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

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

Abstract

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.

Keywords

Transportation Toll 

References

  1. Abdel-Aty M, Abdalla MF. Modeling drivers’ diversion from normal routes under ATIS using generalized estimating equations and binomial probit link function. Transportation 2004;31:327–348.CrossRefGoogle Scholar
  2. Abdel-Aty M, Abdalla MF. Examination of multiple mode/route-choice paradigms under ATIS. IEEE Trans Intell Transport Syst. 2006;7(3):332–348.CrossRefGoogle Scholar
  3. Antoniou C, Koutsopoulos HN, Ben-Akiva M, Chauhan AS, Cusack M. Development and evaluation of traffic diversion strategies. In: Working paper. 2004.Google Scholar
  4. Argiolu R. Office location choice behaviour and Intelligent Transport Systems. PhD thesis, Radboud University Nijmegen, 2008.Google Scholar
  5. Argiolu R, van der Heijden R, Bos I. Intelligent transport systems and preferences for office locations. Environ Plann. 2008;40:1744–1759.CrossRefGoogle Scholar
  6. Aultman-Hall L, Bowling S, Asher JC. ARTIMIS telephone travel information service: Current use patterns and user satisfaction. Transport Res Rec. 2000;1739;9–14.CrossRefGoogle Scholar
  7. Avineri E, Prashker JN. Sensitivity to travel time variability: Travelers’ learning perspective. Transport Res C 2005;13:157–183.CrossRefGoogle Scholar
  8. Avineri E, Prashker JN. The impact of travel time information on travelers’ learning under uncertainty. Transportation 2006;33:393–408.CrossRefGoogle Scholar
  9. Balakrishna R, Koutsopoulos HN, Ben-Akiva M. Evaluation of the estimation and prediction capability of a dynamic traffic assignment system. In: Mahmassani HS, editor. 16th international symposium on transportation and traffic theory. London: Elsevier; 2004.Google Scholar
  10. Balakrishna R, Koutsopoulos HN, Ben-Akiva M, Fernandez-Ruiz BM, Mehta M. Simulation-based evaluation of advanced traveler information systems. Transport Res Rec. 2005;1910:90–98.Google Scholar
  11. Barron G, Erev I. Small feedback-based decisions and their limited correspondence to description-based decisions. J Behav Dec Making 2003;16:215–233.CrossRefGoogle Scholar
  12. Ben-Akiva M, De Palma A, Kaysi I. Dynamic network models and driver information systems. Transport Res A 1991;25A(5):251–266.CrossRefGoogle Scholar
  13. Ben-Akiva M, Bottom J, Ramming MS. Route guidance and information systems. J Syst Contr Eng. 2001;215(14):317–324.Google Scholar
  14. Ben-Elia E, Erev I, Shiftan Y. The combined effect of information and experience on drivers’ route-choice behavior. Transportation 2008;35:165–177.CrossRefGoogle Scholar
  15. Bogers EAI. Traffic information and learning in day-to-day route choice. PhD thesis, Delft University of Technology, 2009.Google Scholar
  16. Bonsall P, Firmin P, Anderson M, Palmer I, Balmforth P. Validating the results of a route choice simulator. Transport Res C 1997;5:371–387.CrossRefGoogle Scholar
  17. Bottom J. Consistent anticipatory route guidance. PhD thesis, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, September 2000.Google Scholar
  18. Bottom J, Ben-Akiva M, Bierlaire M, Chabini I, Koutsopoulos HN, Yang Q. Investigation of route guidance generation issues by simulation with DynaMIT. In: Avishai Ceder, editor. 14th international symposium on transportation and traffic theory, pp. 577–600. Oxford: Pergamon; 1999.Google Scholar
  19. Boyce DE. Route guidance systems for improving urban travel and location choices. Transport Res A 1988;22:275–281.CrossRefGoogle Scholar
