Research On Evolution And Congestion Charging Of Multi-mode Dual Dynamic Network Traffic Flow Considering Multi-person Ridesharing

With the increase of urban residents’ travel demand and the continuous increase of urban car ownership,urban traffic congestion is becoming increasingly severe.ridesharing is a way of travel in which multiple people share a car.With the maturity of online car-hailing platforms,it is gradually accepted by urban residents.Congestion charging is an economic means to manage traffic demand,the purpose is to use the price mechanism to limit the traffic flow of the urban road network during peak hours.Studying the dynamic traffic evolution process under the scenarios of ridesharing and congestion charging is helpful to explore the internal mechanism of traffic flow evolution,especially to understand the impact of ridesharing and congestion charging on the transportation network,so as to better conduct traffic guidance and traffic flow control to improve the transportation network performance.First,a multi-mode travel transportation network with ridesharing services is constructed,and the process of ridesharing travel modes and the calculation method of travel costs are introduced in detail.On this basis,a dual dynamic traffic evolution model(day-by-day dynamic evolution and intra-day dynamic evolution)was constructed by combining the perceptual update model and the cellular transmission model(CTM)to study the dynamic evolution of traffic flow and the impact of traffic flow on ridesharing.Dynamic changes,the performance evaluation indicators of the transportation network in the model are total travel cost and total travel time.Then,a congestion charging model based on multi-objective optimization and two-level programming is constructed.The upper model takes minimizing the total travel cost and total travel time as the optimization goal,and the collected congestion charge is the decision variable.The lower model is used to calculate the congestion charge.Corresponding to the dynamic evolution of traffic flow,the NSGA-II genetic algorithm is used to solve the optimal congestion charging.Finally,the impact of ridesharing and congestion charging on the transportation network is studied through case analysis.The result is a comparative analysis of the ridesharing no-congestion charging network and the no-ridesharing no-congestion charging network.Ridesharing can reduce the total travel cost of the transportation network by 13.25% and the total travel time by 10.82%.On this basis,add the solved optimal congestion.Tolls,comparative analysis of ridesharing with congestion charging networks and ridesharing without congestion charging networks,congestion charging can reduce the total travel time by 34.40%,but the total travel cost has increased by14.4%.This shows that ridesharing can appropriately alleviate traffic congestion and reduce the cost of transportation.The introduction of congestion charging in a ridesharing network can greatly reduce traffic congestion,but it will increase the cost of transportation.