add vehicle group analysis

tests/test_other_functions.py

@@ -510,3 +510,60 @@ def test_area():
     assert df["total_trips"][(df["origin_area"] == "areaNW") & (df["destination_area"] == "areaSE")].values[0] == 1750
     assert df["average_free_travel_time"][(df["origin_area"] == "areaNW") & (df["destination_area"] == "areaSE")].values[0] == 400.0
     assert df["average_shortest_distance"][(df["origin_area"] == "areaNW") & (df["destination_area"] == "areaSE")].values[0] == 8000.0
+
+@pytest.mark.flaky(reruns=10)
+def test_vehicle_group():
+    W = World(
+        name="",
+        deltan=10,
+        tmax=4000,
+        print_mode=1, save_mode=1, show_mode=0,
+        random_seed=None,
+    )
+
+    n_nodes = 4
+    imax = n_nodes
+    jmax = n_nodes
+    nodes = {}
+    for i in range(imax):
+        for j in range(jmax):
+            nodes[i,j] = W.addNode(f"n{(i,j)}", i, j, flow_capacity=1.6)
+
+    links = {}
+    for i in range(imax):
+        for j in range(jmax):
+            if i != imax-1:
+                links[i,j,i+1,j] = W.addLink(f"l{(i,j,i+1,j)}", nodes[i,j], nodes[i+1,j], length=1000)
+            if i != 0:
+                links[i,j,i-1,j] = W.addLink(f"l{(i,j,i-1,j)}", nodes[i,j], nodes[i-1,j], length=1000)
+            if j != jmax-1:
+                links[i,j,i,j+1] = W.addLink(f"l{(i,j,i,j+1)}", nodes[i,j], nodes[i,j+1], length=1000)
+            if j != 0:
+                links[i,j,i,j-1] = W.addLink(f"l{(i,j,i,j-1)}", nodes[i,j], nodes[i,j-1], length=1000)
+
+
+    areas = {
+        "areaN": [nodes[0,0], nodes[0, n_nodes-1]],
+        "areaS": [nodes[n_nodes-1,0], nodes[n_nodes-1, n_nodes-1]],
+        "areaNW": [nodes[0,0]],
+        "areaSE": [nodes[n_nodes-1, n_nodes-1]]
+    }
+
+    W.adddemand_areas2areas(areas["areaN"], areas["areaS"], 0, 3000, volume=7000)
+
+    W.exec_simulation()
+    W.analyzer.print_simple_stats()
+
+    dt = 500
+    group_dict = {}
+    for t in range(0, W.TMAX, dt):
+        group_dict[f"depart_t={t}"] = [veh for veh in W.VEHICLES.values() if t <= veh.departure_time_in_second < t+dt]
+
+    df = W.analyzer.vehicle_groups_to_pandas(list(group_dict.values()), list(group_dict.keys()))
+    print(df)
+
+    assert df["average_travel_time"][df["group"]=="depart_t=0"].values[0] < df["average_travel_time"][df["group"]=="depart_t=1500"].values[0]
+    assert df["average_delay_ratio"][df["group"]=="depart_t=0"].values[0] < df["average_delay_ratio"][df["group"]=="depart_t=1500"].values[0]
+    assert df["average_traveled_distance"][df["group"]=="depart_t=0"].values[0] < df["average_traveled_distance"][df["group"]=="depart_t=1500"].values[0]
+    assert df["average_detour_ratio"][df["group"]=="depart_t=0"].values[0] < df["average_detour_ratio"][df["group"]=="depart_t=1500"].values[0]
+    assert df["average_speed"][df["group"]=="depart_t=0"].values[0] > df["average_speed"][df["group"]=="depart_t=1500"].values[0]

uxsim/analyzer.py

@@ -1437,6 +1437,105 @@ def areas2areas_to_pandas(s, areas, area_names=None):
 
         s.df_areas2areas = pd.DataFrame(out[1:], columns=out[0])
         return s.df_areas2areas
+
+    def vehicle_groups_to_pandas(s, groups, group_names=None):
+        """
+        Converts the vehicle group analysis results to a pandas DataFrame.
+
+        Parameters
+        ----------
+        groups : list
+            The list of vehicle groups. Each group is defined as a list of vehicle object.
+        group_names : list, optional
+            The list of names of vehicle groups.
+        
+        Returns
+        -------
+        pd.DataFrame
+        """
+        df_od = s.W.analyzer.od_to_pandas()
+
+        if group_names == None: 
+            group_names = [f"group {i} including {groups[0].name}" for i in range(len(groups))]
+
+        total_trip_rec = []
+        completed_trip_rec = []
+        average_travel_time_rec = []
+        average_delay_rec = []
+        std_delay_rec = []
+        average_traveled_distance_rec = []
+        average_detour_rec = []
+        std_detour_rec = []
+        averae_speed_rec = []
+        std_speed_rec = []
+        for i, group in enumerate(groups):
+            total_trips = 0
+            completed_trips = 0
+            travel_times = []
+            delays = []
+            traveled_distances = []
+            detours = []
+            speeds = []
+
+
+            for veh in group:
+
+                total_trips += 1
+                if veh.state == "end":
+                    completed_trips += 1
+                    travel_times.append(veh.travel_time)
+                    traveled_distances.append(veh.distance_traveled)
+
+                    free_travel_time = df_od["free_travel_time"][(df_od["orig"]==veh.orig.name) & (df_od["dest"]==veh.dest.name)].values[0]
+                    shortest_distance = df_od["shortest_distance"][(df_od["orig"]==veh.orig.name) & (df_od["dest"]==veh.dest.name)].values[0]
+
+                    delays.append(veh.travel_time/free_travel_time)
+                    detours.append(veh.distance_traveled/shortest_distance)
+
+                    speeds.append(veh.distance_traveled/veh.travel_time)
+
+                #print(f"{group_names[i]=}, {np.average(travel_times)=}, {np.average(traveled_distances)=}, {np.average(delays)=}, {np.average(detours)=}, {np.std(delays)=}, {np.std(detours)=}, {np.average(speeds)}, {np.std(speeds)}")
+
+            total_trip_rec.append(total_trips)
+            completed_trip_rec.append(completed_trips)
+            if completed_trips > 0:
+                average_travel_time_rec.append(np.average(travel_times))
+                average_delay_rec.append(np.average(delays))
+                std_delay_rec.append(np.std(delays))
+                average_traveled_distance_rec.append(np.average(traveled_distances))
+                average_detour_rec.append(np.average(detours))
+                std_detour_rec.append(np.std(detours))
+                averae_speed_rec.append(np.average(speeds))
+                std_speed_rec.append(np.std(speeds))
+            else:
+                average_travel_time_rec.append(np.nan)
+                average_delay_rec.append(np.nan)
+                std_delay_rec.append(np.nan)
+                average_traveled_distance_rec.append(np.nan)
+                average_detour_rec.append(np.nan)
+                std_detour_rec.append(np.nan)
+                averae_speed_rec.append(np.nan)
+                std_speed_rec.append(np.nan)
+
+        df = pd.DataFrame({
+            "group": group_names,
+            "total_trips": total_trip_rec,
+            "completed_trips": completed_trip_rec,
+            "average_travel_time": average_travel_time_rec,
+            "average_delay_ratio": average_delay_rec,
+            "std_delay_ratio": std_delay_rec,
+            "average_traveled_distance": average_traveled_distance_rec,
+            "average_detour_ratio": average_detour_rec,
+            "std_detour_ratio": std_detour_rec,
+            "average_speed": averae_speed_rec,
+            "std_speed": std_speed_rec,
+        })
+
+        s.df_vehicle_groups = df
+
+        return s.df_vehicle_groups
+
+
 
 
     def mfd_to_pandas(s, links=None):

uxsim/uxsim.py

@@ -844,6 +844,7 @@ def __init__(s, W, orig, dest, departure_time, name=None, route_pref=None, route
             s.departure_time = departure_time
         else:
             s.departure_time = int(departure_time/s.W.DELTAT)
+        s.departure_time_in_second = departure_time*s.W.DELTAT  #TODO: temporal workaround
         s.arrival_time = -1
         s.link_arrival_time = -1
         s.travel_time = -1