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  • Distributed Road Traffic Congestion Quantification Using Cooperative VANETs

    Milos Milojevic and Veselin Rakocevic City University London

    {milos.milojevic.1, v.rakocevic @city.ac.uk}

    Abstract The well-known traffic congestion problem in urban environments has negative impact on many areas including economy, environment, health and lifestyle. Recently, a number of solutions based on vehicle-to-vehicle communications were proposed for traffic congestion detection and management. In this paper we present an algorithm designed to enable each vehicle in the network to detect and quantify the level of traffic congestion in completely distributed way, independent of any supporting infrastructure and additional information such as traffic data from local authorities. Based on observations of traffic congestion by every vehicle, and by adapting the broadcast interval, it enables dissemination of the traffic information to other vehicles. The algorithm also makes every vehicle aware about the congestion level on the streets that are spatially separated from their current location by several streets. Its robustness keeps the vehicles overall knowledge about congestion consistent, despite the short-term changes in vehicles motion. Since the quantification of congestion is based on per-vehicle basis, the algorithm is able to operate even when only 10% of vehicles in the network are VANET enabled. Data aggregation and adaptive broadcasting are used to ensure that vehicles do not send redundant information about the traffic congestion. The simulations are conducted in Veins framework based on OMNeT++ network simulator and SUMO vehicular mobility simulator.

    Keywords traffic congestion detection and quantification; vehicular ad hoc networks; intelligent transport systems; cooperation; data aggregation; dissemination;

    I. INTRODUCTION Negative effects of traffic congestion in urban

    environments are well-known and they impact many areas of life including economy, environment, health and lifestyle. Traffic congestion can be caused by different factors from accidents to weather conditions, but when congestion happens the road infrastructure becomes too small to handle the large demand. Recently there have been several solutions proposed for traffic congestion detection and management, based on vehicle-to-vehicle communications and 802.11p standard [1].

    One of the first papers dealing with distributed traffic congestion detection and management is [2], where traffic information system called SOTIS is presented. In SOTIS, each vehicle analyses traffic conditions based on the messages received from other vehicles, which include information about current speed, position, road identification and time. Periodic broadcasting is used as a way to disseminate messages and SOTIS was evaluated by simulation, only in the highway scenario, which evaluates only the delays of sent messages. No

    data about traffic information, measurements and analysis is presented, neither any accuracy-related information. Traffic View [3] is another early published paper considering traffic information dissemination. It focuses on data aggregation in order to distribute the messages which contain information about the average speed of vehicles on the road, position and broadcast time. This way, vehicles are aware about other vehicles on the road. However, it does not provide and disseminate any information about intensity or volume of the traffic on the road. This approach was simulated only based on 802.11b standard and in highway environment. Another work related to distributed V2V traffic congestion detection and forecasting algorithm is presented in [4]. Authors define the road is in congested state if the travel times of vehicles are exceeding normal travel times under free flow traffic. Additionally, the scheme requires that each road segment needs to be observed for a day and that vehicles send their traversal times to a centralized entity. The results show the analysis of traversal time of vehicles in different simulation scenarios, but do not relate to intensity and volume of the traffic. In [5] authors presented cooperative approach for congestion detection (CoTEC) which is based on fuzzy logic, where vehicles periodically broadcast messages. The congestion is detected based on vehicles speed and the received broadcast messages from other vehicles, together with external metrics called level of service (LOS) for classification of traffic congestion developed by Skycomp. This system defines metrics based on aerial surveys of different highways. Congestion detection algorithm has been evaluated in highway scenario. Authors in [6] developed a distributed V2V system, where vehicles send messages containing information based on travel time on the road section. The traffic estimation is evaluated on the basis of trip time analysis, but it is not evaluated how the scheme influences the dissemination of the messages and their accuracy. The authors concluded that real-time and up to date traffic information can reduce the traffic congestion in realistic scenario. Another cooperative solution for traffic congestion detection based on VANETs is presented in [7], where authors use event-driven architecture (EDA) to detect different levels of traffic jams. It showed good results although the system performance highly depends on the VANET penetration rate and the scheme uses periodic broadcasting. In [8] authors presented V2V-based congestion detection scheme which detects the traffic jam based on travel times of the vehicles. It uses geo-cast flooding-based protocol which uses request and response messages. Authors of [9] present a scheme based on periodically broadcasted beacons which contain speed, which are then used to calculate if the

    2014 13th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET)

    978-1-4799-5258-8/14/$31.00 2014 IEEE 203

  • road is congested or not. Paper [10] presents traffic congestion detection mechanism based on VANETs. The scheme shows good results in the case of low VANET penetration rate however it requires initial observation of the streets with external device.

    As previously mentioned, solving the traffic congestion problem by using VANETs has been one of the hot research topics recently. The presented solutions based on V2V communications present the following important limitations:

    Some of the proposed solutions [4] [5] [10], rely on extra information about traffic conditions obtained either from third party companies or local authorities, which are then feed to the algorithms. The extra information sometimes is gathered by centralized entities such as traffic centers, and moreover the schemes in this case also depend on the accuracy of such information.

    Certain schemes, like [2] [7] [9], assume periodic broadcasting as a way of sending the messages without considering adaptation of broadcast interval. This is especially important because in traffic jams it might lead to broadcast storm, collision and inefficient message dissemination [11]-[13].

    Some schemes, [2] [3] [4] [6], only defer congested from non-congested states, and they do not provide any information about the intensity and duration of congestion, but just offer analysis of travel times of vehicles. Additionally, the authors mostly assume that vehicles exchange messages containing data such as speed, direction or coordinates which receiving vehicles then further need to process to determine if and where congestion exists.

    Some authors the failed to show an insight how their schemes disseminate traffic related information [6], and no analysis of accuracy and delay of received information from other vehicles is given.

    Some of the papers in related work have one of previously described problems, but some of them have more or even all of them. We propose an algorithm designed to enable each vehicle in the network to detect and quantify the level of traffic congestion in completely distributed way, independent of any supporting infrastructure and additional information such as traffic data from local authorities. It is based on observations of traffic conditions by every vehicle and adaptive broadcasting as a way to disseminate the traffic information to other vehicles. All the vehicles are also aware about the congestion level on the streets spatially separated from their current location. Its robustness makes vehicles aware of the overall congestion level in certain street, despite the short-term changes in vehicles mobility. The congestion quantification process is based on per-vehicle basis, making the algorithm able to operate even in case of low VANET penetration rate. Data aggregation and adaptive broadcasting, ensures that vehicles do not send redundant information about the traffic congestion.

    The rest of the paper is organized as follows: Section II shows the algorithm, while we present results of extensive

    simulations and testing in section III. The Section IV shows analysis of the low VANET penetration rate impact. Conclusions together with future steps in our research are presented in section V.

    II. THE ALGORITHM Having in mind the previously described problems and the

    limitation of some of the existing proposals for congestion detection by VANETs, we envisaged our algorithm to have several characteristics and capabilities:

    It needs to be completely distributed and independent form any kind of infrastructures and external systems. This means that we want to create an algorithm which will enable every vehicle to detect and quantify the congestion on its own without necessary help from others. In our opinion this is important because not all the vehicles will be VANET enabled and able to communicate with others.

    It needs to be efficient in terms of message excha

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