def calCrowdcarCost(crowd_route_list, model): cost_of_distance = 0 crowd_wait_time = 0 model.crowdcarcost = 0 for route in crowd_route_list: timetable = [] for i in range(len(route)): if i == 0: depot_id = route[i] next_node_id = route[i+1] cost_of_distance = model.distance_matrix[depot_id, next_node_id] travel_time = int(model.distance_matrix[depot_id, next_node_id]) departure = max(0, model.demand_dict[next_node_id].start_time - travel_time) if departure == 0: crowd_wait_time = travel_time - model.demand_dict[next_node_id].start_time timetable.append((departure, departure)) elif 1 <= i <= len(route)-2: last_node_id = route[i-1] current_node_id = route[i] current_node = model.demand_dict[current_node_id] travel_time = int(model.distance_matrix[last_node_id, current_node_id]) cost_of_distance += model.distance_matrix[last_node_id, current_node_id] arrival = max(timetable[-1][1]+travel_time, current_node.start_time) if arrival == timetable[-1][1]+travel_time: crowd_wait_time += timetable[-1][1]+travel_time - current_node.start_time else: break model.crowdcarcost += model.fixcost + model.percost * cost_of_distance return model.crowdcarcost, crowd_wait_time 在这一段代码中我想知道每个点的等待时间总和，等待时间等于到达这个的时间减去它的时间窗上限

在这段代码中，你已经计算了每个点的等待时间，它们被累加到了`crowd_wait_time`变量中。如果你想得到每个点的等待时间总和，可以在循环中添加一个变量来记录。以下是修改后的代码：

def calCrowdcarCost(crowd_route_list, model):
    cost_of_distance = 0
    crowd_wait_time = 0
    point_wait_time = {}  # 记录每个点的等待时间
    model.crowdcarcost = 0
    
    for route in crowd_route_list:
        timetable = []
        for i in range(len(route)):
            if i == 0:
                depot_id = route[i]
                next_node_id = route[i+1]
                cost_of_distance = model.distance_matrix[depot_id, next_node_id]
                travel_time = int(model.distance_matrix[depot_id, next_node_id])
                departure = max(0, model.demand_dict[next_node_id].start_time - travel_time)
                if departure == 0:
                    crowd_wait_time = travel_time - model.demand_dict[next_node_id].start_time
                    point_wait_time[next_node_id] = travel_time - model.demand_dict[next_node_id].start_time
                timetable.append((departure, departure))
            elif 1 <= i <= len(route)-2:
                last_node_id = route[i-1]
                current_node_id = route[i]
                current_node = model.demand_dict[current_node_id]
                travel_time = int(model.distance_matrix[last_node_id, current_node_id])
                cost_of_distance += model.distance_matrix[last_node_id, current_node_id]
                arrival = max(timetable[-1][1]+travel_time, current_node.start_time)
                if arrival == timetable[-1][1]+travel_time:
                    wait_time = timetable[-1][1]+travel_time - current_node.start_time
                    crowd_wait_time += wait_time
                    point_wait_time[current_node_id] = wait_time
                else:
                    break
    
    model.crowdcarcost += model.fixcost + model.percost * cost_of_distance
    
    return model.crowdcarcost, crowd_wait_time, point_wait_time

这样，`point_wait_time`字典中的每个键值对代表一个点的等待时间，其中键为点的ID，值为该点的等待时间。