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Advanced traveller information systems under recurrent traffic conditions: Network equilibrium and stability

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  • Bifulco, Gennaro N.
  • Cantarella, Giulio E.
  • Simonelli, Fulvio
  • Velonà, Pietro

Abstract

In this paper the stability of traffic equilibrium is analysed by using a framework where advanced traveller information systems (ATIS) are explicitly modelled. The role played by information in traffic networks is discussed, with particular reference to the day-to-day dynamics of the traffic network and to system stability at equilibrium. The perspective adopted is that of transportation planning under recurrent network conditions. The network is considered to be in equilibrium, viewed as a fixed-point state of a day-to-day deterministic process, here modelled as a time-discrete non-linear Markovian dynamic system. In discussing the effects generated by the introduction of ATIS, the paper examines: changes in the fixed point(s) with respect to the absence of ATIS, how the theoretical conditions for fixed-point existence and uniqueness are affected, and the impact on the stability properties and the stability region at equilibrium. Most of the analyses are carried out with explicit theoretical considerations. Moreover, a toy network is also employed to explore numerically the effects of removing some assumptions concerning the accuracy of ATIS.

Suggested Citation

  • Bifulco, Gennaro N. & Cantarella, Giulio E. & Simonelli, Fulvio & Velonà, Pietro, 2016. "Advanced traveller information systems under recurrent traffic conditions: Network equilibrium and stability," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 73-87.
    • Handle: RePEc:eee:transb:v:92:y:2016:i:pa:p:73-87
    DOI: 10.1016/j.trb.2015.12.008
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    1. Chorus, Caspar G. & Arentze, Theo A. & Molin, Eric J.E. & Timmermans, Harry J.P. & Van Wee, Bert, 2006. "The value of travel information: Decision strategy-specific conceptualizations and numerical examples," Transportation Research Part B: Methodological, Elsevier, vol. 40(6), pages 504-519, July.
    2. Lindsey, Robin & Daniel, Terry & Gisches, Eyran & Rapoport, Amnon, 2014. "Pre-trip information and route-choice decisions with stochastic travel conditions: Theory," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 187-207.
    3. Olaf Jahn & Rolf H. Möhring & Andreas S. Schulz & Nicolás E. Stier-Moses, 2005. "System-Optimal Routing of Traffic Flows with User Constraints in Networks with Congestion," Operations Research, INFORMS, vol. 53(4), pages 600-616, August.
    4. Yang, Hai, 1998. "Multiple equilibrium behaviors and advanced traveler information systems with endogenous market penetration," Transportation Research Part B: Methodological, Elsevier, vol. 32(3), pages 205-218, April.
    5. Lo, Hong K. & Szeto, W. Y., 2004. "Modeling advanced traveler information services: static versus dynamic paradigms," Transportation Research Part B: Methodological, Elsevier, vol. 38(6), pages 495-515, July.
    6. André de Palma & Robin Lindsey & Nathalie Picard, 2012. "Risk Aversion, the Value of Information, and Traffic Equilibrium," Transportation Science, INFORMS, vol. 46(1), pages 1-26, February.
    7. Eran Ben-Elia & Erel Avineri, 2015. "Response to Travel Information: A Behavioural Review," Transport Reviews, Taylor & Francis Journals, vol. 35(3), pages 352-377, May.
    8. Paz, Alexander & Peeta, Srinivas, 2009. "On-line calibration of behavior parameters for behavior-consistent route guidance," Transportation Research Part B: Methodological, Elsevier, vol. 43(4), pages 403-421, May.
    9. Yang, Hai & Meng, Qiang, 2001. "Modeling user adoption of advanced traveler information systems: dynamic evolution and stationary equilibrium," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(10), pages 895-912, December.
    10. Al-Deek, Haitham M. & Khattak, Asad J. & Thananjeyan, Paramsothy, 1998. "A combined traveler behavior and system performance model with advanced traveler information systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(7), pages 479-493, September.
    11. Erel Avineri & Joseph Prashker, 2006. "The Impact of Travel Time Information on Travelers’ Learning under Uncertainty," Transportation, Springer, vol. 33(4), pages 393-408, July.
    12. Lo, Hong K. & Szeto, W. Y., 2002. "A methodology for sustainable traveler information services," Transportation Research Part B: Methodological, Elsevier, vol. 36(2), pages 113-130, February.
    13. Rapoport, Amnon & Gisches, Eyran J. & Daniel, Terry & Lindsey, Robin, 2014. "Pre-trip information and route-choice decisions with stochastic travel conditions: Experiment," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 154-172.
    14. G. E. Cantarella & E. Cascetta, 1995. "Dynamic Processes and Equilibrium in Transportation Networks: Towards a Unifying Theory," Transportation Science, INFORMS, vol. 29(4), pages 305-329, November.
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