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Risk-Averse Traffic Assignment with Elastic Demands: NCP Formulation and Solution Method for Assessing Performance Reliability


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  • W. Szeto


  • L. O'Brien
  • M. O'Mahony
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    How reliable the transport network is greatly affects the mobility and economy of a country. To assess the reliability of the network accurately and design a reliable transport network properly, one should consider the risk taking and elastic behaviour of travel demand. This paper develops a Nonlinear Complementarity Problem (NCP) formulation considering this behaviour. The NCP formulation is transformed to a smooth and unconstrained mathematical program via a Fischer function and then solved by the proposed modified αBB (branch and bound) algorithm, while guaranteeing finite convergence to a point arbitrarily close to the global minimum (ɛ-convergence). To speed up the computation, a sampling method is suggested. A small numerical study is set up to illustrate the properties and the performance of the algorithm. The results show that the bound improving strategy, the sampling size on estimating the value of α for the convex lower bounding function, and choice of α have a strong influence of the rate of convergence. Copyright Springer Science + Business Media, LLC 2006

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    Bibliographic Info

    Article provided by Springer in its journal Networks and Spatial Economics.

    Volume (Year): 6 (2006)
    Issue (Month): 3 (September)
    Pages: 313-332

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    Handle: RePEc:kap:netspa:v:6:y:2006:i:3:p:313-332

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    Keywords: Risk-averse traffic assignment; Elastic demand; Alpha-based branch-and-bound; Non-linear complementarity problem; Gap function;


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    Cited by:
    1. Chen, Anthony & Zhou, Zhong & Lam, William H.K., 2011. "Modeling stochastic perception error in the mean-excess traffic equilibrium model," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1619-1640.
    2. Nie, Yu (Marco), 2011. "Multi-class percentile user equilibrium with flow-dependent stochasticity," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1641-1659.
    3. Connors, Richard D. & Sumalee, Agachai, 2009. "A network equilibrium model with travellers' perception of stochastic travel times," Transportation Research Part B: Methodological, Elsevier, vol. 43(6), pages 614-624, July.
    4. Ng, ManWo & Szeto, W.Y. & Travis Waller, S., 2011. "Distribution-free travel time reliability assessment with probability inequalities," Transportation Research Part B: Methodological, Elsevier, vol. 45(6), pages 852-866, July.
    5. Li-Jun Tian & Hai-Jun Huang & Zi-You Gao, 2012. "A Cumulative Perceived Value-Based Dynamic User Equilibrium Model Considering the Travelers’ Risk Evaluation on Arrival Time," Networks and Spatial Economics, Springer, vol. 12(4), pages 589-608, December.
    6. ManWo Ng & Hong Lo, 2013. "Regional Air Quality Conformity in Transportation Networks with Stochastic Dependencies: A Theoretical Copula-Based Model," Networks and Spatial Economics, Springer, vol. 13(4), pages 373-397, December.
    7. Xiangdong Xu & Anthony Chen & Lin Cheng, 2013. "Assessing the effects of stochastic perception error under travel time variability," Transportation, Springer, vol. 40(3), pages 525-548, May.
    8. Ng, ManWo & Waller, S. Travis, 2010. "A computationally efficient methodology to characterize travel time reliability using the fast Fourier transform," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1202-1219, December.
    9. Zhi-Chun Li & William Lam & S. Wong, 2009. "The Optimal Transit Fare Structure under Different Market Regimes with Uncertainty in the Network," Networks and Spatial Economics, Springer, vol. 9(2), pages 191-216, June.
    10. Watling, David & Balijepalli, N.C., 2012. "A method to assess demand growth vulnerability of travel times on road network links," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(5), pages 772-789.


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