Operational Optimization Strategies Of Bus Transit Under Real-time Supply And Damand Information

With the development of the new generation of information technology,such as Internetplus,big data and cloud computing,the bus information service system has gradually moved from providing supply information only to a two-way interaction between supply and demand information.At present,the supply information(e.g.,vehicle location,arrival time and invehicle crowding)and the demand information(e.g.,passenger O-D and departure time)can realize real-time two-way communication.Passengers can obtain more accurate bus arrival time and more comprehensive vehicle status(such as crowding)through the supply information,and reasonably choose the travel plan according to their schedules and personal preferences;Operators can make route planning and vehicle scheduling more scientifically through the demand information,and even explore new service modes such as customized bus and flexible transit.Totally speaking,real-time supply and demand information can make the supply and demand match more accurately,which brings new development opportunities for improving the level of service(LOS)of bus transit and passenger travel experience.Considering current technical background and the innovation trend of bus service,this study aims to improve the LOS and passenger travel experience of bus transit under the condition of real-time supply and demand information.The operational optimization strategies under real-time supply and demand information are proposed from the perspectives of passenger comfort(for high-frequency transit),vehicle punctuality(for low-frequency transit),and service accessibility(for responsive feeder transit).The main research work of this paper is as follows.(1)A release method of real-time crowding information based on bus passenger load prediction.In order to provide accurate real-time crowding information(RCI),a two-stage method for bus passenger load prediction using automatic passenger counting data is proposed.In the first stage,adaptive Kalman filter is used to predict(boarding,alighting and section)passenger flows at station level.In the second stage,support vector regression is used to predict the passenger load at vehicle level.Then,based on the predicted passenger load,a release policy of RCI which contains a reduction factor is proposed.The reduction factor makes the release of low-level crowding information more cautious.Using a case study based on Suzhou No.1bus line,the effectiveness of the two-stage passenger load prediction method and the information release policy is tested.The results show that the two-stage method is superior to the existing prediction models,especially suitable for the boarding dominated segment prediction and multi-step ahead prediction.The RCI release policy based on the predicted passenger load can effectively improve the crowding prediction ability.Considering the reduction factor,the accuracy of crwoding prediction decreases slightly,but it can obtain higher passenger gain,which means there is less inconsistency between the released information and the actual situation.(2)A self-control strategy for high-frequency transit based on provision of real-time crowding information.This study proposes a self-control strategy for high-frequency transit,which is providing real-time crowding information for passengers(noted as RCI strategy).In order to test the effect of RCI strategy,a bus motion model under RCI is established,which considers multiple random factors such as demand intensity,passenger behavior,and vehicle running time on links.Through the simulation experiments of multi scenario,multi demand intensity and multi strategy combination,the effect of RCI strategy on the operation stability and passenger travel experience is evaluated in detail.The simulation results show that RCI strategy can improve the bus operation stability by 20% in each scenario,and reduce the passenger's feeling of crowdness by 25% at most.The improvements are more significant for routes with high departure frequency and unstable running time on links.Sensitivity analysis further shows that the penetration rate of RCI is an important factor affecting the effect of the strategy,but 25% penetration rate can still produce about 60% improvement compared with the ideal situation(100% penetration rate).The case study based on the actual data from Changzhou No.2 line further verifies the effectiveness of RCI strategy when applied to the actual line.(3)Bus arrival time information release strategy and speed control strategy for lowfrequency transit.The concept of "relative punctuality" is proposed for low-frequency transit.The idea of the concept is to make the vehicle operation and the released information of vehicle arrival time consistent.Firstly,a bi-directional Bayesian model for arrival time prediction is established.The model includes two kinds of Bayesian operators,i.e.,vertical and horizontal.The essence of the model is to find the historical trips that are similar to the target trip from the horizontal and vertical perspectives,and then to modify and synthesize the prior probability distribution of the arrival time.Based on the arrival time distribution probability obtained by the bidirectional Bayesian model,an arrival information release strategy is proposed.The strategy discusses arrival information for boarding passengers and alighting passengers separately,and adopts corresponding probability coefficients as the basis for information release.Furthermore,a speed control strategy based on the trigger threshold of deceleration is proposed.A case study from Nanjing No.40 line shows that the bi-directional Bayesian model can effectively predict the arrival time in terms of single value and interval,and the horizontal Bayesian operator has significant advantages when the vehicle approaches the target station.The speed control strategy can improve the arrival information accuracy rate or vehicle punctuality rate by about 5% at a small cost.(4)A passenger-oriented operation strategy for responsive feeder transit based on information interaction.A passenger oriented operation strategy is proposed for the responsive feeder transit systems with meeting points.The strategy consists of an update mechanism of estimated vehicle arrival time and a discrimination mechanism of passenger acceptance,and contains two key parameters,namely arrival time limit index and in-vehicle time limit index,which ensure that the vehicle arrival times at stops are constrained by boarding time or alighting time of passengers respectively.In order to solve the real-time stop selection and route planning problem in the system,a mixed integer programming model is established,and then a meta heuristic algorithm is proposed to solve the problem.The results show that,compared with the traditional operation strategy of flexible transit and the fixed-route line,the passenger-oriented strategy is able to avoid passengers' waiting times caused by late arrival,and effectively reduces the in-vehicle times.It has obvious advantages in the total cost of passengers,and the advantages increase with the passenger demand intensity.Sensitivity analysis shows that the optimal values of arrival time limit index and in-vehicle time limit index are 1.4 and 2.5respectively under the numerical experiment.This study will enrich the research regarding bus operation characteristics and strategies under information,and provide theoretical basis and method guidance for optimizing real-time information release and improving LOS and passenger travel experience of bus transit under real-time supply and demand information.