Real-time High-Speed Train Delay Prediction using Seemingly Unrelated Regression Models
(2023) 16th World Conference on Transport Research, Montreal WCTRS 2023- Abstract
- Understanding the impact of various factors on train arrival delays is a prerequisite for effective railway traffic operating control and
management. Existing studies model the train delay prediction problems using a single, generic equation, restricting their capability
in accounting for heterogeneous impacts of spatiotemporal factors on arrival delays as the train travels along its route. The paper
proposes a set of equations conditional on the train location for predicting train arrival delays at stations. We develop a seemingly
unrelated regression equation (SURE) model to estimate the coefficients simultaneously while considering potential correlations
between prediction residuals caused by shared unobserved... (More) - Understanding the impact of various factors on train arrival delays is a prerequisite for effective railway traffic operating control and
management. Existing studies model the train delay prediction problems using a single, generic equation, restricting their capability
in accounting for heterogeneous impacts of spatiotemporal factors on arrival delays as the train travels along its route. The paper
proposes a set of equations conditional on the train location for predicting train arrival delays at stations. We develop a seemingly
unrelated regression equation (SURE) model to estimate the coefficients simultaneously while considering potential correlations
between prediction residuals caused by shared unobserved variables (e.g., driver characteristics). The operational data for high-
speed trains on Sweden’s Southern Mainline from 2016 to 2020 is used to validate the proposed model and explore the effects
of operation-related factors on train arrival delays. The results confirm the necessity of developing a set of station-specific delay
prediction models to understand the heterogeneous impact of explanatory variables, and SURE provides more efficient parameter
estimations than the traditional ordinary least squares regression (OLS). The important factors impacting train arrival delays include
the scheduled and actual running time, scheduled dwell time, and train arrival delays at preceding stations. However, the impact
of these factors could vary depending on where the station is, and different types of operating management strategies should be
targeted. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/eb32a71c-2f09-48ff-bd9d-40dd987075b5
- author
- Tiong, Kah Yong LU ; Ma, Zhenliang and Palmqvist, Carl-William LU
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- in press
- subject
- host publication
- 16th World Conference on Transport Research (Montreal WCTRS 2023)
- publisher
- Elsevier
- conference name
- 16th World Conference on Transport Research, Montreal WCTRS 2023
- conference location
- Montreal, Canada
- conference dates
- 2023-07-17 - 2023-07-21
- language
- English
- LU publication?
- yes
- id
- eb32a71c-2f09-48ff-bd9d-40dd987075b5
- date added to LUP
- 2023-07-16 16:33:12
- date last changed
- 2023-10-10 13:27:13
@inproceedings{eb32a71c-2f09-48ff-bd9d-40dd987075b5, abstract = {{Understanding the impact of various factors on train arrival delays is a prerequisite for effective railway traffic operating control and<br/>management. Existing studies model the train delay prediction problems using a single, generic equation, restricting their capability<br/>in accounting for heterogeneous impacts of spatiotemporal factors on arrival delays as the train travels along its route. The paper<br/>proposes a set of equations conditional on the train location for predicting train arrival delays at stations. We develop a seemingly<br/>unrelated regression equation (SURE) model to estimate the coefficients simultaneously while considering potential correlations<br/>between prediction residuals caused by shared unobserved variables (e.g., driver characteristics). The operational data for high-<br/>speed trains on Sweden’s Southern Mainline from 2016 to 2020 is used to validate the proposed model and explore the effects<br/>of operation-related factors on train arrival delays. The results confirm the necessity of developing a set of station-specific delay<br/>prediction models to understand the heterogeneous impact of explanatory variables, and SURE provides more efficient parameter<br/>estimations than the traditional ordinary least squares regression (OLS). The important factors impacting train arrival delays include<br/>the scheduled and actual running time, scheduled dwell time, and train arrival delays at preceding stations. However, the impact<br/>of these factors could vary depending on where the station is, and different types of operating management strategies should be<br/>targeted.}}, author = {{Tiong, Kah Yong and Ma, Zhenliang and Palmqvist, Carl-William}}, booktitle = {{16th World Conference on Transport Research (Montreal WCTRS 2023)}}, language = {{eng}}, publisher = {{Elsevier}}, title = {{Real-time High-Speed Train Delay Prediction using Seemingly Unrelated Regression Models}}, year = {{2023}}, }