+    dist = 0
+    for elem in L : 
+        if elem.time_to_reach_next is not None :
+            print('- ' + elem.name + ', time to reach next = ' + str(elem.time_to_reach_next))
+            dist += elem.time_to_reach_next
+        else : 
+            print('- ' + elem.name)
+
+    print("\nMinutes walked : " + str(dist))
+    print(f"Visited {len(L)-2} landmarks")
+
+
+# Prevent the use of a particular solution
+def prevent_config(resx):
+    
+    for i, elem in enumerate(resx):
+        resx[i] = round(elem)
+    
+    N = len(resx)               # Number of edges
+    L = int(np.sqrt(N))         # Number of landmarks
+
+    nonzeroind = np.nonzero(resx)[0]                    # the return is a little funky so I use the [0]
+    nonzero_tup = np.unravel_index(nonzeroind, (L,L))
+
+    ind_a = nonzero_tup[0].tolist()
+    vertices_visited = ind_a
+    vertices_visited.remove(0)
+
+    ones = [1]*L
+    h = [0]*N
+    for i in range(L) :
+        if i in vertices_visited :
+            h[i*L:i*L+L] = ones
+
+    return h, [len(vertices_visited)-1]
+
+
+# Prevents the creation of the same circle (both directions)
+def prevent_circle(circle_vertices: list, L: int) :
+
+    l1 = [0]*L*L
+    l2 = [0]*L*L
+    for i, node in enumerate(circle_vertices[:-1]) :
+        next = circle_vertices[i+1]
+
+        l1[node*L + next] = 1
+        l2[next*L + node] = 1
+
+    s = circle_vertices[0]
+    g = circle_vertices[-1]
+
+    l1[g*L + s] = 1
+    l2[s*L + g] = 1
+
+    return np.vstack((l1, l2)), [0, 0]
+
+
+# Returns the order of visit as well as any circles if there are some
+def is_connected(resx) :
+
+    # first round the results to have only 0-1 values
+    for i, elem in enumerate(resx):
+        resx[i] = round(elem)
+    
+    N = len(resx)               # length of res
+    L = int(np.sqrt(N))         # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
+    n_edges = resx.sum()        # number of edges
+
+    nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
+
+    nonzero_tup = np.unravel_index(nonzeroind, (L,L))
+
+    ind_a = nonzero_tup[0].tolist()
+    ind_b = nonzero_tup[1].tolist()
+
+    # Step 1: Create a graph representation
+    graph = defaultdict(list)
+    for a, b in zip(ind_a, ind_b):
+        graph[a].append(b)
+
+    # Step 2: Function to perform BFS/DFS to extract journeys
+    def get_journey(start):
+        journey_nodes = []
+        visited = set()
+        stack = deque([start])
+
+        while stack:
+            node = stack.pop()
+            if node not in visited:
+                visited.add(node)
+                journey_nodes.append(node)
+                for neighbor in graph[node]:
+                    if neighbor not in visited:
+                        stack.append(neighbor)
+
+        return journey_nodes
+
+    # Step 3: Extract all journeys
+    all_journeys_nodes = []
+    visited_nodes = set()
+
+    for node in ind_a:
+        if node not in visited_nodes:
+            journey_nodes = get_journey(node)
+            all_journeys_nodes.append(journey_nodes)
+            visited_nodes.update(journey_nodes)
+
+    for l in all_journeys_nodes :
+        if 0 in l :
+            order = l
+            all_journeys_nodes.remove(l)
+            break
+
+    if len(all_journeys_nodes) == 0 :
+        return order, None
+
+    return order, all_journeys_nodes
+
+
+# Function that returns the time in minutes from one location to another
+def get_time(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, speed: float) :
+    
+    # Compute the straight-line distance in km
+    if p1 == p2 :
+        return 0
+    else: 
+        dist = geodesic(p1, p2).kilometers
+
+    # Consider the detour factor for average cityto deterline walking distance (in km)
+    walk_dist = dist*detour
+
+    # Time to walk this distance (in minutes)
+    walk_time = walk_dist/speed*60
+
+    return round(walk_time)
+
+
+# Initialize A and c. Compute the distances from all landmarks to each other and store attractiveness
+# We want to maximize the sightseeing :  max(c) st. A*x < b   and   A_eq*x = b_eq
+def init_ub_dist(landmarks: List[Landmark], max_steps: int):
+    
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        detour = parameters['detour factor']
+        speed = parameters['average walking speed']
+    
+    # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
+    c = []
+    # Coefficients of inequality constraints (left-hand side)
+    A_ub = []
+
+    for spot1 in landmarks :
+        dist_table = [0]*len(landmarks)
+        c.append(-spot1.attractiveness)
+        for j, spot2 in enumerate(landmarks) :
+            t = get_time(spot1.location, spot2.location, detour, speed)
+            dist_table[j] = t
+        closest = sorted(dist_table)[:22]
+        for i, dist in enumerate(dist_table) :
+            if dist not in closest :
+                dist_table[i] = 32700
+        A_ub += dist_table
+    c = c*len(landmarks)
+
+    return c, A_ub, [max_steps]
+
+
+# Constraint to respect only one travel per landmark. Also caps the total number of visited landmarks
+def respect_number(L: int, max_landmarks: int):
+
+    ones = [1]*L
+    zeros = [0]*L
+    A = ones + zeros*(L-1)
+    b = [1]
+    for i in range(L-1) :
+        h_new = zeros*i + ones + zeros*(L-1-i)
+        A = np.vstack((A, h_new))
+        b.append(1)
+
+    if max_landmarks is None :
+        # Read the parameters from the file
+        with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+            parameters = json.loads(f.read())
+            max_landmarks = parameters['max landmarks']
+
+    A = np.vstack((A, ones*L))
+    b.append(max_landmarks+1)
+
+    return A, b
+
+
+# Constraint to not have d14 and d41 simultaneously. Does not prevent cyclic paths with more elements
+def break_sym(L):
+
+    upper_ind = np.triu_indices(L,0,L)
+
+    up_ind_x = upper_ind[0]
+    up_ind_y = upper_ind[1]
+
+    A = [0]*L*L         # useless row to prevent overhead ? better solution welcomed
+    # A[up_ind_x[0]*L + up_ind_y[0]] = 1
+    # A[up_ind_y[0]*L + up_ind_x[0]] = 1
+    b = [1]
+
+    for i, _ in enumerate(up_ind_x[1:]) :
+        l = [0]*L*L
+        if up_ind_x[i] != up_ind_y[i] :
+            l[up_ind_x[i]*L + up_ind_y[i]] = 1
+            l[up_ind_y[i]*L + up_ind_x[i]] = 1
+
+            A = np.vstack((A,l))
+            b.append(1)
+
+    return A, b
+
+
+# Constraint to not stay in position. Removes d11, d22, d33, etc.
+def init_eq_not_stay(L: int): 
+    l = [0]*L*L
+
+    for i in range(L) :
+        for j in range(L) :
+            if j == i :
+                l[j + i*L] = 1
+    
+    l = np.array(np.array(l), dtype=np.int8)
+
+    return [l], [0]
+
+
+# Go through the landmarks and force the optimizer to use landmarks marked as must_do
+def respect_user_must_do(landmarks: List[Landmark]) :
+    L = len(landmarks)
+
+    A = [0]*L*L
+    b = [0]
+    for i, elem in enumerate(landmarks[1:]) :
+        if elem.must_do is True and elem.name not in ['finish', 'start']:
+            l = [0]*L*L
+            l[i*L:i*L+L] = [1]*L        # set mandatory departures from landmarks tagged as 'must_do'
+
+            A = np.vstack((A,l))
+            b.append(1)
+
+    return A, b
+
+
+# Constraint to ensure start at start and finish at goal