  20. Chatterjee K, McDonald M. Effectiveness of using variable message signs to disseminate dynamic traffic information: Evidence from field trials in European cities. Transport Rev. 2004;24(5):559–585.CrossRefGoogle Scholar
  21. Chen PS-T, Mahmassani HS. Reliability of real-time information systems for route choice decisions in a congested traffic network: Some simulation experiments. In: Proceedings of the vehicle navigation and information systems conference, pages 849–856, 1991.Google Scholar
  22. Chiu Y-C, Mahmassani HS. Hybrid real-time dynamic traffic assignment approach for robust network performance. Transport Res Rec. 2002;89–97.Google Scholar
  23. Chorus CG, Walker JL, Ben-Akiva ME. The value of travel information: A search-theoretic approach. J Intell Transport Syst. 2011;14:154–165.CrossRefGoogle Scholar
  24. Dai H. An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transport Res C 2002;10:331–349.CrossRefGoogle Scholar
  25. Denant-Boemont L, Petiot R. Information value and sequential decision-making in a transport settting: An experimental study. Transport Res B 2003;37:365–386.CrossRefGoogle Scholar
  26. Doan DL, Ziliaskopoulos A, Mahmassani H. On-line monitoring system for real-time traffic management applications. Transport Res Rec. 1999;1678:142–149.CrossRefGoogle Scholar
  27. Dziekan K, Kottenhoff K. Dynamic at-stop real-time information displays for public transport: Effects on customers. Transport Res A 2007;41:489–501.Google Scholar
  28. Emmerink RHM, Axhausen KW, Nijkamp P, Rietveld P. The potential of information provision in a simulated road transport network with non-recurrent congestion. Transport Res C 1995a;3(5):293–309.CrossRefGoogle Scholar
  29. Emmerink RHM, Nijkamp P, Rietveld P, van Ommeren JN. Variable message signs and radio traffic information: An integrated empirical analysis of drivers’ route choice behavior. Transport Res A 1995b;30(2):135–153.Google Scholar
  30. Florian D, Balakrishna R, Ben-Akiva M, Wen Y. Evaluation of on-line dynamic traffic assignment using micro-simulation. In: Proc. second international symposium of transport simulation, 2006.Google Scholar
  31. Freundschuh SM. Is there a relationship between spatial cognition and environmental patterns?, vol 639 of Lecture Notes in Computer Science. New York: Springer; 1992.Google Scholar
  32. Gao S, Chabini I. Optimal routing policy problems in stochastic time-dependent networks. Transport Res B 2006;40(2):93–122.CrossRefGoogle Scholar
  33. Gao S, Huang H. Real-time traveler information for optimal adaptive routing in stochastic time-dependent networks. Transport Res C 2011. doi:10.1016/j.trc.2011.09.007.Google Scholar
  34. Hall RW. The fastest path through a network with random time-dependent travel times. Transport Sci. 1986;20(3):91–101.CrossRefGoogle Scholar
  35. Henk RH, Kuhn BT. Assessing the effectiveness of advanced traveler information on older driver travel behavior and mode choice. Technical report, Texas Transportation Institute, 2000.Google Scholar
  36. Huynh N, Chiu Y-C, Mahmassani HS. Finding near-optimal locations for variable message signs for real-time network traffic management. Transport Res Rec. 2003;1856:34–53.Google Scholar
  37. Iida Y, Akiyama T, Uchida T. Experimental analysis of dynamic route choice behavior. Transport Res B 1992;26(1):17–32.CrossRefGoogle Scholar
  38. Jayakrishnan R, Mahmassani HS, Hu Y. An evaluation tool for advanced traffic information and management systems in urban networks. Transport Res C 1994;2(3):129–147.CrossRefGoogle Scholar
  39. Jayakrishnan R, Oh J-S, Sahraoui A-E-K. Calibration and path dynamics issues in microscopic simulation for advanced traffic management and information systems. Transport Res Rec. 2001;1771:9–17.CrossRefGoogle Scholar
