A decision support tool for vehicle infrastructure integration : understanding information effects and advancing data fusion algorithms for traffic management applications.
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A decision support tool for vehicle infrastructure integration : understanding information effects and advancing data fusion algorithms for traffic management applications.

Filetype[PDF-2.34 MB]


  • English

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    • OCLC Number:
      685138207
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    • NTL Classification:
      NTL-REFERENCES AND DIRECTORIES-Statistics ; NTL-PLANNING AND POLICY-PLANNING AND POLICY ;
    • Abstract:
      This research seeks to explore vehicle-to-vehicle information networks to understand the interplay between the information communicated and traffic conditions on the network. A longer-term goal is to develop a decision support tool for processing and storage of large amount of real-time (probe) data for advancing the state of the art in Vehicle Infrastructure Integration (VII). The fundamental concept in VII is that the (probe) vehicles serve as data collectors and anonymously transmit traffic information to transportation agencies to facilitate proactive strategies for traffic management and safety. The project develops new data fusion algorithms for travel time estimation and online stochastic routing which provides a clear representation of the benefit of information exchange between vehicles in VANETs. A Vehicular Ad Hoc Network (VANET) is composed of smart vehicles in which advanced wireless communication equipment are installed so that inter-vehicular communications are enabled. As a newly emerging paradigm of the Advanced Traveler Information Systems (ATIS), the VANET has received significant interest recently. There are many challenging research topics in this promising area, which can be grouped into two major categories. The first category includes the issues regarding the performance of the communication networks; for example, the network connectivity, the communication capacity, the broadcasting protocol, and so on. The second category comprises issues about traffic networks such as traffic data or information fusion, and online routing. This study focuses on building the information fusion framework to predict the short-term link travel time distribution with the real-time travel-time information provided by a VANET.
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