Sensitivity analysis of integrated activity-based model: using MATSim as an example

Abstract This paper attempts to apply both global and local sensitivity analyses (SA) to fully test the integrated activity-based model (ABM) within several experiments based on a Chinese medium-sized city, Baoding. MATSim (Multi-agent Transport Simulation) that is a typical integrated ABM is used as an example for the SA. The global SA, which is based on the elementary effect method, is firstly applied to identify the influential parameters. Then the once-at-a-time (OAT)-based Local SA is employed to further reveal the relationship between the influential parameters and the outputs of interest, such as traffic flow. The SA results show the extent to which and how three influential MATSim parameters (population scaling factor, the number of iterations and time step size) influence the outputs of interest. In addition, the SA results of MATSim suggest that the parameters of time mutation rate and performing utility can heavily influence the outputs of interest and properly setting them can optimize the daily plans of agents. Finally, this paper concludes with suggestions on how to wisely use the SA findings for both MATSim and other ABM users.

[1]  Ta Theo Arentze,et al.  Analysis of uncertainty in performance indicators of a complex activity-based model: The case of the Albatross model system , 2012 .

[2]  M.‐H. Massot Sensitivity of public transport demand to the level of transport service in French cities without underground , 1994 .

[3]  Chandra R. Bhat,et al.  Activity-based Travel Demand Analysis , 2011 .

[4]  Christoph Dobler Implementation of a Time Step Based Parallel Queue Simula- tion in MATSim , 2010 .

[5]  David J Spiegelhalter,et al.  Don't know, can't know: embracing deeper uncertainties when analysing risks , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[6]  Davy Janssens,et al.  Sensitivity Analysis on Decision Trees in Activity-Based Travel Demand Modeling Framework FEATHERS , 2016 .

[7]  Chao Yang,et al.  Sensitivity-based Uncertainty Analysis of a Combined Travel Demand Model , 2011 .

[8]  D. Hamby A review of techniques for parameter sensitivity analysis of environmental models , 1994, Environmental monitoring and assessment.

[9]  Randolph W. Hall,et al.  Handbook of transportation science , 1999 .

[10]  Jian Gao,et al.  An improvement in MATSim computing time for large-scale travel behaviour microsimulation , 2019, Transportation.

[11]  Hui Zhang,et al.  Agent-based joint model of residential location choice and real estate price for land use and transport model , 2016, Comput. Environ. Urban Syst..

[12]  Jian Gao,et al.  An Initial Implementation of Multiagent Simulation of Travel Behavior for a Medium-Sized City in China , 2014 .

[13]  Davy Janssens,et al.  Investigating micro-simulation error in activity-based travel demand forecasting: a case study of the FEATHERS framework , 2015 .

[14]  Hjp Harry Timmermans,et al.  Uncertainty in travel demand forecasting models: literature review and research agenda , 2012 .

[15]  Chunfu Shao,et al.  Baoding: A Case Study for Testing a New Household Utility Function in MATSim , 2016 .

[16]  Sergei S. Kucherenko,et al.  Derivative based global sensitivity measures and their link with global sensitivity indices , 2009, Math. Comput. Simul..

[17]  Enjian Yao,et al.  Comparison of Electric Vehicle’s Energy Consumption Factors for Different Road Types , 2013 .

[18]  Lin Cheng,et al.  A Review of Activity-Based Travel Demand Modeling , 2012 .

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

[20]  Kay W. Axhausen,et al.  The Multi-Agent Transport Simulation , 2016 .

[21]  Soora Rasouli,et al.  Assessment of model uncertainty in destinations and travel forecasts of models of complex spatial shopping behaviour , 2013 .

[22]  F. Koppelman,et al.  Activity-Based Modeling of Travel Demand , 2003 .

[23]  Hjp Harry Timmermans,et al.  Rural location-based activity generation: a case study of Iran villages , 2016 .

[24]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[25]  Shuangzhe Liu,et al.  Global Sensitivity Analysis: The Primer by Andrea Saltelli, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora Gatelli, Michaela Saisana, Stefano Tarantola , 2008 .

[26]  Ward Vanderheyden Sensitivity analysis of the Feathers activity-based model for Flanders , 2012 .

[27]  Harry Timmermans,et al.  RAMBLAS: A Regional Planning Model Based on the Microsimulation of Daily Activity Travel Patterns , 2000 .

[28]  Warren E. Walker,et al.  Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support , 2003 .

[29]  Davy Janssens,et al.  Implementation Framework and Development Trajectory of FEATHERS Activity-Based Simulation Platform , 2010 .

[30]  Adel W. Sadek,et al.  TRANSIMS Implementation in Chittenden County, Vermont , 2009 .

[31]  Stephen D. Clark,et al.  Sensitivity analysis of the probit-based stochastic user equilibrium assignment model , 2002 .

[32]  Francesca Pianosi,et al.  A Matlab toolbox for Global Sensitivity Analysis , 2015, Environ. Model. Softw..

[33]  A. Samimi,et al.  An activity-based freight mode choice microsimulation model , 2014 .

[34]  Reginald G. Golledge,et al.  An assessment of activity-based modeling and simulation for applications in operational studies, disaster preparedness, and homeland security , 2009 .

[35]  G. Wets,et al.  Activity-Based Travel Demand Modeling Framework FEATHERS: Sensitivity Analysis with Decision Trees , 2016 .

[36]  Soora Rasouli,et al.  Activity-based models of travel demand: promises, progress and prospects , 2014 .

[37]  Stefano Tarantola,et al.  Elementary Effects Method , 2008 .

[38]  Andrea Saltelli,et al.  An effective screening design for sensitivity analysis of large models , 2007, Environ. Model. Softw..

[39]  Xiaozhi Zhou,et al.  Sensitivity Analysis and Uncertainty Analysis in a Large-scale Agent-based Simulation Model of Infectious Diseases , 2014 .

[40]  Paola Annoni,et al.  Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..

[41]  Yuanyuan Song,et al.  Study on Eco-Route Planning Algorithm and Environmental Impact Assessment , 2013, J. Intell. Transp. Syst..

[42]  Michael Balmer Travel demand modeling for multi-agent transport simulations , 2007 .