Space-Time Coordinating Optimization Theory And Method Of Urban Multilevel Transit Network Based On The Travel Behavior Of Residents

With the steady development of our national economy, the individual income goes up significantly, the speed of urbanism and mechanization get higher and the car ownership increase gradually. Even though the urban road is continuously constructed and broadened, the increasing speed of traffic supply is far lower than demand, thus the traffic congestion grows seriously day by day. In addition, the traffic congestion will cause the energy consumption, traffic pollution, traffic accident and so on. Being faced with the pressure from population, energy, environment and safety, prior development transit system can remit the growing of travel demand, improve the traffic condition and convert the development modal of city to land-saving, energy-saving and material-saving.In order to address the issues in public transportation, this paper conducted a space-time coordinating optimization of urban transit network in terms of public transit network, frequency and timetable, and tried to make the layout of multilevel transit network more rational, and frequency and timetable more satisfying. The research coupled residents’ trip demands based on time and space, which made the trip convenient and traffic supply and demand balanced.(1) A model of residents’trip in large-scale transit network with the combination of multi-agent and activity-based model was established, which included the construction of simulation environment factors, attributes of multi-agent, definition of behavior rule, and modeling of multi-agent learning and self-adaptive mechanism. Herein, city of Baoding, which is located on Hebei Province, China, was used in the study. The prediction restuls from multi-agent microscopic simulation are compared with the observed data and the comparison results indicate that the prediction accuracy is applicable. The study focused on the construction of simulation scene and method of preparing the input data.(2) Public transit network hierarchy optimization model based on the residential transit trip distance. To the problem of being lack of transit network hierarchy theory, a research on public transit network hierarchy optimization based on the residential transit trip distance was conducted. From the view of balance between supply and demand, the supply turnover model and demand turnover model were both developed. The method and models were applied to transit network hierarchy optimization of Baoding. (3) A space-time coordinating optimization model of public transit was developed based on the residents’travel behavior and characteristic, meanwhile, the corresponding heuristic algorithm was proposed to solve the model. The space optimization was conducted in the Stage1. The set of candidate lines was generated with the objective of maximum direct flow, and then the set of candidate network was randomly combined. The MNL model and BP neural network were both used to study the influence of transit network layout on residents’travel behavior and the outcome would be employed into the next optimization stage. The time optimization was conducted based on the space optimization in the Stage2. The bi-level model was employed to describe the dynamic relationship between residents’travel behavior and time optimization, in which, the residents’travel behavior was simulated based on the multi-agent technology. The optimized schedule of each candidate network was put out in this stage. The final space-time schedule of public transit was determined in Stage3. With the optimal schedule of each candidate network, the direct flow of each candidate network was calculated and the candidate network with the highest direct flow was chosen as the optimal network, thus the optimal network and schedule were both found. Finally, the optimization model and algorithm were both applied to the Baoding’s transit system.(4) Evaluation method of public transit was proposed and the evaluation system of public transit was developed based on the micro-simulation. With the core of passengers and based on the features of micro-simulation results, the evaluation index system was established from three levels:station-level, line-level and network level. In addition, the AHP, K-mean Cluster Analysis and Gray Correlation Method were used for comprehensive evaluation. Based on the evaluation methods, the evaluation system was developed with the GIS technology and thus the evaluation work would be much more ordered and efficient. The evaluation system was applied to evaluating the current situation and optimized results of Baoding’s public transit system.