Examining the Equity Impacts of Autonomous Vehicles: A Travel Demand Model Approach

Abstract

As investments in autonomous vehicle (AV) technology continue to grow, agencies are beginning to consider how AVs will affect travel behavior within their jurisdictions and how to respond to this new mobility technology. Different autonomous futures could reduce, perpetuate, or exacerbate existing transportation inequities. This paper presents a regional travel demand model used to quantify how transportation outcomes may differ for disadvantaged populations in the Washington, D.C. area under a variety of future scenarios. Transportation performance measures examined included job accessibility, trip duration, trip distance, mode share, and vehicle miles traveled. The model evaluated changes in these indicators for disadvantaged and non-disadvantaged communities under scenarios when AVs were primarily single-occupancy or high-occupancy, and according to whether transit agencies responded to AVs by maintaining the status quo, removing low-performing routes, or applying AV technology to transit vehicles. Across the performance measures, the high-occupancy AV and enhanced transit scenarios provided an equity benefit, either mitigating an existing gap in outcomes between demographic groups or reducing the extent to which that gap was expanded.

Autonomous Vehicles and Public Health

“…Some equity priorities in transportation are related to transportation costs, access to destinations, services (such as health services), and employment (16). A recent study modeled the equity impact of AVs in terms of job accessibility in Washington, DC (17). The study found that in all the scenarios modeled, AVs increased job accessibility, especially in more disadvantaged populations and in scenarios using ridesharing SAVs (17).…”

“…A recent study modeled the equity impact of AVs in terms of job accessibility in Washington, DC (17). The study found that in all the scenarios modeled, AVs increased job accessibility, especially in more disadvantaged populations and in scenarios using ridesharing SAVs (17). Two main recommendations to support social equity for AVs are (a) to engage and include disadvantaged communities in transportation planning, especially regarding SAVs; and (b) to reduce barriers to using SAVs, including financial, technological, language, and cultural barriers.…”

The Impacts of Emerging Mobility Options and Vehicle Technologies on Travel Behavior

“…For impacts of AVs, the study topics mostly focused on network performance measures like VMT, VHT, congestion, etc. (Nadafian et al, 2019;Xu et al, 2019;Cohn et al, 2019); and investigating traffic operation in the era of AVs (Rahmati et al, 2017;Rahman et al, 2019). The methodology is evolving from a four-step demand model (Zmud et al, 2016) to a more advanced multi-modal activity-based model (Xu et al, 2019) and integrated land use and demand model (Nadafian et al, 2019).…”

Preface

“…Moreover, the perception that AVs will bring mainly a change to vehicle performance led researchers to explore primarily the short-term implications for traffic flow, road safety and fuel efficiency ( Milakis et al, 2017 ). More recently, “softer” implications of AVs for travel behavior, accessibility, culture, digital divide, health, land use, and social equity have attracted the attention of the research community, acknowledging the possibility of longer term unanticipated ( Mladenović, 2019 ) socio-technical changes because of this emerging mobility technology ( Cohn et al, 2019 ; Curl and Fitt, 2019 ; Gelauff et al, 2019 ; Milakis, 2019 ; Milakis et al, 2018 ; Milakis and van Wee, 2019 ; Soteropoulos et al, 2019 ; Thomopoulos and Givoni, 2015 ; Whittle et al, 2019 ; Zhang and Guhathakurta, 2018 ). Several scholars argue for more active governance of smart mobility ensuring and enhancing public value (i.e., maximizing individual benefits such as access to opportunities, while ensuring inclusive distribution of such benefits and minimizing adverse impacts to the environment and public health) ( Docherty et al, 2018 ; Lyons, 2018 ; Pangbourne et al, 2020 ).…”