  40. Jou R-C. Modeling the impact of pre-trip information on commuter departure time and route choice. Transport Res B 2001;35:887–902.CrossRefGoogle Scholar
  41. Kaysi I, Ben-Akiva M, Koutsopoulos HN. Integrated approach to vehicle routing and congestion prediction for real-time driver guidance. Transport Res Rec. 1993;1408:66–74.Google Scholar
  42. Kaysi I, Ben-Akiva M, de Palma A. Design aspects of advanced traveler information systems. In: Gartner NH, Improta G, editors. Urban traffic networks: dynamic flow modeling and control. New York: Springer; 1995. pp. 59–81.CrossRefGoogle Scholar
  43. Kaysi IA. Framework and models for the provision of real-time driver information. PhD thesis, Department of Civil Engineering, Massachusetts Institute of Technology, February 1992.Google Scholar
  44. Kenyon S, Lyons G. The value of integrated multimodal traveller information and its potential contribution to modal change. Transport Res F 2003;6:1–21.CrossRefGoogle Scholar
  45. Khattak A, Yim Y, Stalker L. Does travel information influence commuter and noncommuter behavior? Results from the San Francisco Bay Area TravInfo project. Transport Res Rec. 1999;1694:48–58.CrossRefGoogle Scholar
  46. Khattak A, Yim Y, Prokopy LS. Willingness to pay for travel information. Transport Res C 2003;11:137–139.CrossRefGoogle Scholar
  47. Khattak AJ, Kanafani A, Le Colletter E. Stated and reported route diversion behavior: implications of benefits of advanced traveler information systems. Transport Res Rec. 1994;1464:28–35.Google Scholar
  48. Kraan M, Mahmassani HS, Huynh N. Interactive survey approach to study traveler responses to ATIS for shopping trips. In: 79th TRB Annual Meeting CD-ROM, 2000.Google Scholar
  49. Lappin J, Bottom J. Understanding and predicting traveler response to information: A literature review. Technical report, Prepared for USDOT, FHWA, 2001.Google Scholar
  50. Lee DB. Benefit-cost evaluation of traveler information: Seattle’s Washington State Department of Transportation website. Transport Res Rec. 2000;1739:25–34.CrossRefGoogle Scholar
  51. Levinson D. The value of advanced traveler information systems for route choice. Transport Res C 1999;11.Google Scholar
  52. Lu, X, Gao, S and Ben-Elia, E. Information Impacts on Route Choice and Learning Behavior in a Congested Network: An Experimental Approach. Transport Res Rec. 2011; 2243:89–98.CrossRefGoogle Scholar
  53. Mahmassani H, Chang GL. Experiments with departure time choice dynamics of urban commuters. Transport Res B 1986;20(4):297–320.CrossRefGoogle Scholar
  54. Mahmassani H, Liu YH. Dynamics of commuting decision behaviour under advanced traveller information systems. Transport Res C 1999;7:91–107.CrossRefGoogle Scholar
  55. Mahmassani HS, Jayakrishnan R. System performance and user response under real-time information in a congested traffic corridor. Transport Res A 1991;25(5):293–307.CrossRefGoogle Scholar
  56. Mahmassani HS, Huynh NN, Srinivasan K, Kraan M. Tripmaker choice behavior for shopping trips under real-time information: Model formulation and results of stated-preference internet-based interactive experiments. J Retailing Consum Serv. 2003;10:311–321.CrossRefGoogle Scholar
  57. Martin PT, Lahon D, Cook K, Stevanovic A. Traveler information systems: Evaluation of UDOT’s ATIS techologies. Technical report, University of Utah, 2005.Google Scholar
  58. Mehndiratta SR, Kemp M, Pierce S, Lappin J. Users of a regional telephone-based traveler information system - a study of TravInfo users in the San Franciso Bay Area. Transportation 2000a;27:391–417.CrossRefGoogle Scholar
