Modeling Long-Range Travelling Times with Big Railway Data

Sun, Wenya; Grubenmann, Tobias; Cheng, Reynold; Kao, Ben; Ching, Waiki

Abstract

Big Railway Data, such as train movement logs and timetables, have become increasingly available. By analyzing these data, insights about train movement and delay can be extracted, allowing train operators to make smarter train management decisions. In this paper, we study the problem of performing long-range analysis on Big Railway Data, such as estimating the remaining journey time, i.e., the amount of time for a given train to reach the terminal station. We study how existing statistical and machine learning methods, designed for short-range analysis (e.g., estimating the traveling time between two adjacent stations), can be extended to perform long-range analysis. We further design a method, called a-LSTM, based on LSTM (long short-term memory) neural network and attention models. Extensive evaluation on a large amount of train movement data provided by a train service provider in Hong Kong shows that a-LSTM is more effective than other solutions in predicting traveling times.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 27th International Conference on Database Systems for Advanced Applications
• Start Date: Apr 11, 2022
• End Date: Apr 14, 2022
• Online Publication Date: Apr 8, 2022
• Publication Date: 2022
• Deposit Date: Jun 8, 2023
• Publisher: Springer
• Pages: 443-454
• Book Title: Database Systems for Advanced Applications: 27th International Conference, DASFAA 2022
• ISBN: 978-3-031-00128-4
• DOI: https://doi.org/10.1007/978-3-031-00129-1_38