A first approach to a continuous simulation of daily travel

This paper introduces a microscopic traffic simulation that continuously simulates activitybased agent behavior and the resulting traffic. It drops iterative optimization, that builds on stochastic user equilibria, and moves to a continuous planning approach. The behavioral model of this approach utilizes the concept of needs to model continuous demands. Several intuitive parameters control demand and facilitate calibration of versatile behaviors. These behaviors originate from a planning heuristic which makes just in time decisions about upcoming activities an agent should execute. The planning heuristic bases its decisions on the current need levels of an agent and the development of these levels in the near future. We illustrate the model through simulation runs and suggest directions of future research.

[1]  David Charypar,et al.  Q-Learning for Flexible Learning of Daily Activity Plans , 2005 .

[2]  R. Schlich.  Verhaltenshomogene Gruppen in Längsschnitterhebungen , 2004 .

[3]  K. Axhausen.  Can we ever obtain the data we would like to have , 1998 .

[4]  F. J. Richards.  A Flexible Growth Function for Empirical Use , 1959 .

[5]  Kay W. Axhausen,et al.  Pushing the limits: A concept of a parallel microsimulation framework , 2010 .

[6]  G. Dantzig.  Discrete-Variable Extremum Problems , 1957 .

[7]  Eric J. Miller,et al.  An Integrated Framework for Modelling Short- and Long-Run Household Decision-Making , 2005 .

[8]  Harry Timmermans,et al.  New Theory of Dynamic Activity Generation , 2006 .

[9]  K. Nagel,et al.  Agent-based traffic assignment: going from trips to behavioral travelers , 2009 .

[10]  Matthias Feil,et al.  Generating comprehensive all-day schedules , 2009 .

[11]  Murray Silverstein,et al.  A Pattern Language , 1977 .

[12]  T. Arentze,et al.  A need-based model of multi-day, multi-person activity generation , 2009 .

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

[14]  Paul Widmer,et al.  Untersuchung der Stabilitaet des Verkehrsverhaltens , 2005 .

[15]  Kay W. Axhausen,et al.  Travel as a function of (life) projects , 2009 .

[16]  C H Chang-Hyeon Joh,et al.  Measuring and predicting adaptation in multidimensional activity-travel patterns , 2004 .

[17]  J. Ben Atkinson,et al.  A Greedy Look-ahead Heuristic for Combinatorial Optimization: An Application to Vehicle Scheduling with Time Windows , 1994 .

[18]  Max Jacobson,et al.  A Pattern Language: Towns, Buildings, Construction , 1981 .

[19]  Ralph Johnson,et al.  design patterns elements of reusable object oriented software , 2019 .

[20]  Tobias Kuhnimhof,et al.  Multiday Multiagent Model of Travel Behavior with Activity Scheduling , 2009 .

[21]  H. Simon,et al.  A Behavioral Model of Rational Choice , 1955 .

[22]  Stefan Schönfelder,et al.  Urban rhythms: modelling the rhythms of individual travel behaviour , 2006 .

[23]  George Ioannou,et al.  A greedy look-ahead heuristic for the vehicle routing problem with time windows , 2001, J. Oper. Res. Soc..