update demo and others

README.jp.md

@@ -26,7 +26,7 @@ UXsimは単純，軽量，柔軟であるため，研究・教育上の目的に
 - シミュレーション結果の基本的な分析と`pandas.DataFrame`，CSVへのエクスポート
 - `Matplotlib`を使用したシミュレーション結果の可視化．インタラクティブなGUIも利用可能
 - 純Pythonであることを活かした高度なカスタマイズ性．`PyTorch`を用いた深層強化学習による交通制御など，他のPythonベースのフレームワークとも直接連携可能
-- メインコードの`uxsim.py`は約1800行のコードのみ．ユーザーが簡単に理解しカスタマイズできます
+- メインコードの`uxsim.py`は約2300行のコードのみ．ユーザーが簡単に理解しカスタマイズできます
 
 ## 計算例
 

README.md

@@ -27,7 +27,7 @@ If you are interested, please see:
 - Basic analysis of simulation results and their export to `pandas.DataFrame` and CSV files
 - Visualization of simulation results using `Matplotlib`; interactive GUI is also available
 - Flexible and customizable thanks to pure Python implementation; can also be directly integrated with other Python-based frameworks, such as `PyTorch` for deep reinforcement learning traffic control
-- The main code `uxsim.py` is only about 1800 lines of code. Users may easily understand and customize it
+- The main code `uxsim.py` is only about 2300 lines of code. Users may easily understand and customize it
 
 ## Simulation Examples
 

tests/test_other_functions.py

@@ -1143,4 +1143,43 @@ def test_route_enforce_route():
 
     df = W.analyzer.link_to_pandas()
     for l in r2:
-        assert df[df["link"]==l.name]["traffic_volume"].values[0] == 2130
+        assert df[df["link"]==l.name]["traffic_volume"].values[0] == 2130
+
+def test_construct_time_space_network():
+    W = World(
+        name="",
+        deltan=20,
+        tmax=6000,
+        print_mode=1, save_mode=1, show_mode=1,
+        vehicle_logging_timestep_interval=1, 
+        hard_deterministic_mode=False,
+        random_seed=42    #fix seed to reproduce random demand 
+    )
+
+    W.addNode("1", 0, 1)
+    W.addNode("2", 1, 1)
+    W.addNode("3", 5, 1)
+    W.addNode("4", 0, 0)
+    W.addNode("5", 1, 0)
+    W.addNode("6", 5, 0)
+    W.addNode("7", 6, 0.5)
+
+    W.addLink("highway12", "1", "2", length=1000, number_of_lanes=1, merge_priority=1)
+    W.addLink("highway23", "2", "3", length=3000, number_of_lanes=1, merge_priority=1, capacity_out=0.6)
+    W.addLink("highway37", "3", "7", length=1000, number_of_lanes=1, merge_priority=1)
+    W.addLink("onramp", "5", "2", length=1000, number_of_lanes=1, merge_priority=0.5)
+    W.addLink("arterial45", "4", "5", length=1000, free_flow_speed=10, number_of_lanes=2, merge_priority=0.5)
+    W.addLink("arterial56", "5", "6", length=3000, free_flow_speed=10, number_of_lanes=2, merge_priority=0.5)
+    W.addLink("arterial67", "6", "7", length=1000, free_flow_speed=10, number_of_lanes=2, merge_priority=0.5)
+
+    W.adddemand("1", "7", 0, 3000, 0.3)
+    W.adddemand("4", "7", 0, 3000, 0.4*3)
+
+    W.exec_simulation()
+
+    W.analyzer.print_simple_stats()
+
+    construct_time_space_network(W)
+
+    assert W.TSN_paths["4", 0]["7", "end"][-2] == ('7', 320)
+    assert equal_tolerance(W.TSN_costs["4", 0]["7", "end"], W.TSN_paths["4", 0]["7", "end"][-2][1])

tests/test_verification_dta_solvers.py

@@ -98,9 +98,9 @@ def create_World():
     solver_DSO.plot_link_stats()
     solver_DSO.plot_vehicle_stats(orig="4", dest="7")
 
-    solver_DSO.plot_convergence()
-    solver_DSO.plot_link_stats()
-    solver_DSO.plot_vehicle_stats(orig="4", dest="7")
+    solver_DSO_GA.plot_convergence()
+    solver_DSO_GA.plot_link_stats()
+    solver_DSO_GA.plot_vehicle_stats(orig="4", dest="7")
 
     assert W_DUO.analyzer.total_travel_time > W_DUE.analyzer.total_travel_time
     assert W_DUE.analyzer.total_travel_time > W_DSO.analyzer.total_travel_time

uxsim/Utilities/Utilities.py

@@ -162,7 +162,7 @@ def enumerate_k_shortest_routes_on_t(W, source, target, t, k=1, cost_function=la
 
 def enumerate_k_random_routes(W, k):
     """
-    Enumerate k random routes between all node pairs in a network. The shortest path with free flow travel time is always included. This is much faster than `enumerate_k_shortest_routes` and useful for some purposes.
+    Enumerate k random routes between all node pairs in a network. The shortest path with free flow travel time is always included. This is much faster than `enumerate_k_shortest_routes` and could be useful for a plausible choice set generation for route choice problems.
 
     Parameters
     ----------
@@ -328,19 +328,21 @@ def get_shortest_path_instantaneous_travel_time_between_all_nodes_on_t(W, t, ret
 
 def construct_time_space_network(W, dt=None, from_origin_only=True):
     """
-    Construct a time-space network (TSN).
+    Construct a time-space network (TSN) that includes the time-dependent shortest path infomation.
 
     Parameters
     ----------
     W : World
         The World object.
     dt : int, optional
-        The time interval to construct the TSN. Default is None.
+        The time interval to construct the TSN. Default is None, which sets to the simulation timestep.
     from_origin_only : bool, optional
         Whether to compute the shortest path from the origin only. Default is True
 
     Notes
     -----
+        In the default setting, `W.TSN_paths` contains time-dependent shortest paths from the origin nodes to all nodes, for all departure time. The time-dependent link cost is based on the actual travel time. For example, `W.TSN_paths["orig_node_name", 0]` contains the shortest path from the node "orig_node_name" with departure time 0. `W.TSN_paths["orig_node_name", 0]["dest_node_name", "end"]` contains the shortest path from the node "orig_node_name" to "dest_node_name" with departure time 0. The travel time between these nodes on this time can be obtained by `W.TSN_costs["orig_node_name", 0]["dest_node_name", "end"]`.
+
         Not efficient for large networks.
     """
     if dt == None: