area_to_pandas: Correctly calculate density and average speed

It's now weighted with the link length.

uxsim/analyzer.py

@@ -1491,8 +1491,14 @@ def area_to_pandas(s, areas, area_names=None, border_include=True):
                 total_travel_time = np.sum(df_links.loc[rows, "average_travel_time"].values * traffic_volume_rows)
                 total_free_time = np.sum(df_links.loc[rows, "free_travel_time"].values * traffic_volume_rows)
                 average_delay = max(total_travel_time / total_free_time - 1, 0)
-                average_speed = df_links.loc[rows, "average_travel_time"].mean()
-                vehicle_density = traffic_volume / df_links.loc[rows, "link"].nunique()
+
+                # Average speed calculation: total distance / total time
+                total_distance = np.sum(df_links.loc[rows, "length"].values * traffic_volume_rows)
+                average_speed = total_distance / total_travel_time if total_travel_time > 0 else np.nan
+
+                # Vehicle density calculation: total number of vehicles / total link length
+                total_link_length = df_links.loc[rows, "length"].sum()
+                vehicle_density = traffic_volume / total_link_length if total_link_length > 0 else np.nan
             else:
                 total_travel_time = 0
                 total_free_time = 0
@@ -1651,9 +1657,9 @@ def link_to_pandas(s):
         """
         s.link_analysis_coarse()
 
-        out = [["link", "start_node", "end_node", "traffic_volume", "vehicles_remain", "free_travel_time", "average_travel_time", "stddiv_travel_time"]]
+        out = [["link", "start_node", "end_node", "traffic_volume", "vehicles_remain", "free_travel_time", "average_travel_time", "stddiv_travel_time", "length"]]
         for l in s.W.LINKS:
-            out.append([l.name, l.start_node.name, l.end_node.name, s.linkc_volume[l], s.linkc_remain[l], s.linkc_tt_free[l], s.linkc_tt_ave[l], s.linkc_tt_std[l]])
+            out.append([l.name, l.start_node.name, l.end_node.name, s.linkc_volume[l], s.linkc_remain[l], s.linkc_tt_free[l], s.linkc_tt_ave[l], s.linkc_tt_std[l], l.length])
         s.df_linkc = pd.DataFrame(out[1:], columns=out[0])
         return s.df_linkc