“…More complicated models like neural networks [11] can also be used. BeeJamA [12] (IB-TM-N) is derived from the honey bee behavior to avoid traffic congestion. A density-speed model is used to rate the congestion.…”
Dynamic vehicular path planning using real-time traffic information have attracted the interest for both academic and industry. How to collect traffic information and make path planning decisions accordingly are two major problems. Existing works have addressed these issues using centralized or infrastructure based traffic collection approaches. However, existing works have certain weaknesses on efficiency and effectiveness. This paper introduced a novel dynamic vehicular path planning solution. The proposed solution does not rely on infrastructures to collect traffic information. Meanwhile, It utilizes density-speed traffic flow model to predict the traffic condition. In addition, a dynamic candidate path selection algorithm is developed to reduce the redundant data collection overhead. Extensive evaluations using large scale traffic trace based simulation have been performed. The results show that our solution outperforms some existing solutions in terms of communication efficiency and path planning effectiveness.Index Terms-Dynamic path planning, VANET, Traffic data collection, Vehicle speed estimation.
“…More complicated models like neural networks [11] can also be used. BeeJamA [12] (IB-TM-N) is derived from the honey bee behavior to avoid traffic congestion. A density-speed model is used to rate the congestion.…”
Dynamic vehicular path planning using real-time traffic information have attracted the interest for both academic and industry. How to collect traffic information and make path planning decisions accordingly are two major problems. Existing works have addressed these issues using centralized or infrastructure based traffic collection approaches. However, existing works have certain weaknesses on efficiency and effectiveness. This paper introduced a novel dynamic vehicular path planning solution. The proposed solution does not rely on infrastructures to collect traffic information. Meanwhile, It utilizes density-speed traffic flow model to predict the traffic condition. In addition, a dynamic candidate path selection algorithm is developed to reduce the redundant data collection overhead. Extensive evaluations using large scale traffic trace based simulation have been performed. The results show that our solution outperforms some existing solutions in terms of communication efficiency and path planning effectiveness.Index Terms-Dynamic path planning, VANET, Traffic data collection, Vehicle speed estimation.
“…These mobility features may be used to predict the lifetime/duration of routing paths. PBR [13], DisjLi [12], Taleb [14], Abedi [11], Wedde [15] and NiuDe [16] utilize the mobility parameters to route messages.…”
Section: A Taxonomy Of Vanet Routingmentioning
confidence: 99%
“…Moreover, position is the second important parameter that is used for next hop selection. Wedde et al [15] presented a routing algorithm (marked as Wedde) based on a rating value. The rating value is computed to evaluate the road conditions (actual traffic situation), based on the interdependencies of average vehicle speed, traffic density and the traffic quality (in terms of congestion).…”
Section: B State Of the Artmentioning
confidence: 99%
“…The vehicle mobility is used to predict that if the link between two vehicles will break or not after a certain time interval, in mobility based methods. A probability model based Biswas [9] Murthy [10] Abedi [11] DisjLi [12] PBR [13] DisjLi [12] Taleb [14] Abedi [11] Wedde [15] NiuDe [16] DRR [17] SARC [18] Bus [19] CarNet [20] Kato [21] Zone [22] Greedy [23,24] ROVER [25] LORA-DCBF [26] REAR [30] CAR [29] NiuDe [16] GVGrid [28] Yan [27] routing method avoids using extra device or information. A probability model will be setup to model the wireless routing link which only involves two nodes.…”
One of the notoriously difficult problems in vehicular ad-hoc networks (VANET) is to ensure that established routing paths do not break before the end of data transmission. This is a difficult problem because the network topology is constantly changing and the wireless communication links are inherently unstable, due to high node mobility. In this paper we classify existing VANET routing protocols into five categories: connectivitybased, mobility-based, infrastructure-based, geographic-locationbased, and probability-model-based, according to their employed routing metrics. For each category, we present the general design ideas and state of the art. Our objective is to attract more attention to the VANET routing problem and encourage more research efforts on developing reliable solutions.
“…Hence, because of the expected potential impact of VANETs, several researchers have developed unicast routing protocols that are suitable for VANETs. The effect of traffic patterns and congestion on VANETs are studied in [25,55,67,68]. Security issues in VANETs are discussed in [13,22,24,27,56].…”
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