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Modeling and Experimenting with Vehicular Congestion for Distributed Advanced Traveler Information Systems

  • Marco Roccetti
  • Gustavo Marfia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6342)

Abstract

Advanced Traveler Information Systems, which have for long been regarded as one of the most promising future applications of wireless vehicular networks for use in the field of Intelligent Transportation Systems (ITS), are effectively becoming part of today’s reality. Many drivers already access the information provided by such systems, for example checking for the state of the streets along a given route or reading traffic jam alerts on the displays of smart-phones or Personal Navigation Devices (PNDs). Based on such information, drivers, or their PNDs, select the best paths to reach their destinations. Clearly, in order to be effective, such systems are required to reliably estimate and forecast vehicular congestion states. Moreover, they should also be capable of efficiently utilizing the wireless channel resources, as the amount of information that may be exchanged by such systems in dense urban areas grows with the number of services supported by the onboard devices and the amount of vehicles that install them. To answer these challenges, we here discuss how a distributed ATIS can: a) implement an effective vehicular congestion detection and forecasting model, and, b) efficiently disseminate traffic information. The advantage of distributing an ATIS is that each vehicle can compute and redistribute accurate vehicular congestion information very rapidly, with little overhead and without resorting to a central entity. In order to validate our approach, we present the outcomes of a real world experimentation, as well as of multiple simulations.

Keywords

Road Section Vehicular Network Congestion State Broadcast Algorithm Traversal Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marco Roccetti
    • 1
  • Gustavo Marfia
    • 1
  1. 1.Computer Science DepartmentUniversity of BolognaBolognaItaly

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