+def respect_start_finish(L: int):
+    l_start = [1]*L + [0]*L*(L-1)   # sets departures only for start (horizontal ones)
+    l_start[L-1] = 0                # prevents the jump from start to finish
+    l_goal = [0]*L*L                # sets arrivals only for finish (vertical ones)
+    l_L = [0]*L*(L-1) + [1]*L       # prevents arrivals at start and departures from goal
+    for k in range(L-1) :           # sets only vertical ones for goal (go to)
+        l_L[k*L] = 1
+        if k != 0 :
+            l_goal[k*L+L-1] = 1 
+            
+
+    A = np.vstack((l_start, l_goal))
+    b = [1, 1]
+    A = np.vstack((A,l_L))
+    b.append(0)
+
+    return A, b
+
+
+# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
+def respect_order(L: int): 
+
+    A = [0]*L*L                     # useless row to reduce overhead ? better solution is welcome
+    b = [0]
+    for i in range(L-1) :           # Prevent stacked ones
+        if i == 0 or i == L-1:      # Don't touch start or finish
+            continue
+        else : 
+            l = [0]*L
+            l[i] = -1
+            l = l*L
+            for j in range(L) :
+                l[i*L + j] = 1
+
+            A = np.vstack((A,l))
+            b.append(0)
+
+    return A, b
+
+
+# Computes the time to reach from each landmark to the next
+def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        detour_factor = parameters['detour factor']
+        speed = parameters['average walking speed']
+    
+    L =  []
+    j = 0
+    total_dist = 0
+    while j < len(order)-1 :
+        elem = landmarks[order[j]]
+        next = landmarks[order[j+1]]
+
+        d = get_time(elem.location, next.location, detour_factor, speed)
+        elem.time_to_reach_next = d
+        elem.must_do = True
+        elem.location = (round(elem.location[0], 5), round(elem.location[1], 5))
+        L.append(elem)
+        j += 1
+        total_dist += d
+
+    next.location = (round(next.location[0], 5), round(next.location[1], 5))   
+    L.append(next)
+    
+    return L, total_dist
+
+
+# Same as link_list but does it on a already ordered list
+def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        detour_factor = parameters['detour factor']
+        speed = parameters['average walking speed']
+
+    L =  []
+    j = 0
+    total_dist = 0
+    while j < len(ordered_visit)-1 :
+        elem = ordered_visit[j]
+        next = ordered_visit[j+1]
+
+        elem.next_uuid = next.uuid
+        d = get_time(elem.location, next.location, detour_factor, speed)
+        elem.time_to_reach_next = d
+        if elem.name not in ['start', 'finish'] :
+            elem.must_do = True
+        L.append(elem)
+        j += 1
+        total_dist += d
+
+    L.append(next)
+    
+    return L, total_dist
+
+
+# Main optimization pipeline
+def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_details: bool, max_landmarks = None) :
+
+    L = len(landmarks)
+
+    # SET CONSTRAINTS FOR INEQUALITY
+    c, A_ub, b_ub = init_ub_dist(landmarks, max_steps)          # Add the distances from each landmark to the other
+    A, b = respect_number(L, max_landmarks)                     # Respect max number of visits (no more possible stops than landmarks). 
+    A_ub = np.vstack((A_ub, A), dtype=np.int16)
+    b_ub += b
+    A, b = break_sym(L)                                         # break the 'zig-zag' symmetry
+    A_ub = np.vstack((A_ub, A), dtype=np.int16)
+    b_ub += b
+
+
+    # SET CONSTRAINTS FOR EQUALITY
+    A_eq, b_eq = init_eq_not_stay(L)                            # Force solution not to stay in same place
+    A, b = respect_user_must_do(landmarks)                      # Check if there are user_defined must_see. Also takes care of start/goal
+    A_eq = np.vstack((A_eq, A), dtype=np.int8)
+    b_eq += b
+    A, b = respect_start_finish(L)                  # Force start and finish positions
+    A_eq = np.vstack((A_eq, A), dtype=np.int8)
+    b_eq += b
+    A, b = respect_order(L)                         # Respect order of visit (only works when max_steps is limiting factor)
+    A_eq = np.vstack((A_eq, A), dtype=np.int8)
+    b_eq += b
+    
+    # SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
+    x_bounds = [(0, 1)]*L*L
+
+    # Solve linear programming problem
+    res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
+
+    # Raise error if no solution is found
+    if not res.success :
+            raise ArithmeticError("No solution could be found, the problem is overconstrained. Please adapt your must_dos")
+
+    # If there is a solution, we're good to go, just check for connectiveness
+    else :
+        order, circles = is_connected(res.x)
+        #nodes, edges = is_connected(res.x)
+        i = 0
+        timeout = 80
+        while circles is not None and i < timeout:
+            A, b = prevent_config(res.x)
+            A_ub = np.vstack((A_ub, A))
+            b_ub += b
+            #A_ub, b_ub = prevent_circle(order, len(landmarks), A_ub, b_ub)
+            for circle in circles :
+                A, b = prevent_circle(circle, len(landmarks))
+                A_eq = np.vstack((A_eq, A))
+                b_eq += b
+            res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
+            if not res.success :
+                print("Solving failed because of overconstrained problem")
+                return None
+            order, circles = is_connected(res.x)
+            #nodes, edges = is_connected(res.x)
+            if circles is None :
+                break
+            print(i)
+            i += 1
+        
+        if i == timeout :
+            raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
+
+        # Add the times to reach and stop optimizing
+        L, _ = link_list(order, landmarks)
+            
+        if printing_details is True :
+            if i != 0 :
+                print(f"Neded to recompute paths {i} times because of unconnected loops...")
+            print_res(L)
+            print("\nTotal score : " + str(int(-res.fun)))
+
+        return L
+
+
+
+
+
+
diff --git a/backend/src/parameters/landmarks_manager.params b/backend/src/parameters/landmarks_manager.params
index 942c214..e0cbe35 100644
--- a/backend/src/parameters/landmarks_manager.params
+++ b/backend/src/parameters/landmarks_manager.params
@@ -1,8 +1,8 @@
 {
-  "city bbox side" : 10,
-  "radius close to" : 27.5,
-  "church coeff" : 0.6,
-  "park coeff" : 1.5,
-  "tag coeff" : 100,
+  "city bbox side" : 5000,
+  "radius close to" : 50,
+  "church coeff" : 0.8,
+  "park coeff" : 1.2,
+  "tag coeff" : 10,
   "N important" : 40
 }
\ No newline at end of file
diff --git a/backend/src/parameters/optimizer.params b/backend/src/parameters/optimizer.params
index 18ca240..b971821 100644
--- a/backend/src/parameters/optimizer.params
+++ b/backend/src/parameters/optimizer.params
@@ -1,5 +1,5 @@
 {
   "detour factor" : 1.4,
   "average walking speed" : 4.8,
-  "max landmarks" : 10
+  "max landmarks" : 7
 }
\ No newline at end of file
diff --git a/backend/src/refiner.py b/backend/src/refiner.py
index 282f2f9..1971170 100644
--- a/backend/src/refiner.py
+++ b/backend/src/refiner.py
@@ -1,17 +1,15 @@
-from collections import defaultdict
-from heapq import heappop, heappush
-from itertools import permutations
 import os, json
 
