Multiagent-Based Simulation of Temporal-Spatial Characteristics of Activity-Travel Patterns Using Interactive Reinforcement Learning
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MPE.2014.951367.pdf
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2.74 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Yang, Min
Yang, Yingxiang
Wang, Wei
Ding, Haoyang
Chen, Jian
Date Issued
January 2014
Journal
Mathematical Problems in Engineering
Publisher
Hindawi Publishing Corporation
Citation
Yang, Min, Yingxiang Yang, Wei Wang, Haoyang Ding, and Jian Chen. “Multiagent-Based Simulation of Temporal-Spatial Characteristics of Activity-Travel Patterns Using Interactive Reinforcement Learning.” Mathematical Problems in Engineering 2014 (2014): 1–11.
Version
Final published version
Abstract
We propose a multiagent-based reinforcement learning algorithm, in which the interactions between travelers and the environment are considered to simulate temporal-spatial characteristics of activity-travel patterns in a city. Road congestion degree is added to the reinforcement learning algorithm as a medium that passes the influence of one traveler’s decision to others. Meanwhile, the agents used in the algorithm are initialized from typical activity patterns extracted from the travel survey diary data of Shangyu city in China. In the simulation, both macroscopic activity-travel characteristics such as traffic flow spatial-temporal distribution and microscopic characteristics such as activity-travel schedules of each agent are obtained. Comparing the simulation results with the survey data, we find that deviation of the peak-hour traffic flow is less than 5%, while the correlation of the simulated versus survey location choice distribution is over 0.9.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1155/2014/951367