  59. Mehndiratta SR, Kemp MA, Lappin JE, Nierenberg E. Likely users of advanced traveler information systems: Evidence from the Seattle region. Transport Res Rec. 2000b;1739:15–24.CrossRefGoogle Scholar
  60. Miller M. TravInfo evaluation: Traveler information center study. Technical Report UCB-ITS-PWP-98-21, University of California, Berkeley, 1998.Google Scholar
  61. Miller-Hooks ED, Mahmassani HS. Least expected time paths in stochastic, time-varying transportation networks. Transport Sci. 2000;34(2):198–215.CrossRefGoogle Scholar
  62. Molin EJE, Timmermans HJP. Traveler expectations and willingness-to-pay for web-enabled public transport information services. Transport Res C 2006;14:57–67.CrossRefGoogle Scholar
  63. Murray PM, Mahmassani HS, Abdelghany KF. Methodology for assessing high-occupancy toll-lane usage and network performance. Transport Res Rec. 2001;1765:8–15.CrossRefGoogle Scholar
  64. Papageorgiou M, Ben-Akiva M, Bottom J, Bovy PHL, Hoogendoorn SP, Hounsell NB, Kotsialos A, McDonald M. Its and traffic management. In: Barnhart C, Laporte G, editors. Transportation, vol 14 of Handbooks in operations research and management science. London: Elsevier; 2007. pp. 715–774.Google Scholar
  65. Papinski D, Scott DM, Doherty ST. Exploring the route choice decision-making process: A comparison of planned and observed routes obtained using person-based GPS. Transport Res F 2009;12:347–358.CrossRefGoogle Scholar
  66. Paz A, Peeta S. Information-based network control strategies consistent with estimated driver behavior. Transport Res B 2009a;43(1):73–96.CrossRefGoogle Scholar
  67. Paz A, Peeta S. On-line calibration of behavior parameters for behavior-consistent route guidance. Transport Res B 2009b;43(4):403–421.CrossRefGoogle Scholar
  68. Paz A, Peeta S. Behavior-consistent real-time traffic routing under information provision. Transport Res C 2009c;17(6):642–661.CrossRefGoogle Scholar
  69. Petrella M, Lappin J. Comparative analysis of customer response to online traffic information in two cities: Los Angeles, California and Seattle, Washington. Transport Res Rec. 2004;1886:10–17.Google Scholar
  70. Polychronopoulos G, Tsitsiklis JN. Stochastic shortest path problems with recourse. Networks 1996;27:133–143.CrossRefGoogle Scholar
  71. Polydoropoulou A. Modeling user response to advanced traveler information systems (ATIS). PhD thesis, Massachusetts Institute of Technology, Cambridge, MA, 1997.Google Scholar
  72. Polydoropoulou A, Ben-Akiva M. The effect of advanced traveller information systems (ATIS) on travellers’ behaviour, pages 317–352. Ashgate, 1999.Google Scholar
  73. Polydoropoulou A, Ben-Akiva M, Khattak A, Lauprete G. Modeling revealed and stated en-route travel response to advanced traveler information systems. Transport Res Rec. 1996;1537:38–45.CrossRefGoogle Scholar
  74. Pretolani D. A directed hyperpath model for random time dependent shortest paths. Eur J Oper Res. 2000;123:315–324.CrossRefGoogle Scholar
  75. Ramming MS. Network knowledge and route choice. PhD thesis, Massachusetts Institute of Technology, 2002.Google Scholar
  76. Rathi V, Antoniou C, Wen Y, Ben-Akiva M, Cusack M. Assessment of the impact of dynamic prediction-based route guidance using a simulation-based, closed-loop framework. In: 87th annual meeting of the Transportation Research Board, 2008.Google Scholar
  77. Razo M, Gao S. Strategic thinking and risk attitudes in route choice: A stated preference approach. Transport Res Rec. 2010;2085:136–143.Google Scholar
  78. Razo M, Gao S. A rank-dependent expected utility model for strategic route choice with stated preference data. Transport Res C 2011. doi:10.1016/j.trc.2011.08.009.Google Scholar