-from shapely import buffer, LineString, Point, Polygon, MultiPoint, convex_hull, concave_hull, LinearRing
+from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
 from typing import List, Tuple
 from math import pi
 
 from structs.landmarks import Landmark
 from landmarks_manager import take_most_important
-from optimizer import solve_optimization, link_list_simple, print_res, get_distance
+from optimizer_v4 import solve_optimization, link_list_simple, print_res, get_time
 
 
+# Create corridor from tour
 def create_corridor(landmarks: List[Landmark], width: float) :
 
   corrected_width = (180*width)/(6371000*pi)
@@ -22,6 +20,7 @@ def create_corridor(landmarks: List[Landmark], width: float) :
   return obj
 
 
+# Create linestring from tour
 def create_linestring(landmarks: List[Landmark])->List[Point] :
 
   points = []
@@ -32,11 +31,13 @@ def create_linestring(landmarks: List[Landmark])->List[Point] :
   return LineString(points)
 
 
+# Check if some coordinates are in area. Used for the corridor 
 def is_in_area(area: Polygon, coordinates) -> bool :
   point = Point(coordinates)
   return point.within(area)
 
 
+# Function to determine if two landmarks are close to each other 
 def is_close_to(location1: Tuple[float], location2: Tuple[float]):
     """Determine if two locations are close by rounding their coordinates to 3 decimals."""
     absx = abs(location1[0] - location2[0])
@@ -46,6 +47,7 @@ def is_close_to(location1: Tuple[float], location2: Tuple[float]):
     #return (round(location1[0], 3), round(location1[1], 3)) == (round(location2[0], 3), round(location2[1], 3))
 
 
+# Rearrange some landmarks in the order of visit to group visit 
 def rearrange(landmarks: List[Landmark]) -> List[Landmark]:
   
   i = 1
@@ -62,66 +64,9 @@ def rearrange(landmarks: List[Landmark]) -> List[Landmark]:
   
   return landmarks
 
-"""
-def find_shortest_path(landmarks: List[Landmark]) -> List[Landmark]:
-    
-  # Read from data
-  with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
-    parameters = json.loads(f.read())
-    detour = parameters['detour factor']
-    speed = parameters['average walking speed']
 
-  # Step 1: Build the graph
-  graph = defaultdict(list)
-  for i in range(len(landmarks)):
-    for j in range(len(landmarks)):
-      if i != j:
-        distance = get_distance(landmarks[i].location, landmarks[j].location, detour, speed)[1]
-        graph[i].append((distance, j))
-
-  # Step 2: Dijkstra's algorithm to find the shortest path from start to finish
-  start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
-  finish_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'finish')
-
-  distances = {i: float('inf') for i in range(len(landmarks))}
-  previous_nodes = {i: None for i in range(len(landmarks))}
-  distances[start_idx] = 0
-  priority_queue = [(0, start_idx)]
-
-  while priority_queue:
-    current_distance, current_index = heappop(priority_queue)
-
-    if current_distance > distances[current_index]:
-      continue
-
-    for neighbor_distance, neighbor_index in graph[current_index]:
-      distance = current_distance + neighbor_distance
-
-      if distance < distances[neighbor_index]:
-        distances[neighbor_index] = distance
-        previous_nodes[neighbor_index] = current_index
-        heappush(priority_queue, (distance, neighbor_index))
-
-  # Step 3: Backtrack from finish to start to find the path
-  path = []
-  current_index = finish_idx
-  while current_index is not None:
-    path.append(landmarks[current_index])
-    current_index = previous_nodes[current_index]
-  path.reverse()
-
-  return path
-"""
-"""
-def total_path_distance(path: List[Landmark], detour, speed) -> float:
-  total_distance = 0
-  for i in range(len(path) - 1):
-    total_distance += get_distance(path[i].location, path[i + 1].location, detour, speed)[1]
-  return total_distance
-"""
-
-
-def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> List[Landmark]:
+# Simple nearest neighbour planner to try to fix the path
+def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> Tuple[List[Landmark], Polygon]:
     
   # Read from data
   with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
@@ -148,7 +93,7 @@ def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> List[
   
   # Step 4: Use nearest neighbor heuristic to visit all landmarks
   while unvisited_landmarks:
-    nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_distance(current_landmark.location, lm.location, detour, speed)[1])
+    nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_time(current_landmark.location, lm.location, detour, speed))
     path.append(nearest_landmark)
     coordinates.append(nearest_landmark.location)
     current_landmark = nearest_landmark
@@ -162,6 +107,8 @@ def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> List[
 
   return path, path_poly
 
+
+# Returns a list of minor landmarks around the planned path to enhance experience
 def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[Landmark], width: float) -> List[Landmark] :
 
   second_order_landmarks = []
@@ -178,116 +125,120 @@ def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[L
   return take_most_important(second_order_landmarks, len(visited_landmarks))
 
 
+# Try fix the shortest path using shapely
+def fix_using_polygon(tour: List[Landmark])-> List[Landmark] :
 
-"""def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
+  coords = []
+  coords_dict = {}
+  for landmark in tour :
+    coords.append(landmark.location)
+    if landmark.name != 'finish' :
+      coords_dict[landmark.location] = landmark
 
-  minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
+  tour_poly = Polygon(coords)
+  
+  better_tour_poly = tour_poly.buffer(0)
+  try :
+    xs, ys = better_tour_poly.exterior.xy
 
-  if print_infos : print("There are " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
+    if len(xs) != len(tour) :
+      better_tour_poly = concave_hull(MultiPoint(coords))  # Create concave hull with "core" of tour leaving out start and finish
+      xs, ys = better_tour_poly.exterior.xy
 
-  full_set = base_tour[:-1] + minor_landmarks   # create full set of possible landmarks (without finish)
-  full_set.append(base_tour[-1])                # add finish back
-
-  new_tour = solve_optimization(full_set, max_time, print_infos)
-
-  return new_tour"""
+  except :
+    better_tour_poly = concave_hull(MultiPoint(coords))  # Create concave hull with "core" of tour leaving out start and finish
+    xs, ys = better_tour_poly.exterior.xy
 
 
-def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
+  # reverse the xs and ys
+  xs.reverse()
+  ys.reverse()
+
+  better_tour = []   # List of ordered visit
+  name_index = {}     # Maps the name of a landmark to its index in the concave polygon
+
+  # Loop through the polygon and generate the better (ordered) tour
+  for i,x in enumerate(xs[:-1]) :
+    y = ys[i]
+    better_tour.append(coords_dict[tuple((x,y))])
+    name_index[coords_dict[tuple((x,y))].name] = i
+
+
+  # Scroll the list to have start in front again
+  start_index = name_index['start']
+  better_tour = better_tour[start_index:] + better_tour[:start_index]
+
+  # Append the finish back and correct the time to reach
+  better_tour.append(tour[-1])
+
+  # Rearrange only if polygon still not simple
+  if not better_tour_poly.is_simple :
+    better_tour = rearrange(better_tour)
+  
+  return better_tour
+
+
+# Second stage of the optimization. Use linear programming again to refine the path
+def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, detour: int, print_infos: bool) -> List[Landmark] :
 
   # Read from the file
   with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
     parameters = json.loads(f.read())
-    max_landmarks = parameters['max landmarks']
-  
-  if len(base_tour)-2 >= max_landmarks :
-    return base_tour
-  
+    max_landmarks = parameters['max landmarks'] + 4
 
