Advertisement

GeoInformatica

, Volume 10, Issue 4, pp 469–493 | Cite as

GABRIEL: Gis Activity-Based tRavel sImuLator. Activity Scheduling in the Presence of Real-Time Information

  • Mei-Po Kwan
  • Irene CasasEmail author
Article

Abstract

A series of travel simulators have been developed in the past two decades under the Intelligent Transportation Systems (ITS) umbrella. They have addressed issues such as reactions to advisory radio and variable message signs, use of navigation systems, route diversion, and mode choice. The objective of this paper is to present the design and implementation of a different kind of travel simulator. GABRIEL (Gis Activity-Based tRavel sImuLator) has as a foundation the activity-based approach and makes use of geographic information systems (GIS) as a development environment. The simulation scenario consists of a commute trip where two activities take place. En-route to the first destination, congestion occurs and subjects are requested to take action based on a set of alternatives. The simulator provides re-routing, destination substitution, dynamic geographic information and real-time information to aid users in their decision-making process. As a result it helps subjects in developing their ability to adapt given a particular scenario and allow researchers in understanding trip making, activity rescheduling, and the decision-making process from a comprehensive perspective.

Keywords

GIS travel simulator activity-based approach real-time information 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M.A. Abdel-Aty, R. Kitamura, and P.P. Jovanis. “Exploring route choice behavior using geographic information system-based alternative routes and hypothetical travel time information input,” Transportation Record, Vol. 1493:74–80, 1995.Google Scholar
  2. 2.
    J.E. Abraham and J.D. Hunt. “Specification and estimation of nested logit model of home, workplaces, and commuter mode choices by multiple-worker household,” Transportation Research Record, Vol. 1606:17–24, 1997.Google Scholar
  3. 3.
    J. Adler, W.W. Recker, and M.G. McNally. “A conflict model and interactive simulator (FASTCARS) for predicting enroute driver behavior in response to real-time traffic condition information,” Transportation, Vol. 20:83–106, 1993.CrossRefGoogle Scholar
  4. 4.
    J.L. Adler and V.J. Blue. “Toward the design of intelligent traveler information systems,” Transportation Research. Part C, Emerging Technologies, Vol. 6:157–172, 1998.CrossRefGoogle Scholar
  5. 5.
    J.L. Adler and M.G. McNally. “In laboratory experiments to investigate driver behavior under advanced traveler information systems,” Transportation Research. Part C, Emerging Technologies, Vol. 2:140–164, 1994.CrossRefGoogle Scholar
  6. 6.
    H.M. Al-Deek, A.J. Khattak, and P. Thananjeyan. “A combined traveler behavior and system performance model with advanced traveler information systems,” Transportation Research. Part A, Vol. 32:479–493, 1998.Google Scholar
  7. 7.
    R.W. Allen, D. Ziedman, T.J. Rosenthal, A.C. Stein, J.F. Torres, and A. Halati. “Laboratory assessment of driver route diversion in response to in-vehicle navigation and motorist information systems,” Transportation Research Record, Vol. 1306:82–91, 1991.Google Scholar
  8. 8.
    P.W. Bonsall, “Route choice simulators,” in R. Selten (Ed.), Human Behaviour and Traffic Networks. Berlin Heidelberg New York: Springer, 2004.Google Scholar
  9. 9.
    P. Bonsall and T. Parry. “Using an interactive route–choice simulator to investigate drivers’ compliance with route guidance advice,” Transportation Research Record, Vol. 1306:59–68, 1991.Google Scholar
  10. 10.
    P. Bonsall, P. Firmin, M. Anderson, I. Palmer, and P. Balmforth. “Validating the results of a route choice simulator,” Transportation Research. Part C, Emerging Technologies, Vol. 5:371–387, 1997.CrossRefGoogle Scholar
  11. 11.
    D.E. Boyce. “Route guidance systems for improving urban travel and location choices,” Transportation Research. Part A, General, Vol. 22A:275–2811, 1988.CrossRefGoogle Scholar
  12. 12.
    I. Casas. “Evaluating the importance of accessibility to congestion response using a GIS-based travel simulator,” Journal of Geographical Systems, Vol. 5:109–127, 2003.CrossRefGoogle Scholar
  13. 13.
    P. Chen and H. Mahmassani. “Dynamic interactive simulator for studying commuter behavior under real-time traffic information supply strategies,” Transportation Research Record, Vol. 1413:12–21, 1993.Google Scholar
  14. 14.
    M. Choy, M.-P. Kwan, and H. V. Leong. “Distributed database design for mobile geographical applications,” Journal of Database Management, Vol. 11:3–17, 2000.Google Scholar
