Forecasting Travel Speed in the Rainfall Days to Develop Suitable Variable Speed Limits Control Strategy for Less Driving Risk

In order to reduce driving risk in the rainfall days, developing the variable speed limits (VSL) is effective. However, it is hard to develop suitable VSL aligning with travel speed of mainstream that it affected by the traffic flow, rainfall intensity, individual travel speed, peak hours, working d...

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Main Authors: Ping Wang, Yajie Zhang, Saisai Wang, Li Li, Xiaohui Li
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2021/6639559
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author Ping Wang
Yajie Zhang
Saisai Wang
Li Li
Xiaohui Li
author_facet Ping Wang
Yajie Zhang
Saisai Wang
Li Li
Xiaohui Li
author_sort Ping Wang
collection DOAJ
description In order to reduce driving risk in the rainfall days, developing the variable speed limits (VSL) is effective. However, it is hard to develop suitable VSL aligning with travel speed of mainstream that it affected by the traffic flow, rainfall intensity, individual travel speed, peak hours, working days, random events, and so on. In this paper, the average travel speed and traffic flow of each road section are calculated from the toll collection data of Xi’an Ring Road from May to July in 2018 in Shaanxi Province, China. The weather data are collected and extrapolated to the corresponding road sections. Travel speed, traffic flow, and rainfall intensity are integrated to predict the fluctuation trend of travel speed through the proposed deep belief-radial basis function network. The experimental results show that a significant decrease happens in the travel speed in the rainfall day during peak hours. Furthermore, the deep learning algorithm that considers more factors such as the rainfall intensity and traffic flow could improve the prediction accuracy. Then, a VSL method and an expressway risk coefficient evaluation method based on estimation of average travel speed are proposed. The experimental results show that the variable 85th percentile speed limit method proposed in this paper can reduce the risk of expressway driving. This can promote road safety in the development of intelligent transportation system (ITS) in future.
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institution Kabale University
issn 0197-6729
2042-3195
language English
publishDate 2021-01-01
publisher Wiley
record_format Article
series Journal of Advanced Transportation
spelling doaj-art-661ab5c3a1df409f85663fb9680a935f2025-02-03T05:52:34ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66395596639559Forecasting Travel Speed in the Rainfall Days to Develop Suitable Variable Speed Limits Control Strategy for Less Driving RiskPing Wang0Yajie Zhang1Saisai Wang2Li Li3Xiaohui Li4School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronics and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronics and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronics and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronics and Control Engineering, Chang’an University, Xi’an 710064, ChinaIn order to reduce driving risk in the rainfall days, developing the variable speed limits (VSL) is effective. However, it is hard to develop suitable VSL aligning with travel speed of mainstream that it affected by the traffic flow, rainfall intensity, individual travel speed, peak hours, working days, random events, and so on. In this paper, the average travel speed and traffic flow of each road section are calculated from the toll collection data of Xi’an Ring Road from May to July in 2018 in Shaanxi Province, China. The weather data are collected and extrapolated to the corresponding road sections. Travel speed, traffic flow, and rainfall intensity are integrated to predict the fluctuation trend of travel speed through the proposed deep belief-radial basis function network. The experimental results show that a significant decrease happens in the travel speed in the rainfall day during peak hours. Furthermore, the deep learning algorithm that considers more factors such as the rainfall intensity and traffic flow could improve the prediction accuracy. Then, a VSL method and an expressway risk coefficient evaluation method based on estimation of average travel speed are proposed. The experimental results show that the variable 85th percentile speed limit method proposed in this paper can reduce the risk of expressway driving. This can promote road safety in the development of intelligent transportation system (ITS) in future.http://dx.doi.org/10.1155/2021/6639559
spellingShingle Ping Wang
Yajie Zhang
Saisai Wang
Li Li
Xiaohui Li
Forecasting Travel Speed in the Rainfall Days to Develop Suitable Variable Speed Limits Control Strategy for Less Driving Risk
Journal of Advanced Transportation
title Forecasting Travel Speed in the Rainfall Days to Develop Suitable Variable Speed Limits Control Strategy for Less Driving Risk
title_full Forecasting Travel Speed in the Rainfall Days to Develop Suitable Variable Speed Limits Control Strategy for Less Driving Risk
title_fullStr Forecasting Travel Speed in the Rainfall Days to Develop Suitable Variable Speed Limits Control Strategy for Less Driving Risk
title_full_unstemmed Forecasting Travel Speed in the Rainfall Days to Develop Suitable Variable Speed Limits Control Strategy for Less Driving Risk
title_short Forecasting Travel Speed in the Rainfall Days to Develop Suitable Variable Speed Limits Control Strategy for Less Driving Risk
title_sort forecasting travel speed in the rainfall days to develop suitable variable speed limits control strategy for less driving risk
url http://dx.doi.org/10.1155/2021/6639559
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AT saisaiwang forecastingtravelspeedintherainfalldaystodevelopsuitablevariablespeedlimitscontrolstrategyforlessdrivingrisk
AT lili forecastingtravelspeedintherainfalldaystodevelopsuitablevariablespeedlimitscontrolstrategyforlessdrivingrisk
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