Abstract
The service quality of public transit, such as comfort and convenience, is an important factor influencing ridership and fare revenue, which also reflects the passengers’ perception to the transit performance. Passengers are frustrated while waiting to board a crowded train especially during the peak hours, while the fail-to-board (FtB) situation commonly exists. The service performance measures determined by deterministic passenger demand and service frequency cannot reflect the perceived service of passengers. With the automatic fare collection system data provided by Chengdu Metro, we develop a data-driven approach considering the joint probability of spatiotemporal passenger demand at stations based on posted train schedule to approximate passenger travel time (e.g., in-vehicle and out-of-vehicle times). It was found that the estimated wait time can reflect the actual situation as passengers FtB. The proposed modeling approach and analysis results would be useful and beneficial for transit providers to improve system performance and service planning.
Highlights
Urban rail rapid transit, called metro, plays an important role to transport a vast number of passengers, especially in the peak period
From transit users’ perspective, the passenger load affects the comfort during their travel, which varies over space and time in a network. e passenger load on a transit vehicle affects the comfort of the on-board vehicle portion of a transit trip in terms of both being able to find a seat and in overall crowding levels within the vehicle, represented by average space per standee and load factor
With the automatic fare collection system (AFCS) data, the spatiotemporal passenger entry and exit distributions at metro stations can be determined. is study presents a data-driven approach to estimate the probabilities of passenger itineraries considering FtB for a congested metro network. is information is critical for estimating in-vehicle and out-of-vehicle travel times as well as spatiotemporal passenger loads in different details. is information would be extremely useful in justifying and/or optimizing service planning to elevate metro’s quality of service (QoS)
Summary
Called metro, plays an important role to transport a vast number of passengers, especially in the peak period. Passenger load, service reliability, and travel time are such indices associated with comfort and convenience. From transit users’ perspective, the passenger load affects the comfort during their travel, which varies over space and time in a network. From transit operators’ perspective on the other hand, the variation of passenger load may affect the system performance such that service frequency or number of cars per train need to justify for easing congestion and improving comfort [1]. Passenger flows entering and exiting metro stations can be analyzed with the AFCS transaction records, while wait time can be approximated in conjunction with the train schedule. E objective of this study is to develop a data-driven approach applied for approximating passenger itinerary, spatiotemporal passenger load (e.g., average space per standee and load factor), and travel time with the AFCS data considering station layout, train schedule, and the probability of FtB.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
