Work

Spatiotemporal analysis of activity-travel fragmentation based on spatial clustering and sequence analysis

Year: 2022

Type: article

Abstract: In this study using data from the 2017 National Household Travel Survey in California from 26,078 survey participants, sequence analysis is used to estimate a fragmentation indicator of people's daily schedules. Then, spatial clustering is used to find groups of observations with similarly high or low fragmentation using the longitude and latitude of their residential locations. This is followed by a hierarchical sequence clustering within each spatial cluster to identify distinct patterns of time allocation. Using the Local Indicator of Spatial Association (LISA) we find a large portion (approximately 30%) of the sample with significant spatial clustering of fragmentation. We also find systematic and significant differences in membership to these clusters based on land use, county of residence, household and personal characteristics, and travel modes used. Sequence analysis pattern recognition within LISA spatial clusters shows systematically repeating time allocation patterns that include typical work and school schedules as well as staying at home patterns. However, each spatial LISA cluster is composed of different time allocation clusters. All this analysis taken together points out substantial and measurable heterogeneity in spatial clustering of fragmentation and the need for customized policy actions in different geographies.

Source: Journal of Transport Geography

DOI: 10.1016/j.jtrangeo.2022.103382

Authors: Hui Shi, Rongxiang Su, Jingyi Xiao, Konstadinos G. Goulias

Institution: University of California, Santa Barbara

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $2,660