The urban public transportation system is an important part of urban transportation, and the rationality of public transportation routes layout plays a vital role in the transportation of the city. Improving the efficiency of public transportation can have a positive impact on the operation of the public transportation system. This paper uses complex network theory and the symmetry of the up and down bus routes and stations to establish an urban public transit network model and calculates the probability of passengers choosing different routes in the public transit network according to passenger travel impedance. Based on passenger travel impedance, travel path probability and passenger travel demand, the links are weighed, and the network efficiency calculation method is improved. Finally, the public transit network optimization model was established with network efficiency as the objective function and solved by the ant colony algorithm. In order to verify the effectiveness of the model and the solution method, this paper selects areas in Nanguan District of Changchun City for example analysis. The result shows that the efficiency of the optimized network is 8.5% higher than that of the original network, which proves the feasibility of the optimized model and solution method.

Aiming at the problem of urban traffic congestion in morning and evening rush hours, taking commuter carpool path planning as the research object, the spatial correlation of traffic flow at adjacent intersections is mined using convolutional neural networks (CNN), and the temporal features of traffic flow are mined using long short-term memory (LSTM) model. The extracted temporal and spatial features are fused to achieve short-term prediction. Considering the travel willingness of drivers and passengers, a multi-objective optimization model with minimum driver and passenger loss time and total travel time is established under the constraints of vehicle capacity, time windows and detour distances. An Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II) is proposed to solve it. The open-loop saving algorithm is used to generate an initial population with better quality, and the 2-opt local search strategy is adopted in the mutation operation to improve search efficiency. The influence of vehicle speed on the matching scheme is analyzed. The research results show that under the same demand conditions, the total travel distance of the carpool scheme is reduced by about 56.19% and total travel time is reduced by about 65.52% compared with the non-carpool scheme. Research on carpool matching under time-varying road networks will help with urban commuting efficiency and environmental quality, and play a positive role in alleviating traffic congestion and promoting carpool services.

Nowadays, bus lines commonly share terminals, especially in urban areas, because an independent operation of each line would cause a large waste of resources. In addition, if lines share terminal all lines can be monitored by unified management, while the vehicles of different sizes can complement each other. Although the dispatcher generally sets up fixed departure intervals for bus lines, there are differences between bus lines in terms of operating time distribution and line volume. Thus, a new optimization model for multi‐vehicle‐type structure in urban bus system is proposed in this paper. The optimization was conducted according to two main characteristics of bus systems, bus company expense and bus service quality. The optimization model was designed based on minimal possible expense and maximal possible quality. At the same time, we proposed a Markov method based on alternative relations to solve the model. The proposed model was verified by real urban bus system. Moreover, the simulations in MATLAB software environment have proven a high efficiency and applicability of the proposed method.

With the continuous progress of urbanization, the phenomenon of urban suburbanization and the separation of work and housing is becoming increasingly obvious. Due to the low passenger flow density in the suburbs and the poor operation effect of conventional public transportation, there is an urgent need to find a rail transit bus operation mode that is suitable for low passenger density areas such as urban suburbs. Demand Responsive Connector (DRC) is a transportation mode that adopts "online booking and offline travel" to meet the needs of passengers. This paper will focus on the DRC route optimization problem under the simultaneous pick-up mode. This paper analyses the DRC path optimization problem under the single transfer point simultaneous pick-up mode, defines the research situation, proposes the modelling idea, considers the balance of the interests of both passengers and enterprises, and establishes the DRC path optimization model under the single transfer point simultaneous pick-up mode with the goal of maximum passenger satisfaction and minimum enterprise operating cost. According to the characteristics of the model, a multichain chromosome genetic algorithm is designed to solve the model.

In light of the traffic congestion and traffic environment problems around schools that are caused by students commuting by car, this paper explores an efficient and feasible student commuting travel plan. Based on the ideas of “public–private cooperation” and “parking sharing”, combined with the characteristics of the family travel chain during the commuting period, a joint-commuting model of “private car and school bus” is creatively proposed. On the basis of considering the travel cost of parents and the operating cost of school bus, a two-phase commuting travel model for primary and secondary schools is proposed, and an algorithm is designed. The validity of the model is verified by an example and sensitivity analysis. The results show that the total time cost can be reduced by 23.33% when the private-car commuting mode is converted to the joint-commuting model. Among the results, we found that the driving time of a private car in the school commuting phase can be reduced by 23.36%, the dwell time can be reduced by 92.29%, and the driving time in the work and home phase can be reduced by 7.44%. Compared with the school-bus commuting mode, the school-bus time cost of joint commuting can be reduced by 54.88%. In addition, by analyzing the impact of various factors on the objective function and vehicle emissions, it can be seen that staggered commuting to school, regulating regional traffic volume, increasing parking spaces, and improving the utilization of parking spaces can effectively reduce the operating time cost of vehicles and exhaust emissions. The joint-commuting model proposed in this paper considers the balance between service level and resource consumption. While meeting the door-to-door travel needs of students, it can effectively reduce the travel costs of parents and school-bus operation costs, and it can alleviate traffic congestion around schools and reduce the impact on the environment.