-  minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
-
-  if print_infos : print("Using " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
-
-  # full set of visitable landmarks
-  full_set = base_tour[:-1] + minor_landmarks   # create full set of possible landmarks (without finish)
-  full_set.append(base_tour[-1])                # add finish back
-
-  # get a new tour
-  new_tour = solve_optimization(full_set, max_time, False)
-  new_tour, new_dist = link_list_simple(new_tour)
-  
-  """#if base_tour[0].location == base_tour[-1].location :
+  # No need to refine if no detour is taken
+  # if detour == 0 :
   if False :
-    coords = []         # Coordinates of the new tour
-    coords_dict = {}    # maps the location of an element to the element itself. Used to access the elements back once we get the geometry
-
-    # Iterate through the new tour without finish
-    for elem in new_tour[:-1] :
-      coords.append(Point(elem.location))
-      coords_dict[elem.location] = elem         # if start = goal, only finish remains
-
-    # Create a concave polygon using the coordinates
-    better_tour_poly = concave_hull(MultiPoint(coords))  # Create concave hull with "core" of tour leaving out start and finish
-    xs, ys = better_tour_poly.exterior.xy
-
-    # reverse the xs and ys
-    xs.reverse()
-    ys.reverse()
-
-    better_tour = []   # List of ordered visit
-    name_index = {}     # Maps the name of a landmark to its index in the concave polygon
-
-    # Loop through the polygon and generate the better (ordered) tour
-    for i,x in enumerate(xs[:-1]) :
-      better_tour.append(coords_dict[tuple((x,ys[i]))])
-      name_index[coords_dict[tuple((x,ys[i]))].name] = i
-
-
-    # Scroll the list to have start in front again
-    start_index = name_index['start']
-    better_tour = better_tour[start_index:] + better_tour[:start_index]
-
-    # Append the finish back and correct the time to reach
-    better_tour.append(new_tour[-1])
-
-    # Rearrange only if polygon
-    better_tour = rearrange(better_tour)
-
-    # Add the time to reach
-    better_tour = add_time_to_reach_simple(better_tour)
-  """
+    new_tour = base_tour
   
+  else :
+    minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
 
-  """
-  if not better_poly.is_simple :
-    
-    coords_dict = {}
-    better_tour2 = []
-    for elem in better_tour :
-      coords_dict[elem.location] = elem
+    if print_infos : print("Using " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
 
-    better_poly2 = better_poly.buffer(0)
-    new_coords = better_poly2.exterior.coords[:]
-    start_coords = base_tour[0].location
-    start_index = new_coords.
+    # full set of visitable landmarks
+    full_set = base_tour[:-1] + minor_landmarks   # create full set of possible landmarks (without finish)
+    full_set.append(base_tour[-1])                # add finish back
 
-    #for point in new_coords :
-  """
+    # get a new tour
+    new_tour = solve_optimization(full_set, max_time+detour, False, max_landmarks)
+    if new_tour is None :
+      new_tour = base_tour
 
+  # Link the new tour
+  new_tour, new_dist = link_list_simple(new_tour)
 
+  # If the tour contains only one landmark, return
+  if len(new_tour) < 4 :
+    return new_tour
+  
+  # Find shortest path using the nearest neighbor heuristic
   better_tour, better_poly = find_shortest_path_through_all_landmarks(new_tour)
+
+  # Fix the tour using Polygons if the path looks weird
+  if base_tour[0].location == base_tour[-1].location and not better_poly.is_valid :
+    better_tour = fix_using_polygon(better_tour)
+  
+  # Link the tour again
   better_tour, better_dist = link_list_simple(better_tour)
 
+  # Choose the better tour depending on walked distance
   if new_dist < better_dist :
     final_tour = new_tour
   else :
     final_tour = better_tour
 
   if print_infos :
-    print("\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n")
-    print("\nRefined tour (result of second stage optimization): ")
-    print_res(final_tour, len(full_set))
+    print("\n\n\nRefined tour (result of second stage optimization): ")
+    print_res(final_tour)
+    total_score = 0
+    for elem in final_tour : 
+      total_score += elem.attractiveness
+
+    print("\nTotal score : " + str(total_score))
 
 
   
   return final_tour
+
+
diff --git a/backend/src/structs/landmarks.py b/backend/src/structs/landmarks.py
index d02dc5f..2448236 100644
--- a/backend/src/structs/landmarks.py
+++ b/backend/src/structs/landmarks.py
@@ -7,8 +7,6 @@ from uuid import uuid4
 
 # Output to frontend
 class Landmark(BaseModel) :
-    # Unique ID of a given landmark
-    uuid: str = Field(default_factory=uuid4)                    # TODO implement this ASAP
     
     # Properties of the landmark
     name : str
@@ -21,6 +19,9 @@ class Landmark(BaseModel) :
     image_url : Optional[str] = None                            # TODO future
     description : Optional[str] = None                          # TODO future
     duration : Optional[int] = 0                                # TODO future
+
+    # Unique ID of a given landmark
+    uuid: str = Field(default_factory=uuid4)                    # TODO implement this ASAP
     
     # Additional properties depending on specific tour
     must_do : bool
diff --git a/backend/src/tester.py b/backend/src/tester.py
index 91a12b2..acc84d6 100644
--- a/backend/src/tester.py
+++ b/backend/src/tester.py
@@ -4,7 +4,7 @@ from typing import List
 from landmarks_manager import generate_landmarks
 from fastapi.encoders import jsonable_encoder
 
-from optimizer import solve_optimization
+from optimizer_v4 import solve_optimization
 from refiner import refine_optimization
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
@@ -23,7 +23,8 @@ def write_data(L: List[Landmark], file_name: str):
 
     data.to_json(file_name, indent = 2, force_ascii=False)
 
-def test3(city_country: str) -> List[Landmark]:
+
+def test4(coordinates: tuple[float, float]) -> List[Landmark]:
 
 
     preferences = Preferences(
@@ -38,52 +39,12 @@ def test3(city_country: str) -> List[Landmark]:
                     shopping=Preference(
                                   name='shopping', 
                                   type=LandmarkType(landmark_type='shopping'),
-                                  score = 5))
-
-    coordinates = None
-
-    landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
-
-    #write_data(landmarks)
-
-    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.2044576, 16.3870242), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.2044576, 16.3870242), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    
-
-    test = landmarks_short
-    
-    test.insert(0, start)
-    test.append(finish)
-
-    max_walking_time = 2 # hours
-
-    visiting_list = solve_optimization(test, max_walking_time*60, True)
-
-
-    
-
-
-def test4(coordinates: tuple[float, float]) -> List[Landmark]:
-
-
-    preferences = Preferences(
-                    sightseeing=Preference(
-                                  name='sightseeing', 
-                                  type=LandmarkType(landmark_type='sightseeing'),
-                                  score = 5),
-                    nature=Preference(
-                                  name='nature', 
-                                  type=LandmarkType(landmark_type='nature'),
-                                  score = 5),
-                    shopping=Preference(
-                                  name='shopping', 
-                                  type=LandmarkType(landmark_type='shopping'),
-                                  score = 5))
+                                  score = 0))
 
 
     # Create start and finish 
-    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=coordinates, osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=coordinates, osm_type='start', osm_id=0, attractiveness=0, must_do=False, n_tags = 0)
+    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=False, n_tags = 0)
     #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8777055, 2.3640967), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     #start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.847132, 2.312359), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.843185, 2.344533), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
@@ -91,26 +52,29 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
     
     # Generate the landmarks from the start location
     landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
-    #write_data(landmarks, "landmarks.txt")
+    #write_data(landmarks, "landmarks_Wien.txt")
 
     # Insert start and finish to the landmarks list
     landmarks_short.insert(0, start)
     landmarks_short.append(finish)
 
-    # TODO use these parameters in another way
-    max_walking_time = 2    # hours
-    detour = 30             # minutes
+    max_walking_time = 180      # minutes
+    detour = 0                 # minutes
 
     # First stage optimization
-    base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
-    
-    # Second stage optimization
-    refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
+    base_tour = solve_optimization(landmarks_short, max_walking_time, True)
+
+    # Second stage using linear optimization
     
+    refined_tour = refine_optimization(landmarks, base_tour, max_walking_time, detour, True)
+
+
     return refined_tour
 
