Growing Spatially Embedded Social Networks for Activity-Travel Analysis Based on Artificial Transportation Systems

Authored by Songhang Chen, Fenghua Zhu, Jianping Cao

Date Published: 2014-10

DOI: 10.1109/tits.2014.2308975

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

Social activity-travel has gained more and more attention as it is a growing percentage of the whole travel. To study its generation mechanism and behavioral characteristics, social network data are usually essential. However, due to individual privacy, it is rather difficult for traditional methods such as questionnaires to collect abundant reliable data. Therefore, we propose a novel method to grow realistic social networks based on artificial transportation systems (ATS). By incorporating the activity-travel simulation provided by ATS and a new agent-based model for social interaction, the method takes into account human mobility to generate spatially embedded social networks. Human mobility shapes and impacts social networks dynamically but is usually ignored by related studies. A case study based on computational experiments is carried out to verify the method. The results indicate that the method can generate social networks with similar topological and spatial characteristics to real social networks.

Tags

Agent reinforcement learning Activity-based traffic simulation artificial transportation systems (ATS) spatially embedded social networks