  15. 15.
    W.A.V. Clark and T.R. Smith, “Production system models of residential search behavior: A comparison of behavior in computer-simulated and real-world environments,” Environment and Planning A, Vol. 17:555–568, 1985.CrossRefGoogle Scholar
  16. 16.
    H. Dia. “An agent-based approach to modelling driver route choice behaviour under the influence of real-time information,” Transportation Research. Part C, Emerging Technologies, Vol. 10:331–349, 2002.CrossRefGoogle Scholar
  17. 17.
    D. Ettema. Activity-Based Travel Demand Modeling. Eindhoven, The Netherlands: Eindhoven University of Technology, 1996.Google Scholar
  18. 18.
    T. Gärling, M.-P. Kwan, and R.G. Golledge. “Computation-process modelling of household activity scheduling,” Transportation Research. Part B, Methodological, Vol. 28B:355–364, 1994.CrossRefGoogle Scholar
  19. 19.
    R. Golledge and R.J. Stimson. Spatial Behavior a Geographic Perspective. New York: Guilford, 1997.Google Scholar
  20. 20.
    R. Golledge, V. Dougherty, and S. Bell. “Acquiring spatial knowledge: Survey versus route-based knowledge in unfamiliar environments,” Annals of the AAG, Vol. 85:134–158, 1995.Google Scholar
  21. 21.
    R.G. Golledge, A.J. Ruggles, J.W. Pellegrino, and N.D. Gale. “Integrating route knowledge in an unfamiliar neighborhood: along and across route experiments,” Journal of Environmental Psychology, Vol. 13:293–307, 1993.CrossRefGoogle Scholar
  22. 22.
    S. Gopal, R.L. Klatzky, and T.R. Smith. “NAVIGATOR: a psychologically based model of environmental learning through navigation,” Journal of Environmental Psychology, Vol. 9:309–331, 1989.CrossRefGoogle Scholar
  23. 23.
    S. Hanson. “Off the road? Reflections on transportation geography in the information age,” Journal of Transport Geography, Vol. 6:241–249, 1998.CrossRefGoogle Scholar
  24. 24.
    K.E. Haynes, W.M. Bowen, C.R. Arieira, S. Burhans, P.L. Salem, and H. Shafie. “Intelligent transportation systems benefit priorities: an application to the Woodrow Wilson bridge,” Journal of Transport Geography, Vol. 8:129–139, 2000.CrossRefGoogle Scholar
  25. 25.
    D. Hernández. Qualitative Representation of Spatial Knowledge. Berlin Heidelberg New York: Springer, 1994.CrossRefGoogle Scholar
  26. 26.
    D. Hodge and H. Koski. “Information and communication technologies and transportation: European–US collaborative and comparative research possibilities,” Journal of Transport Geography, Vol. 5:191–197, 1997.CrossRefGoogle Scholar
  27. 27.
    M. Iguchi. “A perspective on ITS deployment,” JSAE Review, Vol. 23:173–176, 2002.CrossRefGoogle Scholar
  28. 28.
    Y. Iida, T. Akiyama, and T. Uchida. “Experimental analysis of dynamic route choice behavior,” Transportation Research. Part B, Methodological, Vol. 26B:17–32, 1992.CrossRefGoogle Scholar
  29. 29.
    I. Kaysi, M. Ben-Akiva, and H. Koutsopoulos. “An integrated approach to vehicle routing and congestion prediction for real-time driver guidance,” presented at Transportation Research Board 72nd Annual Meeting, Washington DC, 1993.Google Scholar
  30. 30.
    A.J. Khattak, J.L. Schofer, and F.S. Koppelman. “Commuters’ enroute diversion and return decisions analysis and implications for advanced traveler information systems,” Transportation Research. Part A, General, Vol. 27:101–111, 1993.Google Scholar
  31. 31.
    R. Kitamura, K. Nishii, and K. Goulias. “Trip chaining behaviour by central city commuters: a causal analysis of time–space constraints,” in P. Jones (Ed.), Developments in Dynamic and Activity-Based Approaches to Travel Analysis. Brookfield: Gower, 1990.Google Scholar
  32. 32.
    R. Kitamura, E.I. Pas, C.V. Lula, T.K. Lawton, and P.E. Benson. “The sequenced activity mobility simulator (SAMS): an integrated approach to modeling transportation, land use and air quality,” Transportation, Vol. 23:267–291, 1996.CrossRefGoogle Scholar
  33. 33.
    K. Kitamura, R.M. Pendyala, E.I. Pas, and D.V.G.P. Reddy. “Application of AMOS, an activity-based TCM evaluation tool to the Washington, D.C. metropolitan area,” presented at Transportation Planning Methods, PTRC European Transport Forum, University of Warwick, England, 1995.Google Scholar
  34. 34.
    H.N. Koutsopoulos, T. Lotan, and Q. Yang. “A driving simulator and its application for modeling route choice in the presence of information,” presented at Transportation Research Board 72nd Annual Meeting, Washington, DC, 1993.Google Scholar
  35. 35.
    H. Koutsopoulos, A. Polydoropoulou, and M. Ben-Akiva. “Traveler simulators for data collection on driver behavior in presence of information,” Transportation Research. Part C, Emerging Technologies, Vol. 3:143–159, 1995.CrossRefGoogle Scholar
