An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail Traffic

An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail Traffic

Dwell time estimation plays an important role in the operation of urban rail system. On this specific problem, a range of models based on either polynomial regression or microsimulation have been proposed. However, the generalization performance of polynomial regression models is limited and the acc...

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Main Authors: Wen-jun Chu, Xing-chen Zhang, Jun-hua Chen, Bin Xu
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2015/473432
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spelling doaj-4ab7c7a022a142ab942864df10390d282020-11-24T23:16:58ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/473432473432An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail TrafficWen-jun Chu0Xing-chen Zhang1Jun-hua Chen2Bin Xu3State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaDwell time estimation plays an important role in the operation of urban rail system. On this specific problem, a range of models based on either polynomial regression or microsimulation have been proposed. However, the generalization performance of polynomial regression models is limited and the accuracy of existing microsimulation models is unstable. In this paper, a new dwell time estimation model based on extreme learning machine (ELM) is proposed. The underlying factors that may affect urban rail dwell time are analyzed first. Then, the relationships among different factors are extracted and modeled by ELM neural networks, on basis of which an overall estimation model is proposed. At last, a set of observed data from Beijing subway is used to illustrate the proposed method and verify its overall performance.http://dx.doi.org/10.1155/2015/473432
collection DOAJ
language English
format Article
sources DOAJ
author Wen-jun Chu
Xing-chen Zhang
Jun-hua Chen
Bin Xu
spellingShingle Wen-jun Chu
Xing-chen Zhang
Jun-hua Chen
Bin Xu
An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail Traffic
Mathematical Problems in Engineering
author_facet Wen-jun Chu
Xing-chen Zhang
Jun-hua Chen
Bin Xu
author_sort Wen-jun Chu
title An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail Traffic
title_short An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail Traffic
title_full An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail Traffic
title_fullStr An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail Traffic
title_full_unstemmed An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail Traffic
title_sort elm-based approach for estimating train dwell time in urban rail traffic
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2015-01-01
description Dwell time estimation plays an important role in the operation of urban rail system. On this specific problem, a range of models based on either polynomial regression or microsimulation have been proposed. However, the generalization performance of polynomial regression models is limited and the accuracy of existing microsimulation models is unstable. In this paper, a new dwell time estimation model based on extreme learning machine (ELM) is proposed. The underlying factors that may affect urban rail dwell time are analyzed first. Then, the relationships among different factors are extracted and modeled by ELM neural networks, on basis of which an overall estimation model is proposed. At last, a set of observed data from Beijing subway is used to illustrate the proposed method and verify its overall performance.
url http://dx.doi.org/10.1155/2015/473432
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