Concerning the problems of single fare, low revenue rate of passenger transport and unbalanced passenger flow in different sections of high-speed railway, an adjustment strategy of high-speed railway fare based on passenger flow assignment was proposed. Firstly, the related factors affecting passenger travel choice behavior were analyzed, and a generalized travel cost function including four indicators of economy, rapidity, convenience and comfort was constructed. Secondly, a bilevel programming model considering the maximization of revenue of railway passenger transport management department and the minimization of passenger travel cost was established, in which the upper level programming achieved the maximum revenue of high-speed railway passenger transport by formulating fare adjustment strategy, the lower-level programming took the minimum passenger generalized travel cost as the goal, and used the competition and cooperation relationship between different trains of section to construct Stochastic User Equilibrium (SUE) model, and the model was solved by Method of Successive Averages (MSA) based on the improved Logit assignment model. Finally, the case study shows that the proposed fare adjustment strategy can effectively balance the section passenger flow, reduce passenger travel cost and improve passenger transport revenue to a certain extent. The experimental results show that the fare adjustment strategy can provide decision support and methodological guidance for railway passenger transport management departments to optimize fare system and formulate fare adjustment schemes.

For the actual background of the shortage of self-owned vehicles of the transporters in the early stage of emergency, the combinatorial optimization problem of hybrid vehicle paths with transportation mode of joint distribution of self-owned vehicles and vehicles rented by third-party was studied. Firstly, with the different interests between demand points and transporters considered, a multi-objective hybrid vehicle routing optimization model with soft time windows was established with the goal of maximizing system satisfaction and minimizing system delivery time and total cost. Secondly, the shortcomings of NSGA-Ⅱ algorithm in solving this kind of problems such as poor convergence and uneven distribution of Pareto frontiers were considered, the heuristic strategy and pheromone positive feedback mechanism of ant colony algorithm were used to generate offspring population, non-dominated sorting strategy model was used to guide the multi-objective optimization process, and the variable neighborhood descent search was introduced to expand the search space. A multi-objective non-dominated sorting ant colony algorithm was proposed to break through the bottleneck of the original algorithm. The example shows that the proposed model can provide reference for decision makers to choose reasonable paths according to different optimization objectives in different situations, and the proposed algorithm shows better performance in solving different scale problems and different distribution type problems.

In order to solve the problems in the transfer process connection and collaboration of metro-bus two-layered network faced by the passengers making route selection in the urban transportation network, such as the far distance between some transfer stations, the unclear connection orientation and the imbalance between supply and demand in local transfer, a collaborative optimization method for urban traffic networks based on two-layered complex networks was presented. Firstly, the logical network topology method was applied to the topology of the urban transportation network, and the metro-bus two-layered network model was established by the complex network theory. Secondly, with the transfer station as research object, a node importance evaluation method based on K-shell decomposition method and central weight distribution was presented. This method was able to realize coarse and fine-grained divison and identification of metro and bus stations in large-scale networks. And a collaborative optimization method for two-layered urban traffic network with mutual encouragement was presented, that is to say the method in the complex network theory to identify and filter the node importance in network topology was introduced to the two-layered network structure optimization. The two-layered network structure was updated by identifying high-aggregation effects and locating favorable nodes in the route selection to optimize the layout and connection of stations in the existing network. Finally, the method was applied to the Chengdu metro-bus network, the existing network structure was optimized to obtain the optimal optimized node location and number of existing network, and the effectiveness of the method was verified by the relevant index system. The results show that the global efficiency of the network is optimized after 32 optimizations, and the optimization effect of the average shortest path is 15.89% and 16.97%, respectively, and the passenger transfer behavior is increased by 57.44 percentage points, the impact on the accessibility is the most obvious when the travel cost is 8000-12000 m with the optimization effect of 23.44% on average. At the same time, with the two-layered network speed ratio and unit transportation cost introduced, the response and sensitivity difference of the traffic network to the collaborative optimization process under different operational conditions are highlighted.

In order to reduce the waste gas pollution generated by vehicles in the process of logistics distribution, on the basis of traditional Vehicle Routing Problem with Time Windows (VRPTW) model, an approximate calculation method for fuel consumption and carbon emission was introduced from the perspective of energy saving and emission reduction, then a Green Multi-type Vehicles Routing Problem with Time Windows (G-MVRPTW) model was established. The minimum total cost was taken as an optimization objective to find environment-friendly green paths, and an improved tabu search algorithm was designed to solve the problem. When the initial solution and the neighborhood solution were generated, the order of customer sequence in the subpath was set according to the ascending order of the latest service time and the time window size of each customer point. At the same time, through three indexes of the minimum subpath, the total cost of subpaths and the overload, the evaluation function of solution was improved, and a mechanism of reducing the possibility of precocious maturing was adopted. Finally, the effectiveness and feasibility of the proposed model and algorithm were verified by numerical experiments. The experimental results show that, the ton-kilometer index can better measure the fuel consumption and carbon emission cost, and it is a new trend for new energy vehicles to enter the transportation market. It can provide decision support and methodological guidance for low-carbon transportation and management.