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An equilibrium route choice model with explicit treatment of the effect of intersections

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  • Meneguzzer, Claudio

Abstract

This paper describes the implementation of a framework for incorporating detailed models of intersection operation into a user-optimal route choice model. It is assumed that signal settings are flow-responsive; this yields a combined route choice-intersection control problem, which is formulated as a non-separable network equilibrium problem with asymmetric cost functions. Such cost functions represent delays incurred by vehicles at intersections under various types of control (signalized, major/minor priority and all-way stop), and are based on an approach to capacity analysis widely used in traffic engineering practice, namely the methodologies of the 1985 Highway Capacity Manual and related amendments. The combined route choice-control problem is solved using a diagonalization algorithm; computational tests carried out on a real network indicate that the algorithm converges to a user equilibrium consistent with the control policy, despite violation of the sufficient conditions for convergence. The application also provides empirical evidence that the values of signal timing parameters, which are usually chosen solely on the basis of traffic engineering considerations, may have a substantial impact on the equilibration pattern. Finally, numerical experiments carried out to test the model for solution uniqueness suggest that 'reasonably' close equilibrium flow patterns arise starting from greatly different initial solutions. The modelling framework described in this paper could be typically applied in urban road networks for the assessment of alternative intersection control strategies, or, more generally, to carry out detailed traffic analyses and forecasts for the evaluation of TSM options.

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  • Meneguzzer, Claudio, 1995. "An equilibrium route choice model with explicit treatment of the effect of intersections," Transportation Research Part B: Methodological, Elsevier, vol. 29(5), pages 329-356, October.
  • Handle: RePEc:eee:transb:v:29:y:1995:i:5:p:329-356
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    4. Farahani, Reza Zanjirani & Miandoabchi, Elnaz & Szeto, W.Y. & Rashidi, Hannaneh, 2013. "A review of urban transportation network design problems," European Journal of Operational Research, Elsevier, vol. 229(2), pages 281-302.
    5. Tong, C. O. & Wong, S. C., 2000. "A predictive dynamic traffic assignment model in congested capacity-constrained road networks," Transportation Research Part B: Methodological, Elsevier, vol. 34(8), pages 625-644, November.
    6. Cipriani, Ernesto & Fusco, Gaetano, 2004. "Combined signal setting design and traffic assignment problem," European Journal of Operational Research, Elsevier, vol. 155(3), pages 569-583, June.
    7. Castillo González, Rodrigo & Clempner, Julio B. & Poznyak, Alexander S., 2019. "Solving traffic queues at controlled-signalized intersections in continuous-time Markov games," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 166(C), pages 283-297.
    8. Wang, Yi & Szeto, W.Y. & Han, Ke & Friesz, Terry L., 2018. "Dynamic traffic assignment: A review of the methodological advances for environmentally sustainable road transportation applications," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 370-394.
    9. Ferrari, Paolo, 1997. "Capacity constraints in urban transport networks," Transportation Research Part B: Methodological, Elsevier, vol. 31(4), pages 291-301, August.
    10. Giulio Cantarella & Antonino Vitetta, 2006. "The multi-criteria road network design problem in an urban area," Transportation, Springer, vol. 33(6), pages 567-588, November.
    11. Wong, S. C. & Yang, Chao & Lo, Hong K., 2001. "A path-based traffic assignment algorithm based on the TRANSYT traffic model," Transportation Research Part B: Methodological, Elsevier, vol. 35(2), pages 163-181, February.
    12. Ennio Cascetta & Mariano Gallo & Bruno Montella, 2006. "Models and algorithms for the optimization of signal settings on urban networks with stochastic assignment models," Annals of Operations Research, Springer, vol. 144(1), pages 301-328, April.

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