Information Impacts on Traveler Behavior and Network Performance: State of Knowledge and Future Directions

  • Ramachandran Balakrishna
  • Moshe Ben-Akiva
  • Jon Bottom
  • Song Gao
Part of the Complex Networks and Dynamic Systems book series (CNDS, volume 2)


Advanced Traveler Information Systems (ATIS) have the potential to maximize the operating efficiency of existing transportation infrastructure. Such systems rely on the generation and dissemination of guidance in order to allow drivers to make informed choices about travel mode, route and departure time, etc. The evaluation of the effectiveness of ATIS requires multidimensional study encompassing the analysis of various choice situations arising in the real world, constructing models that explain driver response to information in different contexts, and developing algorithms that can generate traveler information. Since driver confidence in the ATIS is directly related to the accuracy, relevance, and usefulness of the information, a key aspect is the collection of relevant field data that can instruct model development and ATIS evaluation before real-world deployment. This chapter aims to provide a synthesis of both the state of the art and the state of the practice of ATIS modeling and evaluation. We review the literature related to data collection and driver response model development, and classify the same according to the specific choice situations they address. We provide a conceptual discussion of the general framework within which traveler information may be generated, including key ATIS design parameters that may impact the performance of (and consequently, driver confidence in) the system. We also present brief empirical results from past simulation-based evaluations of ATIS, and conclude with recommendations for future research directions in order to further real-world ATIS deployment.


Global Position System Route Choice Reveal Preference Travel Information Travel Choice 
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 Science+Business Media New York 2013

Authors and Affiliations

  • Ramachandran Balakrishna
    • 1
  • Moshe Ben-Akiva
    • 2
  • Jon Bottom
    • 3
  • Song Gao
    • 4
  1. 1.Caliper CorporationNewtonUSA
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.Steer Davies & Gleave Inc.BostonUSA
  4. 4.University of Massachusetts at AmherstAmherstUSA

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