A performance evaluation of an efficient traffic congestion detection protocol (ECODE) for intelligent transportation systems

Younes, Maram Bani; Boukerche, Azzedine

doi:10.1016/j.adhoc.2014.09.005

Cited by 68 publications

(29 citation statements)

References 16 publications

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“…Younes et al [6] proposed an Efficient Congestion Detection protocol to evaluate the traffic characteristics of each road segment. H. Yue et al [7] apply Linear Constraint Databases to forecast speed of traffic, Pattara-atikom et al [8] divided the traffic congestion into three levels, and estimated the road traffic congestion using the weighted averages of measured GPS speed.…”

Section: Related Workmentioning

confidence: 99%

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Efficient traffic congestion estimation using multiple spatio-temporal properties

Yang

Han

et al. 2017

Neurocomputing

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Abstract-Traffic estimation is an important issue to analyze the traffic congestion in large-scale urban traffic situations.Recently, many researchers have used GPS data to estimate traffic congestion. However, how to fuse the multiple data reasonably and guarantee the accuracy and efficiency of these methods are still challenging problems. In this paper, we propose a novel method Multiple Data Estimation (MDE) to estimate the congestion status in urban environment with GPS trajectory data efficiently, where we estimate the congestion status of the area through utilizing multiple properties, including density, velocity, inflow and previous status. Among them, traffic inflow and previous status (combination of time and space factors) are not both used in other existing methods. In order to ensure the accuracy and efficiency, we apply dynamic weights of data and parameters in MDE method. To evaluate our methods, we apply it on large-scale taxi GPS data of Beijing and Shanghai. Extensive experiments on these two real-world datasets demonstrate the significant improvements of our method over several state-ofthe-art methods.

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Section: Related Workmentioning

confidence: 99%

“…First, the number of cars in a section (density of a section: den) is the most significant property to estimate urban congestion [4][5][6]. The cars' quantity in the road can reflect the congestion status directly.…”

Section: A Determination Of Multiple Properties In Congestion Estimamentioning

confidence: 99%

Efficient traffic congestion estimation using multiple spatio-temporal properties

Yang

Han

et al. 2017

Neurocomputing

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“…The purpose is to assess if the temporary induced traffic change related to an event can be mitigated in a short period or does the event represent a permanent change representing an NRC. We implemented the methods described in [2], Basic Traffic Data Gathering algorithm, Local Traffic Evaluation Algorithm and Expanding the Evaluated Area algorithm. We add two types of messages: Extraordinary Event Request (RQ) and Extraordinary Event Response (RP).…”

Section: Phase 3: Cooperative Processmentioning

confidence: 99%

Distributed Classification of Urban Congestion Using VANET

Mallah

Quintero

Farooq

2017

IEEE Trans. Intell. Transport. Syst.

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Vehicular ad hoc networks (VANETs) can efficiently detect traffic congestion, but detection is not enough, because congestion can be further classified as recurrent and nonrecurrent congestion (NRC). In particular, NRC in an urban network is mainly caused by incidents, work zones, special events, and adverse weather. We propose a framework for the real-time distributed classification of congestion into its components on a heterogeneous urban road network using VANET. We present models built on an understanding of the spatial and temporal causality measures and trained on synthetic data extended from a real case study of Cologne. Our performance evaluation shows a predictive accuracy of 87.63% for the deterministic classification tree, 88.83% for the naïve Bayesian classifier, 89.51% for random forest, and 89.17% for the boosting technique. This framework can assist transportation agencies in reducing urban congestion by developing effective congestion mitigation strategies knowing the root causes of congestion.

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“…For the scenarios in which the transmission range of traveling vehicles does not cover the entire road segment, the road segment is divided into a set of virtual adjacent clusters, and the traffic characteristics of each cluster are evaluated separately. Communication among traveling vehicles in these clusters helps to cooperatively generate a traffic report that summarizes the traffic characteristics of the entire road segment . Moreover, the traffic characteristics should be evaluated to consider each traffic direction separately.…”

Section: Distributed Path Recommendation Protocolsmentioning

confidence: 99%

Traffic balancing-based path recommendation mechanisms in vehicular networks

Younes

Boukerche

Román-Alonso

2015

Wirel. Commun. Mob. Comput.

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In this paper, we propose both reactive and proactive balancing traffic path recommendation mechanisms, which we refer to as Bal-Traf and Abs-Bal, respectively. Bal-Traf is initiated when a certain output road segment located at any road intersection is detected in an overloaded situation. In the event that the existing traffic density of any output road exceeds its optimal capacity, Bal-Traf recommends that those vehicles that plan to pass over this road segment as next hop choose another, less congested output road segment. On the other hand, Abs-Bal is a proactive balancing traffic mechanism. Its main purpose is to distribute input traffic completely even among all output road segments at intersections. Moreover, Abs-Bal considers the best travel time of vehicles in addition to the goal of balancing traffic. From the experimental results, we can see that Bal-Traf eliminates the number of overwhelmed road segments over the road network in scenarios with only partial network congestion. It also decreases the number of congested road segments in scenarios with complete network congestion. However, it increases the density drastically over the remaining congested road segments in these scenarios. Abs-Bal performs well in decreasing the percentage of congested road segments and balancing traffic among road segments located throughout the road network, in the event of complete network congestion.

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A performance evaluation of an efficient traffic congestion detection protocol (ECODE) for intelligent transportation systems

Cited by 68 publications

References 16 publications

Efficient traffic congestion estimation using multiple spatio-temporal properties

Efficient traffic congestion estimation using multiple spatio-temporal properties

Distributed Classification of Urban Congestion Using VANET

Traffic balancing-based path recommendation mechanisms in vehicular networks

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