Fully agent-based simulation model of multimodal mobility in European cities

Even though the agent-based simulation modelling has become a standard tool in transport research, current implementations still treat travellers as passive data structures, updated synchronously at infrequent, predefined points in time, thus failing to cover within-the-day decision making and negotiation necessary for cooperative behaviour in a dynamic transport system. Leveraging the fully agent-based modelling approach, we have built large-scale activity-based models of multimodal mobility covering areas up to thousands of square kilometres and simulating populations of up to millions of inhabitants of several European cities. Citizens are represented by autonomous, self-interested agents which schedule and execute their activities (work, shopping, leisure, etc.) and trips in time and space. Individual decisions are influenced by agent's demographic attributes and modelled using the data from mobility surveys. The model is statistically validated against origin-destination matrices and travel diary data sets.

[1]  Daniele Miorandi,et al.  SUPERHUB: a user-centric perspective on sustainable urban mobility , 2012, Sense Transport '12.

[2]  Michal Jakob,et al.  Agent-based Simulation Testbed for On-demand Mobility Services , 2014, ANT/SEIT.

[3]  Michael G. McNally,et al.  The Four Step Model , 2007 .

[4]  Michal Jakob,et al.  Data Driven Validation Framework for Multi-agent Activity-Based Models , 2015, MABS.

[5]  Peter Rossmanith,et al.  Simulated Annealing , 2008, Taschenbuch der Algorithmen.

[6]  Milind Tambe,et al.  AgentPolis: towards a platform for fully agent-based modeling of multi-modal transportation (demonstration) , 2012, AAMAS.

[7]  N. H. M. Wilson,et al.  Simulation of a Computer Aided Routing System (CARS) , 1969 .

[8]  Ta Theo Arentze,et al.  Experiences with developing ALBATROSS: a learning-based transportation oriented simulation system , 1998 .

[9]  M. E. Williams,et al.  TRANSIMS: TRANSPORTATION ANALYSIS AND SIMULATION SYSTEM , 1995 .

[10]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[11]  Franziska Klügl,et al.  Agent-Based Simulation Engineering , 2016 .

[12]  Arthur M. Geoffrion,et al.  The Purpose of Mathematical Programming is Insight, Not Numbers , 1976 .

[13]  Randolph W. Hall,et al.  Discrete models/continuous models , 1986 .

[14]  Bo Chen,et al.  A Review of the Applications of Agent Technology in Traffic and Transportation Systems , 2010, IEEE Transactions on Intelligent Transportation Systems.

[15]  Averill M. Law,et al.  How to build valid and credible simulation models , 2008, 2008 Winter Simulation Conference.

[16]  Kathryn A. Dowsland,et al.  Simulated Annealing , 1989, Encyclopedia of GIS.

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

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