Behavioral acceptance of automated vehicles: The roles of perceived safety concern and current travel behavior

TL;DR

This study investigates how perceived safety concerns and current travel habits influence public acceptance of automated vehicles, using a complex econometric model to inform policies that target specific population groups.

Contribution

It introduces a recursive trivariate econometric model that jointly estimates AV acceptance, safety concerns, and travel behavior, capturing their interdependencies and informing targeted policy strategies.

Findings

Safety concerns negatively impact AV acceptance.

Lower VMT individuals are more receptive to AVs.

Cost-conscious and safety-concerned groups are key targets for policy.

Abstract

With the prospect of next-generation automated mobility ecosystem, the realization of the contended traffic efficiency and safety benefits are contingent upon the demand landscape for automated vehicles (AVs). Focusing on the public acceptance behavior of AVs, this empirical study addresses two gaps in the plethora of travel behavior research on identifying the potential determinants thereof. First, a clear behavioral understanding is lacking as to the perceived concern about AV safety and the consequent effect on AV acceptance behavior. Second, how people appraise the benefits of enhanced automated mobility to meet their current (pre-AV era) travel behavior and needs, along with the resulting impacts on AV acceptance and perceived safety concern, remain equivocal. To fill these gaps, a recursive trivariate econometric model with ordinal-continuous outcomes is employed, which jointly estimates AV acceptance (ordinal), perceived AV safety concern (ordinal), and current annual vehicle-miles traveled (VMT) approximating the current travel behavior (continuous). Importantly, the co-estimation of the three endogenous outcomes allows to capture the true interdependencies among them, net of any correlated unobserved factors that can have common impacts on these outcomes. Besides the classical socio-economic characteristics, the outcome variables are further explained by the latent preferences for vehicle attributes (including vehicle cost, reliability, performance, and refueling) and for existing shared mobility systems. The model estimation results on a stated preference survey in the State of California provide insights into proactive policies that can popularize AVs through gearing towards the most affected population groups, particularly vehicle cost-conscious, safety-concerned, and lower-VMT (such as travel-restrictive) individuals.