Introducing joint trips in a multi-agent transport simulation: from agents to clique replanning

MATS im is a multi-agent transport simulation software, based on an iterative relaxation procedure. In this framework, each agent is assigned a daily plan consisting of a sequence of trips and activities, to which a score is associated. Daily plans are optimised knowing the state of the traffic flows. The system iterates between traffic flow simulation and plan modification, until an equilibrium is reached. In this report, the ability for the agent to travel together with other agents is introduced. This is done by passing from optimisation of the scores of individual plans to the optimisation of scores associated to joint plans. To perform such an optimisation, a genetic algorithm is used, which is presented. This algorithm is able to improve the scores of a joint plan by identifying pertinent joint trips. However, clues show remaining convergence difficulties, which are analysed.

[1]  Roberto Baldacci,et al.  An Exact Method for the Car Pooling Problem Based on Lagrangean Column Generation , 2004, Oper. Res..

[2]  Kay W. Axhausen,et al.  Implementing activity-based models: accelerating the replanning process of agents using an evolution strategy , 2006 .

[3]  J. Krawczyk,et al.  Relaxation Algorithms in Finding Nash Equilibria , 1998 .

[4]  Wei Tu,et al.  A multi-objective approach to scheduling joint participation with variable space and time preferences and opportunities , 2011 .

[5]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[6]  Zbigniew Michalewicz,et al.  Handling Constraints in Genetic Algorithms , 1991, ICGA.

[7]  W.W. Recker,et al.  A MODEL OF COMPLEX TRAVEL BEHAVIOR: PART I. THEORETICAL DEVELOPMENT , 1985 .

[8]  M. Bradley,et al.  A model for joint choice of daily activity pattern types of household members , 2005 .

[9]  Thomas F. Golob,et al.  A Simultaneous Model of Household Activity Participation and Trip Chain Generation , 1999 .

[10]  Craig R. Rindt,et al.  The Activity-Based Approach , 2008 .

[11]  Marcel Rieser,et al.  Agent-Oriented Coupling of Activity-Based Demand Generation with Multiagent Traffic Simulation , 2007 .

[12]  Frank S. Koppelman,et al.  Modeling household activity–travel interactions as parallel constrained choices , 2005 .

[13]  Mark D. Uncles,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1987 .

[14]  Kay W. Axhausen,et al.  Agent-based simulation of travel demand: Structure and computational performance of MATSim-T , 2008 .

[15]  Tim Roughgarden,et al.  Algorithmic Game Theory , 2007 .

[16]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Kay W. Axhausen,et al.  Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations , 2006 .

[18]  Francesco Ciari,et al.  Mobility tool ownership and mode choice decision processes in multi-agent transportation simulation; 7th Swiss Transport Research Conference; Working paper / IVT; , 2007 .

[19]  Kay W. Axhausen,et al.  Definition Of Movement and Activity For Transport Modelling , 2007 .

[20]  Kay W. Axhausen,et al.  New approaches to generating comprehensive all-day activity-travel schedules , 2009 .

[21]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[22]  Michel Bierlaire,et al.  Bayesian Demand Calibration for Dynamic Traffic Simulations , 2011, Transp. Sci..

[23]  Kay W. Axhausen,et al.  Location choice modeling for shopping and leisure activities with MATSim , 2009 .

[24]  P. Jones,et al.  Understanding Travel Behaviour , 1983 .

[25]  Ronald Koch,et al.  Nash Equilibria and the Price of Anarchy for Flows over Time , 2011, Theory of Computing Systems.

[26]  Kay W. Axhausen,et al.  A GA-based household scheduler , 2005 .

[27]  J. Hackney.  Integration of social networks in a large-scale travel behavior microsimulation , 2009 .

[28]  K. Nagel,et al.  Generating complete all-day activity plans with genetic algorithms , 2005 .

[29]  Marcel Rieser,et al.  Truly agent-oriented coupling of an activity-based demand generation with a multi-agent traffic simulation , 2002 .

[30]  Fernando G. Lobo,et al.  A parameter-less genetic algorithm , 1999, GECCO.

[31]  Matthew J. Roorda,et al.  A tour-based model of travel mode choice , 2005 .

[32]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[33]  Francisco Herrera,et al.  A taxonomy for the crossover operator for real‐coded genetic algorithms: An experimental study , 2003, Int. J. Intell. Syst..

[34]  Georges R. Harik,et al.  Finding Multimodal Solutions Using Restricted Tournament Selection , 1995, ICGA.

[35]  H. Timmermans,et al.  A model of household task allocation and time use , 2005 .

[36]  K. Nagel,et al.  Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation , 2012 .

[37]  Frank S. Koppelman,et al.  A model of joint activity participation between household members , 2002 .

[38]  Jeffrey Horn,et al.  The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations , 1997 .

[39]  Zbigniew Michalewicz,et al.  GENOCOP: a genetic algorithm for numerical optimization problems with linear constraints , 1996, CACM.

[40]  Kay W. Axhausen,et al.  Location Choice Modeling for Shopping and Leisure Activities with MATSim , 2010 .

[41]  Wilfred W. Recker,et al.  The Household Activity Pattern Problem: General Formulation and Solution , 1995 .

[42]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[43]  M. McNally,et al.  A MODEL OF ACTIVITY PARTICIPATION AND TRAVEL INTERACTIONS BETWEEN HOUSEHOLD HEADS , 1996 .

[44]  Kay W. Axhausen,et al.  An improved replanning module for agent-based micro simulations of travel behavior , 2005 .

[45]  Francesco Ciari,et al.  Large-scale agent-based travel demand optimization applied to Switzerland, including mode choice , 2010 .

[46]  Kay W. Axhausen,et al.  planomat: a comprehensive scheduler for a large-scale multi-agent transportation simulation , 2006 .

[47]  Francesco Ciari,et al.  A new mode choice model for a multi-agent transport simulation , 2008 .