Study Queuing At Bus Stop Based On Bus Trajectory Data

Bus stop as an important node of bus network is one of the key factors of the public transport system operation efficiently,the phenomenon of queuing at bus stops not only directly affects the waiting time of passengers,but also directly impacts the service level of public transport.Therefore,bus stops queuing phenomenon research for passengers and the bus operation managers has vital significance.This paper mainly studied queuing identification,queuing influencing factors analysis and queuing prediction at bus stop from the point of view of bus trajectory data.First of all,on the basis of summarizing the existing literature,this paper proposed the key issues of queuing research based on bus AVL trajectory data.From the perspective of the characteristics of Chengdu bus AVL operation trajectory data and research needs,it put forward the corresponding bus trajectory data preprocessing steps.Secondly,the definition of bus queuing at stop was given by combining the phenomenon of bus queuing and the spatio-temporal trajectory graph of bus queuing.this paper proposed the identification steps and methods of the vehicle stop process and queuing process based on the values and changes of the two fields ‘GPSSPEED’ and‘GPSMILE’ in AVL data corresponding to the vehicle stop process,the queuing time and queuing length were estimated based on one week data of some stops on Qingjiang East Road in Chengdu;The VISSIM simulation experiments were used to verify the feasibility of the above bus queuing identification steps and methods,The VISSIM simulation results indicated that the method could identify about 95% of the vehicle stop process and accurately identify about 93% of the queuing process at bus stop;The paper leveraged multiple linear regression method to quantitatively analyze the effects of multiple factors on the number of vehicles queuing at the stop.The results showed that the number of vehicles arriving at the stop,peak time period,the fluctuation of the travel time between the stops had significant effects on the number of vehicles queuing at the stop.Thirdly,the prediction model of bus arrival time was established by using support vector regression machine and neural network algorithm.On the basis of the prediction of arrival time,the relationship of the arrival and departure time for adjacent vehicles were combined to predict and judge the queuing of vehicles at the stop.A case study of four adjacent stops on Qingjiang East Road in Chengdu was carried out.The results showed that the proportion of queuing events correctly identified by the two models in flat peak period and peak period was about 70% and 80%,and the proportion of nonqueuing events was about 90% and 80%,respectively.The misjudgment rate of queuing events predicted by the two models in flat peak period was about 8%,while the misjudgment rate in peak period was about 15%.Finaly,on the basis of the related theoretical research above,the paper proposed miligation measures for queing at bus stop from two aspects of optimal design of the stop and intersection signal control,and the VISSIM simulation experiments results indentified that the bottleneck stops implemented corresponding measures could relieve queuing congestion phenomenon to a certain extent.