Exploring Multi-source Data-based Urban Public Transportation Networks Collaborative Optimization

Public transportation is an important part of the urban transportation system.Taxis,buses and subways,each with its own characteristics,form the skeleton of urban spatial mobility.However,in the layout of China’s urban public transportation development,bus,taxi and rail transit have certain timing and independence in planning,construction,development,management and other aspects.Especially in the early stage of urban development and construction,the synergistic optimization of multiple transportation modes is not sufficiently considered,and the capacity of public transportation is far from being fully exploited.On the other hand,the vast majority of urban infrastructure construction and urban expansion efforts in China are based on a demand-driven "see-as-you-go" development model,which often causes public transportation planning and construction to lag behind travel demand.This has led to the mismatch between the spatial and temporal patterns of urban residents’ activities,the spatial structure of the city and the layout of public transportation facilities,and the increasingly prominent problem of imbalance and inadequacy between urban transportation supply and residents’ transportation demand.Therefore,it is very important to carefully analyze the service capacity and travel demand characteristics of each public transportation mode,clarify the supply and demand status and the degree of synergy of each mode of urban public transportation,and carry out the synergistic optimization of public transportation system to alleviate traffic congestion,reduce traffic pollution,optimize the travel structure of residents,improve the efficiency of urban mobility,as well as green,low-carbon,energy-saving and emission reduction.Based on the GPS trajectory data of taxi and buses,subway passenger flow data,etc.,this paper analyzes the spatial and temporal variation characteristics of residents’ travel hotspots by using clustering algorithm and statistical related methods and visualization,focuses on the travel demand and service capacity of urban public transportation,uses complex network theory methods,and investigates the multilayer coupled network topology of different transportation modes of taxi,conventional buses and rail transportation in urban transportation The study will investigate the complexity and traffic dynamics characteristics of the multi-layered coupled urban transportation network,explore the key nodes and key passenger channels in the public transportation network,design a multi-layered coupled urban transportation network supplydemand cascade imbalance model,analyze the supply-demand cascade imbalance transmission mechanism of urban public transportation under different supply-demand conditions,and propose a scientific and reliable urban public transportation cooperative optimization scheme.The main work of this paper is as follows:(1)Research on urban public transportation travel demand and service capacity based on multi-source traffic data.Through OD extraction and cluster analysis of taxi GPS track data,the time-varying pattern and spatial variation characteristics of taxi trips are explored;through line extraction of bus GPS track data,bus 131 in Lanzhou City is used as the research object to analyze the operation status of the line from four aspects: travel time,departure frequency,inter-station running time and stopping time;through metro passenger flow data,Lanzhou Metro Line 1 is used as the research object to analyze the passenger flow characteristics of metro stations from two aspects: full-line passenger flow and sectional passenger flow.By using the metro passenger flow data,we analyzed the passenger flow characteristics of metro stations from two aspects: the whole line passenger flow and cross-sectional passenger flow.(2)Research on the complexity of urban public transportation networks based on complex network theory methods.By using flow space theory and clustering algorithm to construct taxi passenger network and L-space method to construct bus network,we identify and analyze network topology characteristics,urban spatial flow pattern,taxi hotspot passenger carrying area and important bus stops with the help of multiple topological indicators of complex networks and network robustness simulation experiments.(3)Research on optimal passenger flow channels in urban public transportation networks based on optimal transport theory.Based on the spatial location,we construct a multi-layer coupled complex network of taxi-bus-subway,complete the loading of actual multi-source and multi-sink heterogeneous passenger flows based on traffic data,and use the multi-layer optimal transportation algorithm to measure the optimal passenger flow channels in urban public transportation networks.(4)Research on the mechanism of supply-demand cascading failure in urban public transportation networks and its transmission relationship based on the capacity-load cascading failure model.The scientific proposition of cascading imbalance between supply and demand of taxi passenger service is proposed for the first time,and the cascading imbalance model is constructed and relied on the real network and passenger flow data to study the imbalance pattern of different areas of taxi under different passenger flow conditions.We construct and simulate a multilayer public transportation network supply and demand cascading imbalance model,measure the network state change and imbalance scale from the macroscopic level,observe the imbalance transmission links and transmission levels from the microscopic level,and explain the vulnerability characteristics of the taxi network under low node capacity,reveal the "butterfly effect" and "buildup effect" of the urban public transportation network supply and demand cascading imbalance.In order to achieve the accurate identification,key monitoring and key protection of important nodes,important passenger flow channels and important related relationships.