 
-test4(tuple((48.8344400, 2.3220540)))       # Café Chez César 
+#test4(tuple((48.8344400, 2.3220540)))       # Café Chez César 
 #test4(tuple((48.8375946, 2.2949904)))       # Point random
 #test4(tuple((47.377859, 8.540585)))         # Zurich HB
-#test3('Vienna, Austria')
\ No newline at end of file
+#test4(tuple((45.7576485, 4.8330241)))      # Lyon Bellecour
+test4(tuple((48.5848435, 7.7332974)))      # Strasbourg Gare
+#test4(tuple((48.2067858, 16.3692340)))      # Vienne
\ No newline at end of file
diff --git a/backend/throttle.ctrl b/backend/throttle.ctrl
new file mode 100644
index 0000000..e69de29
diff --git a/landmarks.txt b/landmarks.txt
deleted file mode 100644
index 7ee474a..0000000
--- a/landmarks.txt
+++ /dev/null
@@ -1,9730 +0,0 @@
-{
-  "0":{
-    "name":"Musée du quai Branly - Jacques Chirac",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8609823,
-      2.2977973
-    ],
-    "osm_type":"way",
-    "osm_id":17954721,
-    "attractiveness":30,
-    "must_do":false,
-    "n_tags":30,
-    "time_to_reach":0
-  },
-  "1":{
-    "name":"Bourse de Commerce — Pinault Collection",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8628167,
-      2.3428183
-    ],
-    "osm_type":"way",
-    "osm_id":19856722,
-    "attractiveness":32,
-    "must_do":false,
-    "n_tags":32,
-    "time_to_reach":0
-  },
-  "2":{
-    "name":"Galerie de Minéralogie et de Géologie",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8418045,
-      2.3577296
-    ],
-    "osm_type":"way",
-    "osm_id":21999357,
-    "attractiveness":16,
-    "must_do":false,
-    "n_tags":16,
-    "time_to_reach":0
-  },
-  "3":{
-    "name":"Galeries de Paléontologie et d'Anatomie comparée",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8431475,
-      2.3628815
-    ],
-    "osm_type":"way",
-    "osm_id":21999454,
-    "attractiveness":15,
-    "must_do":false,
-    "n_tags":15,
-    "time_to_reach":0
-  },
-  "4":{
-    "name":"Institut du Monde Arabe",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8489231,
-      2.3572223
-    ],
-    "osm_type":"way",
-    "osm_id":22870791,
-    "attractiveness":21,
-    "must_do":false,
-    "n_tags":21,
-    "time_to_reach":0
-  },
-  "5":{
-    "name":"Jardin Tino Rossi - Musée de la Sculpture en Plein Air",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8474186,
-      2.3607371
-    ],
-    "osm_type":"way",
-    "osm_id":23644147,
-    "attractiveness":13,
-    "must_do":false,
-    "n_tags":8,
-    "time_to_reach":0
-  },
-  "6":{
-    "name":"Halle Saint-Pierre",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8848435,
-      2.3445246
-    ],
-    "osm_type":"way",
-    "osm_id":45073393,
-    "attractiveness":13,
-    "must_do":false,
-    "n_tags":13,
-    "time_to_reach":0
-  },
-  "7":{
-    "name":"Jeu de Paume",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8658156,
-      2.3240828
-    ],
-    "osm_type":"way",
-    "osm_id":54188994,
-    "attractiveness":21,
-    "must_do":false,
-    "n_tags":20,
-    "time_to_reach":0
-  },
-  "8":{
-    "name":"Musée de l'Orangerie",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8637655,
-      2.3226594
-    ],
-    "osm_type":"way",
-    "osm_id":54188996,
-    "attractiveness":24,
-    "must_do":false,
-    "n_tags":24,
-    "time_to_reach":0
-  },
-  "9":{
-    "name":"Atelier Brancusi",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8614029,
-      2.3519903
-    ],
-    "osm_type":"way",
-    "osm_id":55503399,
-    "attractiveness":13,
-    "must_do":false,
-    "n_tags":13,
-    "time_to_reach":0
-  },
-  "10":{
-    "name":"Lafayette Anticipations",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8590014,
-      2.354757
-    ],
-    "osm_type":"way",
-    "osm_id":55865819,
-    "attractiveness":9,
-    "must_do":false,
-    "n_tags":9,
-    "time_to_reach":0
-  },
-  "11":{
-    "name":"Grand Palais",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8661514,
-      2.3122667
-    ],
-    "osm_type":"way",
-    "osm_id":56185523,
-    "attractiveness":22,
-    "must_do":false,
-    "n_tags":22,
-    "time_to_reach":0
-  },
-  "12":{
-    "name":"Pavillon de l'Arsenal",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8505861,
-      2.3621853
-    ],
-    "osm_type":"way",
-    "osm_id":56186898,
-    "attractiveness":19,
-    "must_do":false,
-    "n_tags":19,
-    "time_to_reach":0
-  },
-  "13":{
-    "name":"Musée de Cluny",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8506604,
-      2.3437398
-    ],
-    "osm_type":"way",
-    "osm_id":56640163,
-    "attractiveness":18,
-    "must_do":false,
-    "n_tags":18,
-    "time_to_reach":0
-  },
-  "14":{
-    "name":"Musée d'art et d'histoire du Judaïsme",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8612656,
-      2.355418
-    ],
-    "osm_type":"way",
-    "osm_id":56687783,
-    "attractiveness":9,
-    "must_do":false,
-    "n_tags":8,
-    "time_to_reach":0
-  },
-  "15":{
-    "name":"Musée national Picasso-Paris",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8598781,
-      2.3620895
-    ],
-    "osm_type":"way",
-    "osm_id":57745741,
-    "attractiveness":21,
-    "must_do":false,
-    "n_tags":20,
-    "time_to_reach":0
-  },
-  "16":{
-    "name":"Musée Cognacq-Jay",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8581215,
-      2.3616628
-    ],
-    "osm_type":"way",
-    "osm_id":57781649,
-    "attractiveness":16,
-    "must_do":false,
-    "n_tags":16,
-    "time_to_reach":0
-  },
-  "17":{
-    "name":"Argonaute",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8945348,
-      2.3895646
-    ],
-    "osm_type":"way",
-    "osm_id":58169110,
-    "attractiveness":16,
-    "must_do":false,
-    "n_tags":16,
-    "time_to_reach":0
-  },
-  "18":{
-    "name":"Orangerie du Sénat",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8483243,
-      2.3344262
-    ],
-    "osm_type":"way",
-    "osm_id":62848404,
-    "attractiveness":10,
-    "must_do":false,
-    "n_tags":10,
-    "time_to_reach":0
-  },
-  "19":{
-    "name":"Les Réserves du Musée des Arts et Métiers",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.9142687,
-      2.3604902
-    ],
-    "osm_type":"way",
-    "osm_id":63992928,
-    "attractiveness":4,
-    "must_do":false,
-    "n_tags":4,
-    "time_to_reach":0
-  },
-  "20":{
-    "name":"Musée Ernest-Hébert (en travaux)",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8473326,
-      2.3227159
-    ],
-    "osm_type":"way",
-    "osm_id":64314872,
-    "attractiveness":10,
-    "must_do":false,
-    "n_tags":9,
-    "time_to_reach":0
-  },
-  "21":{
-    "name":"Musée Nissim de Camondo",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8791676,
-      2.3124361
-    ],
-    "osm_type":"way",
-    "osm_id":68353844,
-    "attractiveness":9,
-    "must_do":false,
-    "n_tags":9,
-    "time_to_reach":0
-  },
-  "22":{
-    "name":"Musée Cernuschi",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8794667,
-      2.312538
-    ],
-    "osm_type":"way",
-    "osm_id":68353920,
-    "attractiveness":18,
-    "must_do":false,
-    "n_tags":18,
-    "time_to_reach":0
-  },
-  "23":{
-    "name":"Musée Jacquemart-André",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8755169,
-      2.3105464
-    ],
-    "osm_type":"way",
-    "osm_id":68804399,
-    "attractiveness":23,
-    "must_do":false,
