Enhanced detection of road traffic congestion areas using cell dwell times

Thajchayapong, Suttipong; Pattara-Atikom, Wasan; Chadil, N.; Mitrpant, C.

doi:10.1109/itsc.2006.1707366

Cited by 28 publications

(9 citation statements)

References 5 publications

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“…[1] proposes a scheme to determine the congestion by calculating the length of the waiting queue at the intersection. [20] utilizes the "cell dwell time" (also called "area passage time" in [4]) to obtain the information of congestion. The cell dwell time is the duration for which a mobile unit stays in a certain region.…”

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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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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“…Additionally, with the help of the audio receiver units mounted on street lamp post [53], the GPS system embedded in drivers' smartphone [54], or the E-SAR airborne radar sensors [55], the road congestion information detection also can be realized.…”

Section: Road Congestion Detectionmentioning

confidence: 99%

A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs

Zhang

Gao

Foh

et al. 2014

International Journal of Distributed Sensor Networks

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With the continuously increasing number of vehicles, particularly in cities, there are more and more abnormal traffic events occurring on road, which easily cause road congestion. In vehicular sensor networks (VSNs), with the intervehicle cooperative communication, vehicle nodes can detect the abnormal traffic events and disseminate the related information to the interesting vehicles or persons. This paper gives out a survey of the abnormal traffic information detection and transmission mechanisms in VSNs. Firstly, we overview the origin, characteristic, and application of VSNs. Meanwhile, the definition and features of traffic abnormal information are also introduced and presented. Then, we put more energy on elaborating the existing information detection and transmission mechanisms based on VSNs. Finally, the challenges and problems are discussed.

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“…In our previous work [5], we described Cell Dwell Time (CDT) as the time period that a mobile phone remains registered to a base station and that there is a statistically significant correlation between CDT and the degree of traffic congestion. In our recent work [6], we used CDT to estimate traffic congestion and the results further supported our suggestion that CDT data have the potential to be a traffic congestion estimation measure.…”

Section: Related Workmentioning

confidence: 99%

Estimating road traffic congestion from cellular handoff information using cell-based neural networks and K-means clustering

Hongsakham

Pattara-Atikom

Peachavanish

2008

2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolog

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This research proposes alternative methods for estimating degrees of road traffic congestion by using Cell Dwell Time (CDT) information available from cellular networks. CDT is the duration that a cellular phone remains associated to a base station between handoff events. As a phone in a vehicle travels along a road having different degrees of congestion, the value of CDT varies accordingly. Measurements of CDT were taken and classified into one of the three degrees of congestion using 1) Kmeans clustering algorithm and 2) backpropagation neural network. These machine-assigned classifications were then compared against human opinion to assess the accuracy. The results demonstrate the feasibility of using K-means and neural networks in classifying degrees of traffic congestion and that the neural network approach performs well for this task.

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Enhanced detection of road traffic congestion areas using cell dwell times

Cited by 28 publications

References 5 publications

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

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

A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs

Estimating road traffic congestion from cellular handoff information using cell-based neural networks and K-means clustering

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