Rail Transit Travel Time Distribution and Prediction Based on Automatic Fare Collection Data
- 10.2991/emcs-16.2016.30How to use a DOI?
- Rail transit; Travel time; AFC data; Pedestrian flow; Prediction methodology
With the development of urban rail transit line networking, precisely obtaining the travel time distribution and analyzing its characters has become very important. This paper is based out of Automated Fare Card data to obtain the travel time distributions by analyzing the transaction records from Automated Fare Collection system. Beijing Metro is used as a case study. By choosing typical Origins to Destinations with different travel distances and transfer times, travel time distributions are generated and get fitted to the curves. A linear model is used to describe the travel time with different distance and transfer times. With consideration of the actual travel distance, transfer times, the time period and some other factors, a travel time prediction method is proposed. This proposed measure is used to predict the travel time from the time point when passengers swipe their smartcards at the entry gate to the time point when they reach the exit gate. The outcomes of this research are validated by the actual OD data from the Beijing Metro case.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Kun Ma AU - Jiaxing Wen AU - Qi Wang PY - 2016/01 DA - 2016/01 TI - Rail Transit Travel Time Distribution and Prediction Based on Automatic Fare Collection Data BT - Proceedings of the 2016 International Conference on Education, Management, Computer and Society PB - Atlantis Press SP - 120 EP - 123 SN - 2352-538X UR - https://doi.org/10.2991/emcs-16.2016.30 DO - 10.2991/emcs-16.2016.30 ID - Ma2016/01 ER -