Title

Extracting Agent-Based Models Of Human Transportation Patterns

Keywords

agent-based models; human transportation patterns; Markov Chain Monte Carlo

Abstract

Due to their cheap development costs and ease of deployment, surveys and questionnaires are useful tools for gathering information about the activity patterns of a large group and can serve as a valuable supplement to tracking studies done with mobile devices. However in raw form, general survey data is not necessarily useful for answering predictive questions about the behavior of a large social system. In this paper, we describe a method for generating agent activity profiles from survey data for an agent-based model (ABM) of transportation patterns of 47,000 students on a university campus. We compare the performance of our agent-based model against a Markov Chain Monte Carlo (MCMC) simulation based directly on the distributions fitted from the survey data. A comparison of our simulation results against an independently collected dataset reveals that our ABM can be used to accurately forecast parking behavior over the semester and is significantly more accurate than the MCMC estimator. © 2012 IEEE.

Publication Date

1-1-2012

Publication Title

Proceedings of the 2012 ASE International Conference on Social Informatics, SocialInformatics 2012

Number of Pages

157-164

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/SocialInformatics.2012.60

Socpus ID

84881055818 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84881055818

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