  79. Richards A, McDonald M. Questionnaire surveys to evaluate user response to variable message signs in an urban network. IET Intell Transport Syst. 2007;1(3):177–185.CrossRefGoogle Scholar
  80. Rodriguez DA, Levine J, Agrawal AW, Song J. Can information promote transportation-friendly location decisions? A simulation experiment. J Transport Geogr. 2011;19:304–312.CrossRefGoogle Scholar
  81. Saricks CL, Schofer JL, Soot S, Belella PA. Evaluating effectiveness of real-time advanced traveler information systems using a small test vehicle fleet. Transport Res Rec. 1997;1588:41–48.CrossRefGoogle Scholar
  82. Selten R, Chmura T, Pitz T, Kube S, Schreckenberg M. Commuters route choice behaviour. Games Econ Behav. 2007;58(2):394–406.CrossRefGoogle Scholar
  83. Shah VP, Wunderlich K, Larkin J. Time management impacts of pretrip advanced traveler information systems: Findings from a Washington DC, case study. Transport Res Rec. 2001;1774:36–43.CrossRefGoogle Scholar
  84. Smith SA, Perez C. Evaluation of INFORM: Lessons learned and application to other systems. Transport Res Rec. 1992;1360:62–65.Google Scholar
  85. Srinivasan KK, Mahmassani H. Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS: A dynamic kernel logit formulation. Transport Res B 2003;37:793–814.CrossRefGoogle Scholar
  86. Stephanedes YJ, Kwon E, Michalopoulos P. Demand diversion for vehicle guidance, simulation, and control in freeway corridors. Transport Res Rec. 1989;1220:12–20.Google Scholar
  87. Sun Z. Travel information impact on activity-travel patterns. PhD thesis, Eindhoven University of Technology, 2006.Google Scholar
  88. Tian H, Gao S, Fisher DL, Post B. Route choice behavior in a driving simulator with real-time information. In: 90th TRB Annual Meeting DVD, 2011.Google Scholar
  89. Tsirimpa A. Development of a simulation model of individuals activity travel patterns in an information rich environment. PhD thesis, University of Aegean, 2010.Google Scholar
  90. Tsirimpa A, Polydoropoulou A, Antoniou C. Development of a mixed multi-normal logit model to capture the impact of information systems on travelers’ switching behavior. J Intell Transport Syst. 2007;11(2):79–89.CrossRefGoogle Scholar
  91. Uchida T, Iida Y, Nakahara M. Panel survey on drivers’ route choice behavior under travel time information. In: IEEE vehicle navigation and information systems conference proceedings, pp. 383–388, 1994.Google Scholar
  92. Waller ST, Ziliaskopoulos AK. On the online shortest path problem with limited arc cost dependencies. Networks 2002;40(4):216–227.CrossRefGoogle Scholar
  93. Wolinetz LD, Khattak A. Why will some individuals pay for travel information when it can be free? Analysis of a Bay Area traveler survey. Transport Res Rec. 2004;1759:9–18.CrossRefGoogle Scholar
  94. Wunderlich K, Hardy M, Larkin J, Shah V. On-time reliability impacts of advanced traveler information services (ATIS). Technical report, Mitretek Systems, 2001.Google Scholar
  95. Yang Q, Koutsopoulos HN, Ben-Akiva ME. Simulation laboratory for evaluating dynamic traffic management systems. Transport Res Rec. 2000;1710:122–130.CrossRefGoogle Scholar
  96. Yim Y, Hall R, Koo R, Miller M. TravInfo 817-1717 caller study. In: 78th TRB Annual Meeting CD-ROM, 1999.Google Scholar
  97. Yim Y, Khattak A, Raw J. Traveler response to new dynamic information sources: Analyzing corridor and areawide behavioral surveys. Transport Res Rec. 2002;1803:66–75.Google Scholar
  98. Ziegelmeyer A, Koessler F, My KB, Denant-Boemont L. Road traffic congestion and public information: An experimental investigation. J Transport Econ Pol. 2008;42:43–82.Google Scholar

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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