Optimum Vehicle Flows in a Fully Automated Vehicle Network

This paper provides a novel assignment method and a solution algorithm that allows to determine the optimum vehicle flows in a fully automated vehicle network. This assignment method incorporates the following specific features: (1) optimal redistribution of occupied and unoccupied vehicles; (2) inter-vehicle spacing is adapted to meet the minimum safe distance criteria on congested link, (no collision in the worst failure case); (3) trip-time minimization of all traffic participants by a centralized vehicle routing. The latter feature allows the realization of a so called system optimum solution, which minimizes the total time of all trips. This assignment method is applied to two, topologically different, test networks at different travel demand levels, in order to determine: the share of unoccupied vehicle, the minimum number of required vehicles, the share of congested links, the lost trip-time of occupied vehicles due to the presents of unoccupied vehicles. Furthermore, the advantage of a centralized vehicle routing is quantified by comparing the total trip-times of a scenario using a system optimum solution with a scenario applying the user equilibrium solution, without considering unoccupied vehicle flows. Regarding the investigated scenarios, the share of unoccupied vehicle flows with centralized vehicle routing in a uniform, random demand scenario is approximately 11%−14%.

[1]  Joerg Schweizer,et al.  Comparison of static vehicle flow assignment methods and microsimulations for a personal rapid transit network , 2012 .

[2]  R. Horowitz,et al.  Control design of an automated highway system , 2000, Proceedings of the IEEE.

[3]  M J Beckmann.  ON OPTIMAL TOLLS FOR HIGHWAYS, TUNNELS, AND BRIDGES. IN VEHICULAR TRAFFIC SCIENCE , 1967 .

[4]  John D. Lees-Miller,et al.  Theoretical Maximum Capacity as Benchmark for Empty Vehicle Redistribution in Personal Rapid Transit , 2010 .

[5]  J. G. Wardrop,et al.  Some Theoretical Aspects of Road Traffic Research , 1952 .

[6]  Jacques Desrosiers,et al.  Selected Topics in Column Generation , 2002, Oper. Res..

[7]  Hai Yang,et al.  Principle of marginal-cost pricing : How does it work in a general road network ? , 1998 .

[8]  Perry Y. Li,et al.  AHS safe control laws for platoon leaders , 1997, IEEE Trans. Control. Syst. Technol..

[9]  Takuya Maruyama,et al.  Computational Experience on Advanced Algorithms for User Equilibrium Traffic Assignment Problem and Its Convergence Error , 2012 .

[10]  J. Schweizer.  Non-linear feedback control for short time headways based on constant-safety vehicle-spacing , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[11]  Ziyou Gao,et al.  Implementing Frank-Wolfe Algorithm under Different Flow Update Strategies and Line Search Technologies , 2008 .

[12]  Kari Koskinen,et al.  Developing a Microscopic Simulator for Personal Rapid Transit (PRT) Systems , 2007 .

[13]  Ennio Cascetta,et al.  Transportation Systems Engineering: Theory and Methods , 2001 .

[14]  Philippe Mahey,et al.  A Survey of Algorithms for Convex Multicommodity Flow Problems , 2000 .

[15]  Yosef Sheffi,et al.  Urban Transportation Networks: Equilibrium Analysis With Mathematical Programming Methods , 1985 .

[16]  H. M. Zhang,et al.  Models and algorithms for the traffic assignment problem with link capacity constraints , 2004 .

[17]  Michael Patriksson,et al.  The Traffic Assignment Problem: Models and Methods , 2015 .

[18]  Giorgio Gallo,et al.  A Bundle Type Dual-Ascent Approach to Linear Multicommodity Min-Cost Flow Problems , 1999, INFORMS J. Comput..

[19]  Philip Wolfe,et al.  An algorithm for quadratic programming , 1956 .

[20]  Ingmar Andreasson.  VEHICLE DISTRIBUTION IN LARGE PERSONAL RAPID TRANSIT SYSTEMS , 1994 .

[21]  J. A. Tomlin,et al.  Minimum-Cost Multicommodity Network Flows , 1966, Oper. Res..