Full Text:   <1167>

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CLC number: U491

On-line Access: 2017-03-10

Received: 2015-10-21

Revision Accepted: 2016-02-16

Crosschecked: 2017-02-21

Cited: 1

Clicked: 2267

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Erfan Shaghaghi

http://orcid.org/0000-0003-4461-9334

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.3 P.373-393

10.1631/FITEE.1500355


Adaptive green traffic signal controlling using vehicular communication


Author(s):  Erfan Shaghaghi, Mohammad Reza Jabbarpour, Rafidah Md Noor, Hwasoo Yeo, Jason J. Jung

Affiliation(s):  Department of Computer Systems and Technology, University of Malaya, Kuala Lumpur 50603, Malaysia; more

Corresponding email(s):   erfan_shaghaghi@siswa.um.edu.my

Key Words:  Vehicular ad hoc network (VANET), Intelligent transportation systems (ITSs), Clustering, Adaptive traffic signal control, Traffic controller, Fuel consumption


Erfan Shaghaghi, Mohammad Reza Jabbarpour, Rafidah Md Noor, Hwasoo Yeo, Jason J. Jung. Adaptive green traffic signal controlling using vehicular communication[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(3): 373-393.

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Abstract: 
The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today’s metropolitan life cannot be overemphasized. The vehicular ad hoc network (VANET), as an integral component of intelligent transportation systems (ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems (TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses: (1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and (2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service (QoS). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications is used. The V2V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network QoS criteria.

The authors describe a traffic signal control strategy for urban areas using VANET or connected vehicle technologies. The authors’ careful consideration of realistic communication systems to ensure that the proposed system is feasible is appreciated. It is good that their model was based on a real-world environment with buildings to influence interference, rather than a theoretical environment. Their approach has some promising innovations, primarily the V2V density estimation strategy. Their preliminary results seem to show promise when compared with alternative signal control strategies, especially in saturated and over-saturated conditions.

采用车载通信的自适应绿色交通信号控制

概要:为了解决当今大都市生活不可预测的交通拥堵问题,使用自适应交通信号控制的重要性无论怎样强调都不为过。作为智能交通系统(intelligent transportation systems, ITSs)的一个组成部分,一种能够取代传统仪器为自适应交通信号控制系统(traffic signal controlling systems, TSCSs)提供信息来源的车载局域网(vehicular ad hoc network, VANET)技术最近引起了学术界高度关注。同时,基于ITSs的VANET方法也有一些弱点:(1)VANET的基础数据类型与同步算法获得的信号不兼容;(2)从网络服务质量(quality of service,QoS)的角度看,车辆密度信息采集与传输过程的效率不高。为减少上述问题,本文提出了一种方法,通过减少车辆在十字路口的等待时间,提高了自适应TSCS的工作效率,从而进一步减少污染物排放。为实现上述目的,通信使用了车辆对车辆(vehicle-to-vehicle,V2V)和车辆对基础设施(vehicle-to-infrastructure,V2I)的结合。V2V通信方案包含了集群车辆密度的计算程序,而V2I通信用于转移计算密度信息和优化行动信息给路边的交通控制器。流量评估所需的主要交通输入数据为交叉路口主要交通车辆队列的长度。除了采用传统Webster方法的简单自适应TSCS以外,本文还将所提出的方法与目前流行的基于VANET相关的MC-DRIVE进行了比较。评价结果显示了所提出的方法在流量和网络QoS标准基础上的优越性。

关键词:车载局域网;智能交通系统;集群;自适应交通信号控制;交通控制器;燃料消耗量

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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