Transport policy evaluation based on elasticity analysis with social interactions

• Published in: Transportation research. Part A, Policy and practice Vol. 139; pp. 273 - 296
• Main Author: Hsieh, Hsu-Sheng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2020
• Subjects: Elasticity analysis; Parking behaviour; Social interaction; Spillover effect; Transport policy evaluation; Two-wheel vehicle; Spillover effect; Parking behaviour; Social interaction; Elasticity analysis; Transport policy evaluation; Two-wheel vehicle
• ISSN: 0965-8564; 1879-2375
• DOI: 10.1016/j.tra.2020.07.011

•Modified elasticity analysis with social interactions considers spillover effects.•Psychological heterogeneity is introduced into parking location choice model.•Social interactions in parking behaviour are attested and affect individual choice.•Direct policy effect and policy spillover effects in policy evaluation are calculated.•Soft and hard parking measures based on psychology and social influence are offered. The effects of social interactions on individual behaviour have been observed in many social phenomena, including travel-related behaviour involving direct or indirect contact with other agents. However, most formulations of urban transport policy assume that individual behaviour is independent of each other, which likely results in policy evaluation bias. Therefore, this study aimed at urban parking behaviour with frequent interactions among agents in Taiwan (1) to examine the influence of others’ behaviour on individuals by modelling parking location choice and introducing social utility and (2) to develop a modified elasticity analysis method that considers policy spillover effects driven by social interactions and then use it to conduct parking policy evaluation based on the choice model. A stated preference survey targeting scooter and bicycle users was performed through face-to-face interviews to simulate parking policy and reference-group behaviour levels. The pseudo panel where the observations of each respondent may be interdependent was addressed by a mixed logit model incorporating an error component to capture unobserved individual heterogeneity. The results of the model estimation supported the presence of social interactions among users and identified other influential determinants of parking location choice including travel characteristics (visit frequency and vehicle used), psychological heterogeneity (attitudes and personal norms), and parking policy (distance, fixed cost, and marginal cost). For policy evaluation, the proposed elasticity analysis method with social interactions illustrated that policy spillover effects (49.7%) were nearly as strong as direct policy effects (50.3%). Two-wheel vehicle parking policy measures containing soft measures based on attitudes and personal norms and hard measures considering social influences were presented for on-street parking mitigation.