An Automobile Control Method for Alleviation of Traffic Congestions Using Inter-Vehicle Ad Hoc Communication in Lattice-Like Roads

Inoue, Shinji; Shozaki, K.; Kakuda, Yoshiaki

doi:10.1109/glocomw.2007.4437828

Cited by 42 publications

(17 citation statements)

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“…The low level of accuracy is the main limitation of the StreetSmart because a number of vehicles can get involved in multiple clusters due to the overlapping cluster areas. Other congestion detection schemes for VANETs include Virtual Sink [29], V2X [11], and Lattice [4].…”

Section: Protocolmentioning

confidence: 99%

“…So far, a number of research efforts have been devoted to traffic congestion detection in both infrastructure mode and infrastructure-free mode. And these protocols aim to monitor road traffic and to estimate vehicle speed, density, and the arrival time [2][3][4]. Basically, sensing devices such as induction loop detectors [5], infrared detectors [6], microwave radars [7], and video recording devices [8] can be utilized to monitor vehicles on the highway.…”

Section: Introductionmentioning

confidence: 99%

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Microscopic Congestion Detection Protocol in VANETs

Ahmad

Chen

Khan

2018

Journal of Advanced Transportation

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Effective transportation status surveillance imposes critical challenges for the Intelligent Transportation System (ITS) design. In this paper, the microscopic congestion detection protocol (MCDP) is proposed to make the vehicle-to-vehicle (V2V) communication capable of monitoring vehicle density and identifying traffic jam. By introducing transportation control domain in the existing network protocol header, each vehicle can count its neighbors and estimate the time spacing among vehicles. MCDP provides an infrastructure-less solution to the estimate of vehicle density, flow, and average velocity in a microscopically manner. Moreover, the safety speed limit is introduced to make each vehicle calculate its time to cover the intervehicle distance, such that every vehicle is able to assess the transportation congestion by comparing with some predefined safety time threshold. Monte Carlo simulations of the MCDP over four typical Chinese highways are presented to compare the MCDP scheme with the existing GreenShield congestion detection scheme. In addition, real road traces generated by SUMO over NS2 are utilized to show the achieved performance in terms of throughput, end-to-end delay, and packet delivery rate (PDR) in comparison to DSR and AOMDV in IEEE 802.11p and IEEE 802.11ac scenarios. On the basis of all the results, we conclude that MCDP is an inexpensive transport congestion detection technique for Vehicular Ad Hoc Networks (VANETs).

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Section: Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microscopic Congestion Detection Protocol in VANETs

Ahmad

Chen

Khan

2018

Journal of Advanced Transportation

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“…However, this approach relies on the average area-passing time, which is not accurate to describe the realtime situation. [23] uses a label to interpret the congestion with vehicle density. The vehicle adopts an adaptive scheme to avoid entering any congested road.…”

Section: Related Workmentioning

confidence: 99%

GUI: GPS-Less Traffic Congestion Avoidance in Urban Areas with Inter-Vehicular Communication

Jiang

Sabatino

2014

2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems

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Abstract-Driving in an urban canyon can be frustrating when your GPS teller keeps telling you to make a turn at the place that you just passed, because the information transmission is deferred by the wireless signal reflecting off of buildings and other interfering objects. In this paper, we provide a practical solution for turn-to-turn guidance with inter-vehicle communication in vehicle ad-hoc networks (VANETs). Vehicles collect information from neighbors and catch the snapshot to describe the global impact of traffic congestions, in the presence of unpredictable changes of topology and vehicle trajectory. Without any centralized control, the information can be aggregated along the traffic flow and be disseminated in a minimal area, while sufficiently guiding each vehicle to achieve a global optimization on its path, and to remain on a non-blocked route. The information constitution is implemented in the proactive model, saving the delay of reconstruction in the reactive model (on-demand). Its substantial improvement on the elapsed time will be shown in the experimental results, compared with the best results known to date in both proactive and reactive information models.

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“…The former type of communications is used to collect the basic traffic data in each vehicle's transmission range. Once the data is collected, each vehicle computes that area's traffic density [6], traffic speed [5] or vehicle's path traveling time [14]. These computed values can give a good indication about the congestion level.…”

Section: A Congestion Detectionmentioning

confidence: 99%

Efficient traffic congestion detection protocol for next generation VANETs

Younes

Boukerche

2013

2013 IEEE International Conference on Communications (ICC)

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In large and highly populated downtown areas, traffic congestion is becoming a challenging problem. This work proposes an Efficient road COngestion DEtection protocol (ECODE). This protocol intends to detect road segments that are suffering high traffic congestion using cooperative vehicular communication. It uses multi-hop communication and geocast principles to gather and analyze vehicles' basic data per road segment. Each vehicle can evaluate and report its road segment's congestion level per direction efficiently. This protocol is discussed and reported on its performance compared to previous techniques in this field, using an extensive set of scenarios and experiments implemented on NS-2.

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An Automobile Control Method for Alleviation of Traffic Congestions Using Inter-Vehicle Ad Hoc Communication in Lattice-Like Roads

Cited by 42 publications

References 1 publication

Microscopic Congestion Detection Protocol in VANETs

Microscopic Congestion Detection Protocol in VANETs

GUI: GPS-Less Traffic Congestion Avoidance in Urban Areas with Inter-Vehicular Communication

Efficient traffic congestion detection protocol for next generation VANETs

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