Speed-Flow-Geometric Relationship for Urban Roads Network

Speed on the urban roads is significantly affected by the surrounding geometric and traffic parameters. Based on this fact, we ascertained the impact of the geometric and traffic parameters on the average travel speed of the urban roads network. Herein, 197 urban road segments in Johor (Malaysia) wi...

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发表在:Applied Sciences
Main Authors: Tareq M. Al-Bahr, Sitti Asmah Hassan, Othman Che Puan, Nordiana Mashros, Nur Sabahiah Abdul Sukor
格式: 文件
语言:英语
出版: MDPI AG 2022-04-01
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在线阅读:https://www.mdpi.com/2076-3417/12/9/4231
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author Tareq M. Al-Bahr
Sitti Asmah Hassan
Othman Che Puan
Nordiana Mashros
Nur Sabahiah Abdul Sukor
author_facet Tareq M. Al-Bahr
Sitti Asmah Hassan
Othman Che Puan
Nordiana Mashros
Nur Sabahiah Abdul Sukor
author_sort Tareq M. Al-Bahr
collection DOAJ
container_title Applied Sciences
description Speed on the urban roads is significantly affected by the surrounding geometric and traffic parameters. Based on this fact, we ascertained the impact of the geometric and traffic parameters on the average travel speed of the urban roads network. Herein, 197 urban road segments in Johor (Malaysia) with diverse features were randomly selected. The average travel speed and volume of the traffic on these road segments were measured using the moving observer method (MOM). Meanwhile, these roads’ features were recorded via the direct visual inspection. Various geometric (density of the traffic calming speed, right-turn driveway, access, and right-turn) and cross-sectional (median, number of lanes, and side friction) parameters were considered. First, 14 multilinear models constructed via multilinear regression analysis were developed for traffic volume scenarios (in veh/h and pcu/h). Then, 10 models were adopted to evaluate the geometric parameters’ influence on the average travel speed for the selected roads. The results revealed a considerable impact of some geometric and traffic parameters on the average travel speed for the studied urban roads. Furthermore, the density of traffic calming speed, driveway, and intersection per 1 km of urban road segment one for each parameter was found to reduce the speed of the vehicles from 1.3 to 0.22 km/h. The combination of the road cross-section features such as median, number of lanes, and side friction strongly affected the observed speed variation. It is asserted that the developed model may facilitate the Malaysian urban roads network management to provide better traffic performance with higher mobility and safer roads design and planning, thereby offering a gateway toward sustainability.
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spelling doaj-art-66331fedebfd44e7b72d2d310ff2ace72025-08-19T22:44:22ZengMDPI AGApplied Sciences2076-34172022-04-01129423110.3390/app12094231Speed-Flow-Geometric Relationship for Urban Roads NetworkTareq M. Al-Bahr0Sitti Asmah Hassan1Othman Che Puan2Nordiana Mashros3Nur Sabahiah Abdul Sukor4Faculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia (UTM), Skudai 81310, MalaysiaFaculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia (UTM), Skudai 81310, MalaysiaDepartment of Civil Engineering, Engineering College, Universiti Malaysia Pahang, Lebuhraya Tun Razak, Kuantan 26300, MalaysiaFaculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia (UTM), Skudai 81310, MalaysiaSchool of Civil Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, MalaysiaSpeed on the urban roads is significantly affected by the surrounding geometric and traffic parameters. Based on this fact, we ascertained the impact of the geometric and traffic parameters on the average travel speed of the urban roads network. Herein, 197 urban road segments in Johor (Malaysia) with diverse features were randomly selected. The average travel speed and volume of the traffic on these road segments were measured using the moving observer method (MOM). Meanwhile, these roads’ features were recorded via the direct visual inspection. Various geometric (density of the traffic calming speed, right-turn driveway, access, and right-turn) and cross-sectional (median, number of lanes, and side friction) parameters were considered. First, 14 multilinear models constructed via multilinear regression analysis were developed for traffic volume scenarios (in veh/h and pcu/h). Then, 10 models were adopted to evaluate the geometric parameters’ influence on the average travel speed for the selected roads. The results revealed a considerable impact of some geometric and traffic parameters on the average travel speed for the studied urban roads. Furthermore, the density of traffic calming speed, driveway, and intersection per 1 km of urban road segment one for each parameter was found to reduce the speed of the vehicles from 1.3 to 0.22 km/h. The combination of the road cross-section features such as median, number of lanes, and side friction strongly affected the observed speed variation. It is asserted that the developed model may facilitate the Malaysian urban roads network management to provide better traffic performance with higher mobility and safer roads design and planning, thereby offering a gateway toward sustainability.https://www.mdpi.com/2076-3417/12/9/4231average travel speedurban roads networkMOMgeometric and traffic parametersmultilinear regression analysis
spellingShingle Tareq M. Al-Bahr
Sitti Asmah Hassan
Othman Che Puan
Nordiana Mashros
Nur Sabahiah Abdul Sukor
Speed-Flow-Geometric Relationship for Urban Roads Network
average travel speed
urban roads network
MOM
geometric and traffic parameters
multilinear regression analysis
title Speed-Flow-Geometric Relationship for Urban Roads Network
title_full Speed-Flow-Geometric Relationship for Urban Roads Network
title_fullStr Speed-Flow-Geometric Relationship for Urban Roads Network
title_full_unstemmed Speed-Flow-Geometric Relationship for Urban Roads Network
title_short Speed-Flow-Geometric Relationship for Urban Roads Network
title_sort speed flow geometric relationship for urban roads network
topic average travel speed
urban roads network
MOM
geometric and traffic parameters
multilinear regression analysis
url https://www.mdpi.com/2076-3417/12/9/4231
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AT sittiasmahhassan speedflowgeometricrelationshipforurbanroadsnetwork
AT othmanchepuan speedflowgeometricrelationshipforurbanroadsnetwork
AT nordianamashros speedflowgeometricrelationshipforurbanroadsnetwork
AT nursabahiahabdulsukor speedflowgeometricrelationshipforurbanroadsnetwork