Citation

Zhang D, Cao J, Feygin S, Tang D, Shen ZJM, Pozdnoukhov A. Transp. Res. C Emerg. Technol. 2019; 103: 1-16.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.trc.2018.12.014

PMID

unavailable

Abstract

Agent-based modeling in transportation problems requires detailed information on each of the agents that represent the population in the region of a study. To extend the agent-based transportation modeling with social influence, a connected synthetic population with both synthetic features and its social networks need to be simulated. However, either the traditional manually-collected household survey data (ACS) or the recent large-scale passively-collected Call Detail Records (CDR) alone lacks features. This work proposes an algorithmic procedure that makes use of both traditional survey data as well as digital records of networking and human behavior to generate connected synthetic populations. The generated populations coupled with recent advances in graph (social networks) algorithms can be used for testing transportation simulation scenarios with different social factors.

Language: en

Keywords

Agent-based modeling; Bayesian networks; Cellular data; Exponential random graph model; Mixed integer programming; Population synthesis; Structural learning; Transportation simulation