By Susan Shaheen and Adam Cohen
In recent years, on-demand passenger and courier services – known as Mobility on Demand (MOD) – have grown rapidly due to technology advancements; changing consumer patterns (both mobility and retail consumption); and a combination of economic, environmental, and social forces. MOD is an innovative concept based on the principle that transportation is a commodity where modes have economic values that are distinguishable in terms of cost, journey time, wait time, number of connections, convenience, and other attributes. Earlier this month, we wrote about innovations in goods delivery that are transforming transportation and consumer behavior as travelers increasingly turn to MOD. In this blog, we discuss four potential impacts of driverless vehicles and the need for proactive public policy to maximize the potential benefits and minimize potential adverse impacts.
Potential Impacts of Vehicle Automation
In the near future, automation could be the most transformative change transportation has seen since the advent of the automobile. While MOD is already impacting many cities, it has the potential to have even more notable impacts, particularly in four key areas:
Travel Behavior: It should be emphasized that the impacts of automation on travel behavior are uncertain and difficult to forecast due to a number of highly variable factors, most importantly societal acceptance and use. One potential outcome is that existing roadway capacity may increase due to more efficient operations associated with technology (e.g., closer vehicle spacing known as platooning, etc.). Additionally, operators could “right-size fleets,” providing consumers with vehicles sized based on the number of passengers and trip length. However, there is a possibility that automated vehicles (AVs) and shared AVs (SAVs) could induce demand by making motorized travel more convenient and affordable than personal driving. This could adversely impact congestion. Additionally, automation has the potential to fundamentally change historic relationships between public transportation and private vehicle use, which could support or detract from public transit ridership (we will discuss the future of public transportation in our next blog). In summary, the impacts of AVs on congestion will likely depend on whether the vehicles are predominantly shared or privately owned as well as public policy, such as pricing and restrictions on zero occupant vehicles.
Land Use and the Built Environment: AVs could result in reduced parking demand, particularly in urban centers that can create opportunities to repurpose urban parking with infill development. Infill development has the potential to increase urban densities and could in turn support higher-occupancy transportation modes. However, vehicle automation and telecommuting growth could also make longer commutes less burdensome, which could encourage suburban and exurban lifestyles.
Labor: Automation has the potential to reduce labor costs. However, automation is not likely to completely eliminate transportation jobs. With an aging population, we may likely need attendants to assist people with disabilities and older adults, security personnel, and a high-tech workforce to maintain an automated fleet.
Social Equity: While AVs have the potential to enhance access and economic opportunities for underserved communities, there are numerous challenges that could impact the equitable deployment of AVs. A few challenges could include: 1) affordability/payability (the services are simply too expensive for low-income households or require banking access); 2) availability (the services are not available equally in all neighborhoods); 3) accessibility (the services are not accessible to people with disabilities); and 4) digital poverty (the services require a smartphone or data plan to access). Additionally, AVs may employ machine learning and artificial intelligence that could create other equity concerns. While machine learning – if designed well — can help minimize human bias in decision making, it is also possible that such systems can also reinforce historic bias and discrimination in the transportation network. Just as humans learn to drive through experience, many perception algorithms use machine learning that is trained by events based on past experience. In a driverless vehicle future, machine learning may also impact where vehicles are pre-positioned, roam, charge, and other defining operational characteristics. Learning biases could create notable equity challenges in the future. There is a risk for discrimination when designing transportation algorithms for machine learning systems, including the potential for exclusionary transportation.
Need for Proactive Policy in a Driverless Vehicle Future
Public policy can have a notable influence on the success or potential challenges of driverless vehicles. Public agencies should consider proactively guiding public policy in four key areas to maximize the potential benefits of AVs:
Pricing: Public agencies should consider employing pricing based on occupancy, time of day, and congestion to encourage higher occupancy SAVs and discourage single- and zero-occupant vehicles.
Incentivizing Urban Growth and Urban Growth Boundaries: Metropolitan Planning Organizations, local governments, and other public agencies may want to consider policies that limit outward growth and encourage urban in-fill development to discourage the potential suburban and exurban growth pressure that AVs could create.
Workforce Development Programs: Local and state governments should develop workforce development programs designed to prepare for and respond to a driverless future. This should include a broad program encompassing job training/re-training and job placement resources to minimize the potential adverse labor impacts of vehicle automation.
A Comprehensive Equity Policy: Public agencies at all levels of government should consider a comprehensive equity policy to ensure SAVs are equally accessible and available to everyone. This should include policies that ensure access for people with disabilities, un- and under-banked households, low-income communities, households without access to smartphones or mobile data, and others. Additionally, this should include policies that prevent discrimination and bias from machine learning, artificial intelligence, and other systems that impact or guide the operations of AVs.
The public and private sectors, along with key stakeholders (e.g., non-governmental organizations, community-based organizations, and foundations) should partner to develop proactive policies to prevent and overcome these challenges. Proactive policy and research understanding will be critical to balance public goals with commercial interests and to harness and maximize the social and environmental effects of driverless vehicles.
Susan Shaheen and Adam Cohen are currently studying the impacts of connected and automated vehicles on state and local transportation agencies as part of the National Cooperative Highway Research Program (NCHRP) study 20-102(11).
Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.