Now showing 1 - 3 of 3
  • Publication
    Next Road Rerouting: A Multiagent System for Mitigating Unexpected Urban Traffic Congestion
    During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.
    Scopus© Citations 106  672
  • Publication
    Comprehensive performance analysis and comparison of vehicles routing algorithms in smart cities
    Due to the severe impact of road traffic congestion on both economy and environment, several vehicles routing algorithms have been proposed to optimize travelers itinerary based on real-time traffic feeds or historical data. However, their evaluation methodologies are not as compelling as their key design idea because none of them had been tested under both real transportation map and real traffic data. In this paper, we conduct a deep performance analysis and comparison of four typical vehicles routing algorithms under various scalability levels (i.e. trip length and traffic load) based on realistic transportation simulation. The ultimate goal of this work is to suggest the most suitable routing algorithm to use in different transportation scenarios, so that it can provide a valuable reference for both traffic managers and researchers when they deploy or optimize a large scale centralized Traffic Management System (TMS). The obtained simulation results reveal that dynamic A* is the best routing algorithm if the TMS has sufficient memory or storage capacities, otherwise static A* is also a great alternative.
    Scopus© Citations 16  321
  • Publication
    An Adaptive and VANETs-based Next Road Re-routing System for Unexpected Urban Traffic Congestion Avoidance
    Unexpected road traffic congestion caused by en-route events, such as car crashes, road works, unplanned parades etc., is a real challenge in today's urban road networks as it considerably increases the drivers' travel time and decreases travel time reliability. To face this challenge, this paper extends our previous work named Next Road Rerouting (NRR) by designing a novel vehicle rerouting strategy that can adapt itself to the sudden change of urban road traffic conditions. This is achieved through a smart calibration of the algorithmic and operational parameters of NRR without any intervention from traffic managers. Specifically, a coefficient of variation based method is used to assign weight values to three factors in the routing cost function of NRR, and the k-means algorithm is applied periodically to choose the number of NRR enabled agents needed. This adaptive-NRR (a-NRR) strategy is supported by vehicular ad-hoc networks (VANETs) technology as this latter can provide rich traffic information at much higher update frequency and much larger coverage than induction loops used in the previously proposed static NRR. Simulation results show that in the city center area of TAPASCologne scenario, compared to the existing vehicle navigation system (VNS) and static NRR, our adaptive-NRR can achieve considerable gain in terms of trip time reduction and travel time reliability improvement.
    Scopus© Citations 21  391