A Novel Method for Passengers' Origin- Destination Distribution Prediction Over Metro Networks With Expansions Based on Automatic Fare Collection Data

• Published in: IEEE access Vol. 12; pp. 137418 - 137426
• Main Author: Liu, Chang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: aggregate model; Analytical models; Automatic fare collection; automatic fare collection data; Automation; Data models; disaggregate model; Disaggregate models; Metro expansions; origin-destination distribution prediction; Passengers; Predictions; Predictive models; Public transportation; Socioeconomics; Soft sensors; Surveys; Urban rail
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3435918

With the development of the metro system, an increasing number of new metro lines are being constructed. However, the lack of historical data on the new metro lines leads to the challenge of predicting the origin-destination distribution of the whole network. Based on the advantage of Automatic Fare Collection data, this study proposes a novel method to predict origin-destination distribution over metro networks with expansions. Firstly, the disaggregate model is built to predict the origin-destination distributions of the whole network with consideration of passengers' destination choice behavior. Then, the aggregate model which can capture the historical passenger distribution patterns is developed to correct the origin-destination distribution in existing metro lines. Finally, this study combines the results of the disaggregate model and aggregate model to obtain the whole network origin-destination distribution. This study applies the method to the Guangzhou Metro network, and the results show the superiority of the proposed model. The results can help to improve the operation and management of urban rail transit.