Abstract
In a congested large-scale subway network, the distribution of passenger flow in space-time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection (AFC) data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s path choice with multisource data, in which the impact of the network time-varying state (e.g., path travel time) on passenger path choice is fully considered. First, according to the number and characteristics of OD (origin-destination) candidate paths, the AFC data among special kinds of OD are selected to estimate the distribution of passengers’ walking time and waiting time of each platform. Then, based on the composition of path travel time, its real-time probability distribution is formulated with the distribution of walking time, waiting time, and in-vehicle time as parameters. Finally, a membership function is introduced to evaluate the dependence between passenger’s travel time and the real-time travel time distribution of each candidate path and take the path with the largest membership degree as passenger’s choice. Finally, a case study with Beijing Subway data is applied to verify the effectiveness of the model presented in this study. We have compared and analysed the path calculation results in which the time-varying characteristics of network state are considered or not. The results indicate that a passenger’s path choice behavior is affected by the network time-varying state, and our model can quantify the time-varying state and its impact on passenger path choice.
Highlights
To alleviate the pressure on urban public transport caused by the increasing demand for urban travel, more and more cities have built large-scale subway networks, such as Shanghai, Beijing, Paris, Tokyo, especially in China, about 20 cities with subway operation mileage of more than 100 kilometers
We propose a probability model to infer passenger path choice in which the time-vary characteristics of network state are explicitly considered. e model takes automated fare collection (AFC), train timetable, and network topology data as input parameters and can provide a number of network performance indicators including passenger path choice, waiting time distribution, path travel time probability distribution
We propose a method to estimate the real-time travel time probability distribution of path based on multisource data
Summary
To alleviate the pressure on urban public transport caused by the increasing demand for urban travel, more and more cities have built large-scale subway networks, such as Shanghai, Beijing, Paris, Tokyo, especially in China, about 20 cities with subway operation mileage of more than 100 kilometers. While the subway brings convenient travel services to passengers [8], the expansion of the network and the influx of passenger flow have brought new problems to the subway operation, such as crowding [9, 10], train utilization efficiency [11], ticket revenue allocation among operators [12, 13], and the uncertainty of passenger flow distribution caused by the diversity of passenger path choice. To improve service quality and operation efficiency, operators urgently need to know the distribution of passenger flow in time and space dimensions [14]. In order to improve passengers’ travel experience, the subway operation service usually adopts “seamless transfer mode”; that is, passengers do not need to tap-out/tap-in when transferring
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
