User response to autonomous vehicles and emerging mobility systems

Akar, Gulsah; Erhardt, Gregory D. · 2018 · OpenAlex-citations

DOI: 10.1007/s11116-018-9943-y

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Summary

This editorial introduces a special issue of *Transportation* focused on user responses to autonomous vehicles (AVs) and emerging mobility systems, such as shared mobility and Mobility as a Service (MaaS). The authors highlight that while these technologies promise safety and efficiency, their implications for vehicle ownership and travel behavior remain largely unknown. The issue compiles five papers originally presented at the 2018 Transportation Research Board Annual Meeting, offering state-of-the-art insights into how these systems affect human behavior and travel outcomes. The first paper by Harb et al. utilizes a naturalistic experiment to estimate travel behavior implications of self-driving vehicles. The study provided 60 hours of free chauffeur service to 13 households in the San Francisco Bay Area over seven days, recording activity-travel patterns for three weeks. Results indicated an 83% increase in vehicle miles traveled (VMT), rising from 4% to 341%. Approximately one-third of this increase stemmed from sending the car on errands or escorting others, while one-fourth consisted of "zero-occupancy" miles. Participants made longer, more frequent trips and traveled more in the evenings, confirming that the removal of driving duties significantly alters activity-travel patterns. The second paper by Shabanpour et al. applies an innovation diffusion model to predict AV adoption timing using stated preference data from the Chicago metropolitan area. The model accounts for consumer heterogeneity, distinguishing between "innovators" and "imitators." Findings suggest that AV market penetration could eventually reach 70%, with adoption likelihood influenced by socioeconomic factors, attitudes, and travel habits, particularly among frequent long-distance travelers. The third paper by Nair et al. introduces a rank-ordered probit (ROP) modeling approach to analyze traveler preferences for various AV deployment schemes. This method overcomes the instability of coefficients found in standard rank-ordered logit models, providing a robust tool for understanding preferences when primary choices are unavailable. The fourth paper by Strömberg et al. evaluates the UbiGo MaaS field trial in Gothenburg, Sweden, which offered a subscription-based package of public transit, taxi, bike/car sharing, and rentals. Analysis of user interviews identified four subgroups: car shedders, car accessors, simplifiers, and economizers. Generally, users drove less and walked more, shifting toward more sustainable transport options and altering pre-trip planning and trip chaining. The fifth paper by Friedrich et al. presents an algorithm for matching ridesharing trips within macroscopic travel demand models. This approach converts driver paths into zone sequences to identify compatible passenger trips, aiding in the assessment of ridesharing market impacts. Collectively, these studies provide critical methods for modeling demand and understanding the behavioral shifts associated with emerging transportation technologies.

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• acceptance adoption
• exposure measurement

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• Empirical Findings: observational prevalence
• Theoretical Contribution: computational model