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See detailMitigating flash crowd effect using connected vehicle technology
Grzybek, Agata UL; Danoy, Grégoire UL; Bouvry, Pascal UL et al

in Vehicular Communications (2015), 2(4),

A Flash Crowd Effect (FCE) occurs when in the case of non-recurring congestion a large portion of drivers follows similar re-routing advice. Consequently, congestion is transferred from one road to ... [more ▼]

A Flash Crowd Effect (FCE) occurs when in the case of non-recurring congestion a large portion of drivers follows similar re-routing advice. Consequently, congestion is transferred from one road to another. Coping with the FCE is challenging, especially if the congestion results from a temporary loss of capacity (e.g. due to a traffic incident). The existing route guidance systems do not address FCE, as they either do not consider the effects of guidance on the rest of the road network, or predict link travel times based on the number of vehicles travelling on the link, which in the case of the loss of capacity is unreliable. We demonstrate that the FCE can be addressed in a distributed way with Vehicle-to-Vehicle (V2V) communication provided by Connected Vehicle (CV) technology. The proposed in-vehicle TrafficEQ system provides vehicles with mixed route guidance strategy—i.e. a route is autonomously chosen by the vehicle with a probability that is inversely proportional to the latest reported travel time on the route. Real-time travel time information is crowd-sourced by TrafficEQ users. Using realistic simulations of incident-related capacity drops on a classic two-route highway example and a realistic urban road network, we demonstrate that TrafficEQ can address the FCE by reducing travel time oscillations among the alternative routes. The system's drawbacks—in particular the occasional necessity of providing incentives to follow the guidance—are discussed. [less ▲]

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See detailCommunity-based vehicular networks for traffic information systems
Grzybek, Agata UL

Doctoral thesis (2015)

Traffic information systems (TIS) monitor road traffic conditions with the goal of improving the efficiency of road networks. Given the ever increasing number of cars, TIS are becoming more critical, as ... [more ▼]

Traffic information systems (TIS) monitor road traffic conditions with the goal of improving the efficiency of road networks. Given the ever increasing number of cars, TIS are becoming more critical, as they help reduce traffic congestion which have large negative social, economical, and environmental impact. Current TIS have several drawbacks. First, there are insufficient sources of traffic data, that often require either expensive sensing infrastructure or a contract with a network provider owning GPS data. Secondly, they are often limited by a centralised architecture introducing both delays in delivery of real-time data and a single-point of failure. Finally, even after collecting traffic information, they provide no easy way to effectively relay information to drivers or apply traffic management strategies. New vehicle-to-vehicle (V2V) communication technologies can potentially aid in overcoming these limitations. Using V2V, vehicles can communicate with each other and exchange traffic information within an ad hoc vehicular network (VANET). VANETs could drastically change the nature of traffic systems, moving from a centrally-controlled to a self-organised complex system covering every road segment.This thesis studies the potential of VANETs in the context of traffic data collection and traffic management using a multidisciplinary approach. We combine knowledge of networking protocols, distributed systems, and traffic theory to propose novel solutions for TIS. First, we propose a TrafficEQ system---a VANET-based TIS, that uses pure V2V communication to collect and disseminate traffic information among vehicles. Moreover, TrafficEQ provides intelligent route guidance based on a probabilistic route choice strategy. The system is evaluated using a simulation platform developed with realistic real-world scenarios. We demonstrate that TrafficEQ deals with challenging non-predictable traffic congestion better than traditional route guidance systems can. Secondly, we propose grouping vehicles into communities by similarity of their mobility patterns to improve traffic congestion detection and analysis. To facilitate this, we present the Crowdz algorithm---a community detection algorithm for VANETs. Extensive analysis on large-scale vehicular networks show that detected communities are more stable than those in a competitive algorithm. Lastly, we introduce an application which uses communities to detect and analyse traffic congestion. Simulation experiments show that identification of congested communities can help in applying appropriate control strategies. [less ▲]

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