-    "n_tags":23,
-    "time_to_reach":0
-  },
-  "24":{
-    "name":"Pagoda Paris",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8767479,
-      2.3077454
-    ],
-    "osm_type":"way",
-    "osm_id":68828954,
-    "attractiveness":14,
-    "must_do":false,
-    "n_tags":14,
-    "time_to_reach":0
-  },
-  "25":{
-    "name":"Pavillon de Vendôme",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.9047868,
-      2.3038403
-    ],
-    "osm_type":"way",
-    "osm_id":74468585,
-    "attractiveness":17,
-    "must_do":false,
-    "n_tags":17,
-    "time_to_reach":0
-  },
-  "26":{
-    "name":"Galerie des Gobelins",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8348138,
-      2.3528
-    ],
-    "osm_type":"way",
-    "osm_id":78407170,
-    "attractiveness":6,
-    "must_do":false,
-    "n_tags":6,
-    "time_to_reach":0
-  },
-  "27":{
-    "name":"Musée Yves Saint Laurent Paris",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8655978,
-      2.2993165
-    ],
-    "osm_type":"way",
-    "osm_id":79219238,
-    "attractiveness":11,
-    "must_do":false,
-    "n_tags":11,
-    "time_to_reach":0
-  },
-  "28":{
-    "name":"Musée d'Art Moderne de Paris",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8643053,
-      2.297793
-    ],
-    "osm_type":"way",
-    "osm_id":79219308,
-    "attractiveness":30,
-    "must_do":false,
-    "n_tags":30,
-    "time_to_reach":0
-  },
-  "29":{
-    "name":"Fondation Cartier pour l'art contemporain",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8373189,
-      2.3319319
-    ],
-    "osm_type":"way",
-    "osm_id":79616736,
-    "attractiveness":21,
-    "must_do":false,
-    "n_tags":21,
-    "time_to_reach":0
-  },
-  "30":{
-    "name":"Centre Tignous d'Art Contemporain",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8567341,
-      2.4270255
-    ],
-    "osm_type":"way",
-    "osm_id":81767364,
-    "attractiveness":8,
-    "must_do":false,
-    "n_tags":8,
-    "time_to_reach":0
-  },
-  "31":{
-    "name":"Grande Galerie de l'Évolution",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8421181,
-      2.3562418
-    ],
-    "osm_type":"way",
-    "osm_id":83913793,
-    "attractiveness":21,
-    "must_do":false,
-    "n_tags":21,
-    "time_to_reach":0
-  },
-  "32":{
-    "name":"Musée national Jean-Jacques Henner",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8829326,
-      2.3076157
-    ],
-    "osm_type":"way",
-    "osm_id":84326105,
-    "attractiveness":11,
-    "must_do":false,
-    "n_tags":11,
-    "time_to_reach":0
-  },
-  "33":{
-    "name":"Musée de la Vie Romantique",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.881203,
-      2.333519
-    ],
-    "osm_type":"way",
-    "osm_id":84511123,
-    "attractiveness":22,
-    "must_do":false,
-    "n_tags":22,
-    "time_to_reach":0
-  },
-  "34":{
-    "name":"Mémorial de la Shoah",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8548862,
-      2.3560564
-    ],
-    "osm_type":"way",
-    "osm_id":124052479,
-    "attractiveness":9,
-    "must_do":false,
-    "n_tags":8,
-    "time_to_reach":0
-  },
-  "35":{
-    "name":"Musée Zadkine",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8428133,
-      2.3337722
-    ],
-    "osm_type":"way",
-    "osm_id":153776401,
-    "attractiveness":12,
-    "must_do":false,
-    "n_tags":12,
-    "time_to_reach":0
-  },
-  "36":{
-    "name":"Musée de Montmartre",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8880654,
-      2.3406347
-    ],
-    "osm_type":"way",
-    "osm_id":154679327,
-    "attractiveness":14,
-    "must_do":false,
-    "n_tags":14,
-    "time_to_reach":0
-  },
-  "37":{
-    "name":"Crypte Archéologique du Parvis Notre-Dame",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8535851,
-      2.3480846
-    ],
-    "osm_type":"way",
-    "osm_id":159896046,
-    "attractiveness":18,
-    "must_do":false,
-    "n_tags":18,
-    "time_to_reach":0
-  },
-  "38":{
-    "name":"Manufacture des Gobelins",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8344502,
-      2.3520875
-    ],
-    "osm_type":"way",
-    "osm_id":161119956,
-    "attractiveness":14,
-    "must_do":false,
-    "n_tags":14,
-    "time_to_reach":0
-  },
-  "39":{
-    "name":"Musée du Luxembourg",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8485965,
-      2.3340156
-    ],
-    "osm_type":"way",
-    "osm_id":170226810,
-    "attractiveness":15,
-    "must_do":false,
-    "n_tags":15,
-    "time_to_reach":0
-  },
-  "40":{
-    "name":"Institut suédois",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.858226,
-      2.3619639
-    ],
-    "osm_type":"way",
-    "osm_id":243973065,
-    "attractiveness":16,
-    "must_do":false,
-    "n_tags":16,
-    "time_to_reach":0
-  },
-  "41":{
-    "name":"Musée national Eugène Delacroix",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8546662,
-      2.3353836
-    ],
-    "osm_type":"way",
-    "osm_id":395785603,
-    "attractiveness":15,
-    "must_do":false,
-    "n_tags":15,
-    "time_to_reach":0
-  },
-  "42":{
-    "name":"Géoroom",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8454453,
-      2.4238848
-    ],
-    "osm_type":"way",
-    "osm_id":541473683,
-    "attractiveness":9,
-    "must_do":false,
-    "n_tags":9,
-    "time_to_reach":0
-  },
-  "43":{
-    "name":"Musée des Moulages",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8721889,
-      2.3681344
-    ],
-    "osm_type":"way",
-    "osm_id":692817231,
-    "attractiveness":12,
-    "must_do":false,
-    "n_tags":12,
-    "time_to_reach":0
-  },
-  "44":{
-    "name":"Grand palais éphémère",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8531684,
-      2.3024461
-    ],
-    "osm_type":"way",
-    "osm_id":854459034,
-    "attractiveness":19,
-    "must_do":false,
-    "n_tags":19,
-    "time_to_reach":0
-  },
-  "45":{
-    "name":"Mémorial de Bobigny",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.9095992,
-      2.4295813
-    ],
-    "osm_type":"way",
-    "osm_id":1236950476,
-    "attractiveness":7,
-    "must_do":false,
-    "n_tags":7,
-    "time_to_reach":0
-  },
-  "46":{
-    "name":"Hôtel de Sully",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.854635,
-      2.3638126
-    ],
-    "osm_type":"relation",
-    "osm_id":403146,
-    "attractiveness":14,
-    "must_do":false,
-    "n_tags":13,
-    "time_to_reach":0
-  },
-  "47":{
-    "name":"Fondation Henri Cartier-Bresson",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8632278,
-      2.3598659
-    ],
-    "osm_type":"relation",
-    "osm_id":551459,
-    "attractiveness":22,
-    "must_do":false,
-    "n_tags":22,
-    "time_to_reach":0
-  },
-  "48":{
-    "name":"Hôtel de la Monnaie",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.856403,
-      2.3388625
-    ],
-    "osm_type":"relation",
-    "osm_id":967664,
-    "attractiveness":11,
-    "must_do":false,
-    "n_tags":11,
-    "time_to_reach":0
-  },
-  "49":{
-    "name":"Musée Bourdelle",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8432078,
-      2.3186583
-    ],
-    "osm_type":"relation",
-    "osm_id":1212876,
-    "attractiveness":23,
-    "must_do":false,
-    "n_tags":23,
-    "time_to_reach":0
-  },
-  "50":{
-    "name":"Institut Giacometti",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
