An agent-based micro-simulation framework for modelling of dynamic activity–travel rescheduling decisions

The simultaneous implementation of daily activity–travel schedules of individuals in a given spatial environment generally gives rise to time- and location-varying congestion levels, which affect the conditions for subsequent activity and travel choices. Although such dynamics are commonly recognized, current activity-based models typically ignore the adaptive behaviour of individuals. In this article, we propose an agent-based simulation system that allows one to simulate, in addition to activity-scheduling behaviour, also the execution of schedules in space and time. Congestion levels at specific times and places emerge in the system and may lead to discrepancies between scheduled and actual activity and travel times. Agents respond to such unforeseen events by reconsidering an existing schedule (within-day re-planning) and by adapting their expectations about traffic conditions for subsequent days (learning). The system is illustrated using the activity–travel diary data collected in the Eindhoven region, the Netherlands, to better understand the choice of urban parks in the study area. We discuss the merits of the system for transport and spatial planning and identify avenues for future research.

[1]  Fulong Wu,et al.  Calibration of stochastic cellular automata: the application to rural-urban land conversions , 2002, Int. J. Geogr. Inf. Sci..

[2]  Mark Bradley,et al.  Activity-Based Travel Forecasting Models in the United States: Progress since 1995 and Prospects for the Future , 2005 .

[3]  Jon M. Kerridge,et al.  PEDFLOW: Development of an Autonomous Agent Model of Pedestrian Flow , 2001 .

[4]  Ta Theo Arentze,et al.  Activity-Travel Scheduling and Rescheduling Decision Processes: Empirical Estimation of Aurora Model , 2004 .

[5]  Ta Theo Arentze,et al.  Understanding activity scheduling and rescheduling behaviour : theory and numerical illustration , 2002 .

[6]  Jan Dijkstra,et al.  Towards a multi-agent model for visualizing simulated user behavior to support the assessment of design performance , 2002 .

[7]  D. Scott,et al.  Exploring time patterns in people's use of a metropolitan park district , 1997 .

[8]  D. McFadden.  The Choice Theory Approach to Market Research , 1986 .

[9]  Harvey J. Miller,et al.  What about people in geographic information science? , 2003, Comput. Environ. Urban Syst..

[10]  R. Kitamura.  A model of daily time allocation to discretionary out-of-home activities and trips , 1984 .

[11]  Torsten Hägerstraand.  WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .

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

[13]  Ta Theo Arentze,et al.  Creating Synthetic Household Populations , 2007 .

[14]  Wenzhong Shi,et al.  Development of Voronoi-based cellular automata -an integrated dynamic model for Geographical Information Systems , 2000, Int. J. Geogr. Inf. Sci..

[15]  Kai Nagel,et al.  An Improved Framework for Large-Scale Multi-Agent Sim- ulations of Travel Behavior , 2004 .

[16]  Xia Li,et al.  Modelling sustainable urban development by the integration of constrained cellular automata and GIS , 2000, Int. J. Geogr. Inf. Sci..

[17]  Xia Li,et al.  Data mining of cellular automata's transition rules , 2004, Int. J. Geogr. Inf. Sci..

[18]  Helen Couclelis,et al.  Map Dynamics Integrating Cellular Automata and GIS Through Geo-Algebra , 1997, Int. J. Geogr. Inf. Sci..

[19]  Satoshi Fujii,et al.  FAMOS: The Florida activity mobility simulator , 2005 .

[20]  M. D. McKay,et al.  Creating synthetic baseline populations , 1996 .

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

[22]  K E Rosing,et al.  The Robustness of Two Common Heuristics for the p-Median Problem , 1979 .

[23]  Mordechai Haklay,et al.  STREETS: an agent-based pedestrian model , 1999 .

[24]  G. Becker,et al.  A Theory of the Allocation of Time , 1965 .

[25]  Michael Batty,et al.  The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades , 2003, Int. J. Geogr. Inf. Sci..

[26]  Peter Jones,et al.  'Encouraging behavioural change through marketing and management: what can be achieved?' Resource paper at the 10th International Conference on Travel Behaviour Research, Lucerne, 10-15 August , 2003 .

[27]  Tommy Gärling,et al.  Computational-Process Modelling of Household Activity Scheduling , 1993 .

[28]  Matthew J. Roorda,et al.  Strategies for Resolving Activity Scheduling Conflicts: An Empirical Analysis , 2005 .

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

[30]  Tommy Gärling,et al.  The role of anticipated time pressure in activity scheduling , 1999 .

[31]  Harry Timmermans,et al.  Albatross version 2: A learning-Based Transportation Oriented Simulation System , 2005 .

[32]  James E. Marca,et al.  An Agent-Based Activity Microsimulation Kernel Using a Negotiation Metaphor , 2002 .

[33]  H. Timmermans,et al.  Simulating the Effects of Urban Development on Activity — Travel Patterns: An Application of Ramblas to the Randstad North Wing , 2005 .

[34]  Ta Theo Arentze,et al.  Modeling learning and adaptation processes in activity-travel choice A framework and numerical experiment , 2003 .

[35]  F. Chapin.  Human activity patterns in the city , 1974 .

[36]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[37]  P. Kanaroglou,et al.  AN ACTIVITY-EPISODE GENERATION MODEL THAT CAPTURES INTERACTIONS BETWEEN HOUSEHOLD HEADS: DEVELOPMENT AND EMPIRICAL ANALYSIS , 2002 .

[38]  Gunnar Flötteröd,et al.  Enhancing MATSim with capabilities of within-day re-planning , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[39]  Ta Theo Arentze,et al.  Modeling Effects of Anticipated Time Pressure on Execution of Activity Programs , 2001 .

[40]  Eric J. Miller,et al.  ILUTE: An Operational Prototype of a Comprehensive Microsimulation Model of Urban Systems , 2005 .

[41]  Siddhartha Bhattacharyya,et al.  A review of machine learning in scheduling , 1994 .

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

[43]  Tijs Neutens,et al.  Space–time opportunities for multiple agents: a constraint‐based approach , 2007, Int. J. Geogr. Inf. Sci..

[44]  Janusz Supernak,et al.  TEMPORAL UTILITY PROFILES OF ACTIVITIES AND TRAVEL: UNCERTAINTY AND DECISION MAKING , 1992 .

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