A probabilistic Passenger-to-Train Assignment Model based on automated data

Authors

Yiwen Zhu
Haris N. Koutsopoulos
Nigel H.M. Wilson

Document Type

Journal Article

Publication Date

2017

Subject Area

place - urban, mode - rail, mode - subway/metro, technology - ticketing systems, technology - passenger information, technology - automatic vehicle monitoring, operations - capacity, planning - surveys

Keywords

Automated data, Passenger assignment, Train load, Simulation, Left behind, Walk speed, Crowding

Abstract

The paper presents a methodology for assigning passengers to individual trains using: (i) fare transaction records from Automatic Fare Collection (AFC) systems and (ii) Automatic Vehicle Location (AVL) data from train tracking systems. The proposed Passenger-to-Train Assignment Model (PTAM) is probabilistic and links each fare transaction to a set of feasible train itineraries. The method estimates the probability of the passenger boarding each feasible train, and the probability distribution of the number of trains a passenger is unable to board due to capacity constraints. The access/egress time distributions are important inputs to the model. The paper also suggests a maximum likelihood approach to estimate these distributions from AFC and AVL data. The methodology is applied in a case study with data from a major, congested, subway system during peak hours. Based on actual AFC and train tracking data, synthetic data was generated to validate the model. The results, both in terms of the trains passengers are assigned to and train loads, are similar to the “true” observations from the synthetic data. The probability of a passenger being left behind (due to capacity constraints) in the actual system is also estimated by time of day and compared with survey data collected by the agency at the same station. The left behind probabilities can be accurately estimated from the assignment results. Furthermore, it is shown that the PTAM output can also be used to estimate crowding metrics at transfer stations.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part B Home Page:

http://www.sciencedirect.com/science/journal/01912615

Recommended Citation

Zhu, Y., Koutsopoulos, H.N., & Wilson, N.H.M. (2017). A probabilistic Passenger-to-Train Assignment Model based on automated data. Transportation Research Part B: Methodological, Available online 5 May 2017. In Press, Corrected Proof — Note to users.