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-    "osm_type":"way",
-    "osm_id":1074618792,
-    "attractiveness":8,
-    "must_do":false,
-    "n_tags":5,
-    "time_to_reach":0
-  },
-  "577":{
-    "name":"Square Jacques Manavian",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.899006,
-      2.3245959
-    ],
-    "osm_type":"way",
-    "osm_id":1104894585,
-    "attractiveness":5,
-    "must_do":false,
-    "n_tags":3,
-    "time_to_reach":0
-  },
-  "578":{
-    "name":"Square Henri Sellier",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.9027174,
-      2.3173947
-    ],
-    "osm_type":"way",
-    "osm_id":1157423264,
-    "attractiveness":3,
-    "must_do":false,
-    "n_tags":2,
-    "time_to_reach":0
-  },
-  "579":{
-    "name":"Jardins du Grand Échiquier",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.9175228,
-      2.3126717
-    ],
-    "osm_type":"way",
-    "osm_id":1184461730,
-    "attractiveness":3,
-    "must_do":false,
-    "n_tags":2,
-    "time_to_reach":0
-  },
-  "580":{
-    "name":"Parvis de l'Abbé Pierre",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.9091508,
-      2.4276158
-    ],
-    "osm_type":"way",
-    "osm_id":1236950475,
-    "attractiveness":3,
-    "must_do":false,
-    "n_tags":2,
-    "time_to_reach":0
-  },
-  "581":{
-    "name":"Jardin du Carré Rouge",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.9103226,
-      2.4259501
-    ],
-    "osm_type":"way",
-    "osm_id":1236950495,
-    "attractiveness":3,
-    "must_do":false,
-    "n_tags":2,
-    "time_to_reach":0
-  },
-  "582":{
-    "name":"Square Henri Galli",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.8513526,
-      2.3618576
-    ],
-    "osm_type":"relation",
-    "osm_id":2080074,
-    "attractiveness":14,
-    "must_do":false,
-    "n_tags":8,
-    "time_to_reach":0
-  },
-  "583":{
-    "name":"Square de l’église Notre-Dame-de-la-Croix",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.8687336,
-      2.3881113
-    ],
-    "osm_type":"relation",
-    "osm_id":2100826,
-    "attractiveness":9,
-    "must_do":false,
-    "n_tags":4,
-    "time_to_reach":0
-  },
-  "584":{
-    "name":"Coulée verte du sud parisien",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.7855092,
-      2.2944127
-    ],
-    "osm_type":"relation",
-    "osm_id":2646085,
-    "attractiveness":12,
-    "must_do":false,
-    "n_tags":8,
-    "time_to_reach":0
-  },
-  "585":{
-    "name":"Parc Monceau",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.8793706,
-      2.3088907
-    ],
-    "osm_type":"relation",
-    "osm_id":2826933,
-    "attractiveness":14,
-    "must_do":false,
-    "n_tags":9,
-    "time_to_reach":0
-  },
-  "586":{
-    "name":"Parc départemental Jean-Moulin - Les Guilands",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.8630132,
-      2.424942
-    ],
-    "osm_type":"relation",
-    "osm_id":6530383,
-    "attractiveness":14,
-    "must_do":false,
-    "n_tags":9,
-    "time_to_reach":0
-  },
-  "587":{
-    "name":"Parc de la Villette",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.89352,
-      2.3893334
-    ],
-    "osm_type":"relation",
-    "osm_id":7574623,
-    "attractiveness":13,
-    "must_do":false,
-    "n_tags":9,
-    "time_to_reach":0
-  },
-  "588":{
-    "name":"Les Jardins d'Éole",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.8876495,
-      2.3662081
-    ],
-    "osm_type":"relation",
-    "osm_id":11717983,
-    "attractiveness":9,
-    "must_do":false,
-    "n_tags":6,
-    "time_to_reach":0
-  },
-  "589":{
-    "name":"Jardin Alexandra David-Néel",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.8454726,
-      2.4228422
-    ],
-    "osm_type":"relation",
-    "osm_id":11989941,
-    "attractiveness":6,
-    "must_do":false,
-    "n_tags":3,
-    "time_to_reach":0
-  },
-  "590":{
-    "name":"Parc Clichy-Batignolles Martin Luther King",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.8911906,
-      2.313643
-    ],
-    "osm_type":"relation",
-    "osm_id":12273749,
-    "attractiveness":15,
-    "must_do":false,
-    "n_tags":10,
-    "time_to_reach":0
-  },
-  "591":{
-    "name":"Square de la Porte de la Villette",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.8998347,
-      2.3901654
-    ],
-    "osm_type":"relation",
-    "osm_id":13014387,
-    "attractiveness":8,
-    "must_do":false,
-    "n_tags":5,
-    "time_to_reach":0
-  },
-  "592":{
-    "name":"Square Rose Guérin\/Heidenheim",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.9075996,
-      2.300795
-    ],
-    "osm_type":"relation",
-    "osm_id":13725453,
-    "attractiveness":7,
-    "must_do":false,
-    "n_tags":3,
-    "time_to_reach":0
-  },
-  "593":{
-    "name":"Parc zoologique de Paris",
-    "type":{
-      "landmark_type":"nature"
-    },
-    "location":[
-      48.8318246,
-      2.4172936
-    ],
-    "osm_type":"way",
-    "osm_id":26008116,
-    "attractiveness":8,
-    "must_do":false,
-    "n_tags":7,
-    "time_to_reach":0
-  },
-  "594":{
-    "name":"Galerie Lafayette Maison \/ Gourmet",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.8730326,
-      2.3306871
-    ],
-    "osm_type":"way",
-    "osm_id":69224027,
-    "attractiveness":7,
-    "must_do":false,
-    "n_tags":7,
-    "time_to_reach":0
-  },
-  "595":{
-    "name":"Printemps",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.8738744,
-      2.3290548
-    ],
-    "osm_type":"way",
-    "osm_id":69224137,
-    "attractiveness":20,
-    "must_do":false,
-    "n_tags":20,
-    "time_to_reach":0
-  },
-  "596":{
-    "name":"Marché Saint-Honoré",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.8670006,
-      2.3317785
-    ],
-    "osm_type":"way",
-    "osm_id":54155008,
-    "attractiveness":7,
-    "must_do":false,
-    "n_tags":7,
-    "time_to_reach":0
-  },
-  "597":{
-    "name":"Passage du Grand-Cerf",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.8648328,
-      2.3493274
-    ],
-    "osm_type":"way",
-    "osm_id":54970437,
-    "attractiveness":8,
-    "must_do":false,
-    "n_tags":7,
-    "time_to_reach":0
-  },
-  "598":{
-    "name":"Centre Commercial Émile Dubois",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.9152909,
-      2.4027961
-    ],
-    "osm_type":"way",
-    "osm_id":73004279,
-    "attractiveness":5,
-    "must_do":false,
-    "n_tags":4,
-    "time_to_reach":0
-  },
-  "599":{
-    "name":"Bistrot des rosiers",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.902197,
-      2.3421451
-    ],
-    "osm_type":"way",
-    "osm_id":73835282,
-    "attractiveness":4,
-    "must_do":false,
-    "n_tags":4,
-    "time_to_reach":0
-  },
-  "600":{
-    "name":"Marché Dauphine",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.9018063,
-      2.341632
-    ],
-    "osm_type":"way",
-    "osm_id":73836239,
-    "attractiveness":4,
-    "must_do":false,
-    "n_tags":4,
-    "time_to_reach":0
-  },
-  "601":{
-    "name":"Marché Malassis",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.9016795,
-      2.3421091
-    ],
-    "osm_type":"way",
-    "osm_id":73838196,
-    "attractiveness":5,
-    "must_do":false,
-    "n_tags":5,
-    "time_to_reach":0
-  },
-  "602":{
-    "name":"Marché Malik",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.901541,
-      2.3400264
-    ],
-    "osm_type":"way",
-    "osm_id":73839906,
-    "attractiveness":4,
