Modeling spatial segregation and travel cost influences on utilitarian walking: Towards policy intervention

Authored by Rick L Riolo, Daniel G Brown, Amy H Auchincloss, Yong Yang, Daniel A Rodriguez, Ana V Diez-Roux

Date Published: 2015

DOI: 10.1016/j.compenvurbsys.2015.01.007

Sponsors: National Heart Lung and Blood Institute Office of Behavioral and Social Sciences Research

Platforms: Repast Java

Model Documentation: UML Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

We develop an agent-based model of utilitarian walking and use the model to explore spatial and socioeconomic factors affecting adult utilitarian walking and how travel costs as well as various educational interventions aimed at changing attitudes can alter the prevalence of walking and income differentials in walking. The model is validated against US national data. We contrast realistic and extreme parameter values in our model and test effects of changing these parameters across various segregation and pricing scenarios while allowing for interactions between travel choice and place and for behavioral feedbacks. Results suggest that in addition to income differences in the perceived cost of time, the concentration of mixed land use (differential density of residences and businesses) are important determinants of income differences in walking (high income walk less), whereas safety from crime and income segregation on their own do not have large influences on income differences in walking. We also show the difficulty in altering walking behaviors for higher income groups who are insensitive to price and how adding to the cost of driving could increase the income differential in walking particularly in the context of segregation by income and land use. We show that strategies to decrease positive attitudes towards driving can interact synergistically with shifting cost structures to favor walking in increasing the percent of walking trips. Agent-based models, with their ability to capture dynamic processes and incorporate empirical data, are powerful tools to explore the influence on health behavior from multiple factors and test policy interventions. (C) 2015 Elsevier Ltd. All rights reserved.

Tags

Agent-based model behavior movement Built Environment Physical-activity School Parking policies Active travel Choice models Atherosclerosis