Merge pull request #32 from toruseo/develop

Add import from OSM [experimental], new demand setup functions

.github/workflows/run-examples.yml

@@ -19,6 +19,6 @@ jobs:
     - name: Install uxsim and dependencies
       run: pip install .
     - name: Install pytest other dependencies
-      run: pip install pytest setuptools gymnasium torch
+      run: pip install pytest setuptools gymnasium torch osmnx
     - name: Run examples with pytest
       run: pytest demos_and_examples/test_examples.py --durations=0

README.md

@@ -72,10 +72,10 @@ your_project_directory/
 │ ├── utils/ # Utility files of UXsim
 │ ├── uxsim.py # The main code of UXsim. You can customize this as you wish
 │ ├── utils.py # Utility funcsions of UXsim
-│ └── ...
-├── your_simulation_code.py # Your code
-├── your_simulation_notebook.ipynb # Your Jupyter notebook
-├── ...
+│ └── ... # Other files in uxsim
+├── your_simulation_code.py # Your code if nessesary
+├── your_simulation_notebook.ipynb # Your Jupyter notebook if nessesary
+├── ... # Other files if nessesary
 ```
 In this way, you can flexibly customize UXsim by your own.
 
@@ -194,9 +194,15 @@ The right is with DRL control scenario, where traffic signal can be changed by o
 
 ## Future Plans
 
+- multi-lane link
+	- Note: The current model assumes that all links are 1 lane, and the reaction time of all drivers are the same. This means that all links have more or less the same capacity.  It is not possible to model  multi-lane links that have 2 or 3 times the capacity of 1-lane links. This is a theoretical limitation.
+ 	- Workarounds for the current model:
+		- Create multiple links between same node pair; each link corresponds to each lane between the nodes. This is a reasonable solution. However, the analysis becomes tedious.
+		- Reduce `capacity_out` parameter of `Link`. In this way, we can place a bottleneck to the end of a link, so that capacities of links can vary significantly. However, it only reduce the capacity. Also, the jam propagation speed becomes too fast or slow.
 - day-to-day dynamics
 - taxi and shared mobility (i.e., vehicles travel through a network by passing through specific nodes that are dynamically updated)
 - network import from OSMnx
+	- Done, but still experimental.
 - basemap for visualization
 - modern packaging
 
@@ -211,7 +217,7 @@ When publishing works based on from UXsim, please cite:
 
 ## Acknowledgments
 
-UXsim is based on various works in traffic flow theory. We would like to acknowledge the contributions of the transportation research community in advancing the this field.
+UXsim is based on various works in traffic flow theory. We would like to acknowledge the contributions of the research community in advancing this field.
 
 ## Related Links
 

demos_and_examples/example_16_import_from_OpenStreetMap.py

@@ -0,0 +1,33 @@
+from uxsim import *
+
+
+W = World(
+    name="",
+    deltan=5,
+    tmax=7200,
+    print_mode=1, save_mode=1, show_mode=0, 
+    random_seed=0
+)
+
+#Tokyo highway
+nodes, links = OSMImporter.import_osm_data(north=35.817, south=35.570, east=139.881, west=139.583, custom_filter='["highway"~"motorway"]')
+nodes, links = OSMImporter.osm_network_postprocessing(nodes, links, node_merge_threshold=0.005, node_merge_iteration=5, enforce_bidirectional=True)  # merge threshold distance: 0.005 degree ~= 500 m. `enforce_bidirectional` makes all links bidirectional, so that network is not fragmented (but the original network topology is not preserved rigorously).
+OSMImporter.osm_network_visualize(nodes, links, show_link_name=0)
+OSMImporter.osm_network_to_World(W, nodes, links, default_jam_density=0.2, coef_degree_to_meter=111000)
+
+# set demand to central Tokyo from surroundings
+W.adddemand_area2area(139.70, 35.60, 0, 139.75, 35.68, 0.05, 0, 3600, volume=5000)
+W.adddemand_area2area(139.65, 35.70, 0, 139.75, 35.68, 0.05, 0, 3600, volume=5000)
+W.adddemand_area2area(139.75, 35.75, 0, 139.75, 35.68, 0.05, 0, 3600, volume=5000)
+W.adddemand_area2area(139.85, 35.70, 0, 139.75, 35.68, 0.05, 0, 3600, volume=5000)
+
+W.exec_simulation()
+
+W.analyzer.print_simple_stats()
+for t in list(range(0,W.TMAX,int(W.TMAX/6))):
+    W.analyzer.network(t, detailed=0, network_font_size=0, figsize=(6,6))
+
+W.analyzer.network_anim(animation_speed_inverse=15, detailed=0, network_font_size=0)
+W.analyzer.network_fancy(animation_speed_inverse=15, sample_ratio=0.1, interval=10, trace_length=5)
+
+W.analyzer.output_data()