-    "must_do":false,
-    "n_tags":4,
-    "time_to_reach":0
-  },
-  "603":{
-    "name":"Centre Commercial de la Grande Porte",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.855103,
-      2.4162425
-    ],
-    "osm_type":"way",
-    "osm_id":81767599,
-    "attractiveness":4,
-    "must_do":false,
-    "n_tags":4,
-    "time_to_reach":0
-  },
-  "604":{
-    "name":"Avenir",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.9197328,
-      2.4219237
-    ],
-    "osm_type":"way",
-    "osm_id":84437363,
-    "attractiveness":11,
-    "must_do":false,
-    "n_tags":11,
-    "time_to_reach":0
-  },
-  "605":{
-    "name":"Marques Avenue",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.9225096,
-      2.3303125
-    ],
-    "osm_type":"way",
-    "osm_id":434347031,
-    "attractiveness":11,
-    "must_do":false,
-    "n_tags":11,
-    "time_to_reach":0
-  },
-  "606":{
-    "name":"Qwartz",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.92563,
-      2.3290174
-    ],
-    "osm_type":"way",
-    "osm_id":843953196,
-    "attractiveness":18,
-    "must_do":false,
-    "n_tags":18,
-    "time_to_reach":0
-  },
-  "607":{
-    "name":"Galerie Hoche",
-    "type":{
-      "landmark_type":"shopping"
-    },
-    "location":[
-      48.8908975,
-      2.4039477
-    ],
-    "osm_type":"relation",
-    "osm_id":1132674,
-    "attractiveness":5,
-    "must_do":false,
-    "n_tags":5,
-    "time_to_reach":0
-  }
-}
\ No newline at end of file
diff --git a/minor_landmarks.txt b/minor_landmarks.txt
deleted file mode 100644
index 8c3f4ab..0000000
--- a/minor_landmarks.txt
+++ /dev/null
@@ -1,482 +0,0 @@
-{
-  "0":{
-    "name":"Atelier Brancusi",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8614029,
-      2.3519903
-    ],
-    "osm_type":"way",
-    "osm_id":55503399,
-    "attractiveness":13,
-    "must_do":false,
-    "n_tags":13,
-    "time_to_reach":0
-  },
-  "1":{
-    "name":"Musée de Cluny",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8506604,
-      2.3437398
-    ],
-    "osm_type":"way",
-    "osm_id":56640163,
-    "attractiveness":18,
-    "must_do":false,
-    "n_tags":18,
-    "time_to_reach":0
-  },
-  "2":{
-    "name":"Musée du Luxembourg",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8485965,
-      2.3340156
-    ],
-    "osm_type":"way",
-    "osm_id":170226810,
-    "attractiveness":15,
-    "must_do":false,
-    "n_tags":15,
-    "time_to_reach":0
-  },
-  "3":{
-    "name":"Musée national Eugène Delacroix",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8546662,
-      2.3353836
-    ],
-    "osm_type":"way",
-    "osm_id":395785603,
-    "attractiveness":15,
-    "must_do":false,
-    "n_tags":15,
-    "time_to_reach":0
-  },
-  "4":{
-    "name":"Hôtel de Sully",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.854635,
-      2.3638126
-    ],
-    "osm_type":"relation",
-    "osm_id":403146,
-    "attractiveness":14,
-    "must_do":false,
-    "n_tags":13,
-    "time_to_reach":0
-  },
-  "5":{
-    "name":"Hôtel de la Monnaie",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.856403,
-      2.3388625
-    ],
-    "osm_type":"relation",
-    "osm_id":967664,
-    "attractiveness":11,
-    "must_do":false,
-    "n_tags":11,
-    "time_to_reach":0
-  },
-  "6":{
-    "name":"Musée Bourdelle",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8432078,
-      2.3186583
-    ],
-    "osm_type":"relation",
-    "osm_id":1212876,
-    "attractiveness":23,
-    "must_do":false,
-    "n_tags":23,
-    "time_to_reach":0
-  },
-  "7":{
-    "name":"Musée Carnavalet",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8576819,
-      2.3627147
-    ],
-    "osm_type":"relation",
-    "osm_id":2405955,
-    "attractiveness":25,
-    "must_do":false,
-    "n_tags":25,
-    "time_to_reach":0
-  },
-  "8":{
-    "name":"Pont Neuf",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8565248,
-      2.3408132
-    ],
-    "osm_type":"way",
-    "osm_id":53574149,
-    "attractiveness":16,
-    "must_do":false,
-    "n_tags":15,
-    "time_to_reach":0
-  },
-  "9":{
-    "name":"Jardin du Luxembourg",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8467137,
-      2.3363649
-    ],
-    "osm_type":"way",
-    "osm_id":128206209,
-    "attractiveness":35,
-    "must_do":false,
-    "n_tags":23,
-    "time_to_reach":0
-  },
-  "10":{
-    "name":"Cathédrale Notre-Dame de Paris",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8529372,
-      2.3498701
-    ],
-    "osm_type":"way",
-    "osm_id":201611261,
-    "attractiveness":55,
-    "must_do":false,
-    "n_tags":54,
-    "time_to_reach":0
-  },
-  "11":{
-    "name":"Maison à l'enseigne du Faucheur",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8558553,
-      2.3568266
-    ],
-    "osm_type":"way",
-    "osm_id":1123456865,
-    "attractiveness":13,
-    "must_do":false,
-    "n_tags":11,
-    "time_to_reach":0
-  },
-  "12":{
-    "name":"Maison à l'enseigne du Mouton",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8558431,
-      2.3568914
-    ],
-    "osm_type":"way",
-    "osm_id":1123456866,
-    "attractiveness":13,
-    "must_do":false,
-    "n_tags":11,
-    "time_to_reach":0
-  },
-  "13":{
-    "name":"Palais de Justice de Paris",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8556537,
-      2.3446072
-    ],
-    "osm_type":"relation",
-    "osm_id":536982,
-    "attractiveness":24,
-    "must_do":false,
-    "n_tags":24,
-    "time_to_reach":0
-  },
-  "14":{
-    "name":"Mémorial des Martyrs de la Déportation",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8517365,
-      2.3524734
-    ],
-    "osm_type":"relation",
-    "osm_id":9396191,
-    "attractiveness":21,
-    "must_do":false,
-    "n_tags":21,
-    "time_to_reach":0
-  },
-  "15":{
-    "name":"Fontaine des Innocents",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8606368,
-      2.3480233
-    ],
-    "osm_type":"way",
-    "osm_id":52469222,
-    "attractiveness":16,
-    "must_do":false,
-    "n_tags":15,
-    "time_to_reach":0
-  },
-  "16":{
-    "name":"Hôtel de Sens",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8535257,
-      2.3588733
-    ],
-    "osm_type":"way",
-    "osm_id":55541122,
-    "attractiveness":21,
-    "must_do":false,
-    "n_tags":20,
-    "time_to_reach":0
-  },
-  "17":{
-    "name":"Hôtel d'Ourscamp",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8555465,
-      2.3571206
-    ],
-    "osm_type":"way",
-    "osm_id":55620201,
-    "attractiveness":10,
-    "must_do":false,
-    "n_tags":9,
-    "time_to_reach":0
-  },
-  "18":{
-    "name":"Hôtel de Choiseul-Praslin",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8478008,
-      2.3203873
-    ],
-    "osm_type":"way",
-    "osm_id":65756922,
-    "attractiveness":12,
-    "must_do":false,
-    "n_tags":12,
-    "time_to_reach":0
-  },
-  "19":{
-    "name":"Chapelle Notre-Dame-des-Anges",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8462836,
-      2.3235558
-    ],
-    "osm_type":"way",
-    "osm_id":219378497,
-    "attractiveness":16,
-    "must_do":false,
-    "n_tags":16,
-    "time_to_reach":0
-  },
-  "20":{
-    "name":"Fontaine du Palmier",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8575005,
-      2.3472864
-    ],
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\ No newline at end of file