  36. 36.
    B. Kuipers. “Modeling spatial knowledge,” Cognitive Science, Vol. 2:129–153, 1978.CrossRefGoogle Scholar
  37. 37.
    M.-P. Kwan. “GISICAS: an activity-based travel decision support system using a GIS-interfaced computational-process model,” in D.F. Ettema and H.J.P. Timmermans (Eds.), Activity-Based Approaches to Travel Analysis. New York: Pergamon, 263–282, 1997.Google Scholar
  38. 38.
    E. Lang, K.-U. Cartensen, and G. Simmons. “Modelling spatial knowledge on a linguistic basis. Theory – prototype – integration,” Springer Lecture Notes in Artificial Intelligence, Vol. 481, New York: Springer–Verlag, 1991.Google Scholar
  39. 39.
    D. Leiser and A. Zilberschatz. “The TRAVELLER: a computational model of spatial network learning,” Environment and Behavior, Vol. 21:435–463, 1989.Google Scholar
  40. 40.
    Y.-C. Liu. “Effect of advanced traveler information system displays on younger and older drivers’ performance,” Displays, Vol. 21:161–168, 2000.CrossRefGoogle Scholar
  41. 41.
    J.P. Löwenau, P.J.T. Venhovens, and J.H. Bernasch. “Advanced vehicle navigation applied in the BMW real time light simulation,” presented at Telematics Automotive, Birmingham, 1999.Google Scholar
  42. 42.
    A.J. May, T. Ross, and S.H. Bayer. “Drivers’ information requirements when navigating in an urban environment,” The Journal of Navigation, Vol. 56:89–100, 2003.CrossRefGoogle Scholar
  43. 43.
    G.I. McCalla, L. Reid, and P.K. Schneider. “Plan creation, plan execution and knowledge execution in dynamic micro world,” International Journal of Man–Machine Studies, Vol. 16:89–112, 1982.CrossRefGoogle Scholar
  44. 44.
    K.C. Mouskos and J. Greenfeld. “A GIS-based multimodal advanced traveler information system,” Computer-Aided Civil and Infrastructure Engineering, Vol. 14:267–279, 1999.CrossRefGoogle Scholar
  45. 45.
    K.E. Nygard. “Computing and modeling issues in wide-area advanced traveler information systems,” Mathematical and Computer Modelling, Vol. 22:431–437, 1995.CrossRefGoogle Scholar
  46. 46.
    J.W. Payne, J.R. Bettman, E. Coupey, and E.J. Johnson. “A constructive process view of decision making: multiple strategies in judgment and choice,” Acta Psychologica, Vol. 80:107–141, 1992.CrossRefGoogle Scholar
  47. 47.
    J. Polak and P. Jones. “The acquisition of pre-trip information: a stated preference approach,” Transportation, Vol. 20:179–198, 1993.CrossRefGoogle Scholar
  48. 48.
    W.W. Recker, M.G. McNally, and G.S. Root. “A model of complex travel behavior: Part 1—theoretical development,” Transportation Research. Part A, General, Vol. 20A:307–318, 1986.CrossRefGoogle Scholar
  49. 49.
    J.L. Schofer, A. Khattak, and F. Koppelman. “Behavioral issues in the design and evaluation of advanced traveler information systems,” Transportation Research. Part C, Emerging Technologies, Vol. 1:101–117, 1993.CrossRefGoogle Scholar
  50. 50.
    T.R. Smith, W.A.V. Clark, and J.W. Cotton. “Deriving and testing production system models of sequential decision-making behavior,” Geographical Analysis, Vol. 16:191–222, 1984.CrossRefGoogle Scholar
  51. 51.
    P.R. Stopher, D.T. Hartgen, and Y. Li. “SMART: simulation model for activities, resources and travel,” Transportation, Vol. 23:293–312, 1996.CrossRefGoogle Scholar
  52. 52.
    R. Valdez and C. Arce. “Comparison of travel behavior and attitudes of ridesharers, solo drivers, and the general commuter population,” Transportation Research Record, Vol. 1285:105–108, 1990.Google Scholar
  53. 53.
    K.M. Vaughn, M.A. Abdel-Aty, R. Kitamura, P.P. Jovanis, H. Yang, N.E.A. Kroll, R. B. Post, and B. Oppy. “Experimental analysis and modeling of sequential route choice under an advanced traveler information system in a simplistic traffic network,” Transportation Research Record, Vol. 1408:75–82, 1993.Google Scholar
  54. 54.
    J. Wahle, A.L.C. Bazzan, F. Klügl, and M. Schreckenberg. “The impact of real-time information in a two route scenario using agent-based simulation,” Transportation Research. Part C, Emerging Technologies, Vol. 10:399–417, 2002.CrossRefGoogle Scholar
  55. 55.
    N.J. Ward. “Automation of task processes: an example of intelligent transportation systems,” Human Factors and Ergonomics in Manufacturing, Vol. 10:395–408, 2000.CrossRefGoogle Scholar
  56. 56.
    Y. Yim. “The effects of traffic information on traveler behavior,” University of California at Berkeley, ITS/PATH, June 1996.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Department of GeographyThe Ohio State UniversityColumbusUSA
  2. 2.Department of GeographyUniversity at Buffalo, State University of New YorkBuffaloUSA

Personalised recommendations