Literature Reviews

Automated vehicle technologies hold significant promise for benefiting vulnerable populations and bridging urban-rural disparities. Demographically, numerous studies highlight the potential of automated vehicles to improve mobility for people with disabilities, elderly individuals, and low-income populations by offering accessible and affordable transportation options [1], [2], [3], [4], [5].
Automated vehicles offer a game-changing solution for individuals with disabilities, including those with vision impairments [6], [7], [8], cognitive impairments [9], [10], [11], or limited mobility [12], [13], [14]. Equipped with advanced sensors and navigation systems, these vehicles could provide safe and reliable transportation for people with disabilities. They could incorporate user-friendly interfaces and assistive technologies, such as wheelchair ramps and voice-activated controls, to ensure accessibility and ease of use [15], [16], [17]. By removing physical barriers and offering personalized assistance, automated vehicles empower individuals with disabilities to travel independently and participate more fully in their communities.
Geographically, the deployment of automated vehicles has the potential to address “transportation deserts” in small urban, rural, or remote areas, providing residents with access to essential services and opportunities that were previously out of reach [18], [19], [20]. For rural areas, where transportation infrastructure may be lacking and population densities are lower, automated vehicles, like other shared ride services, could provide on-demand mobility options and connect residents to employment opportunities, healthcare services, and education centers [21]. Similarly, in small urban areas, where public transportation may be less extensive compared to larger cities, automated vehicles could serve as a flexible and efficient transportation solution, improving mobility and access to resources for residents.
However, the literature also emphasizes the need for careful planning and implementation to ensure that these technologies do not exacerbate existing inequalities. Concerns such as the digital divide [22], [23], [24], affordability [1], [25], [26], [27], and infrastructure limitations [28], [29], [30], [31] in rural and small urban areas must be addressed to ensure that the benefits of automation are equitably distributed across demographic and geographic lines. In addition, the literature emphasizes the importance of community engagement and inclusive planning processes to ensure that the deployment of automated vehicle technologies is responsive to the needs and priorities of diverse communities [18], [32], [33], [34].

Micromobility works best when the land use and transportation system supports it. The typical scooter share or bikeshare trip is under two miles and takes 11-12 minutes [1]. Micromobility - both manually-powered or electric-powered - may be faster than walking, but nonetheless slower than driving, and leaves users exposed to the elements and street traffic. Streets that are well-connected [2] and dense with a mix of establishments and residences, and robust transit options shorten trip distances and times, and, in turn, facilitate micromobility trips. A meta-analysis of shared micromobility programs found that ridership increased with population density, employment density, bus stops and metro stations, and bike infrastructure [3]. In contrast, low-density neighborhoods with few young people and zero-car households have less access to micromobility services [4]. In the longer run, micromobility may ultimately impact land use by providing more transportation nodes and extending the reach of shared transportation services [5]. A floating bikeshare or carshare service, for example, may enable residents in outlying urban areas to connect to a city’s fixed-route transit system.

Carshare’s emissions and fuel consumption depend on several factors, including the types of trips they are substituting for, how many new trips they generate, and how the vehicles are fueled. User demographics play a key role in determining these factors; for example, if people are primarily joining a carshare to shed their private car or delay purchasing one and thus reduce their overall car trips, then the program may reduce emissions [1]. If, however, users generally come from car-less or car-lite households, joining the carshare program may create more trips than subtract them, and thus may increase emissions. Carshare programs that use electric vehicles, such as BlueLA, reduce tailpipe emissions [2]. Electric carshare programs also expose users to cleaner vehicle types, and carshare users are more likely to later select electric cars when purchasing a vehicle than non-users [2]. However, even zero-emission vehicles generate fine air particulate emissions from tire friction, which worsens local air quality.

Carshares can offer emissions and energy savings by reducing net private automobile travel and by replacing more polluting private fleets with cleaner shared fleets. While some carshare trips replace public transit trips, in aggregate and when taking the life cycle energy and emissions impacts into account, carshares reduce net household greenhouse gas emissions [3]. One study found a majority of surveyed North American carshare users traveled more by car after joining the program, thus increasing emissions, but that those emissions increases were outweighed by emissions saved by users who gave up their personal cars [1]. Studies find that a carshare vehicle tends to replace approximately 15 private vehicles [4], [5]. Carshare vehicles may also be more fuel efficient than the overall private car fleet [6]. Another study, based on the same survey of North American carshare users, found a significant increase in walking, cycling and carpooling among people who joined a carshare [7].

While the market for zero emissions vehicles is growing, zero emission vehicles still exceed the budgets of some low-income potential drivers. Electric carshare programs offer a low-risk way to improve access to clean mobility among car-light or car-free households, without requiring users to pay the full costs of owning and maintaining their own electric vehicle [8].

In disadvantaged communities with especially high rates of air pollution, electric carshare programs may help reduce localized air pollution [9].

Following the historical research gaps on carsharing, Shaheen [1] recommended longitudinal monitoring to better understand market developments and social and environmental impacts due to growth and policymakers’ interests. For a brief period of time, carshare literature focused on workforce development and labor conditions related to rebalancing in one-way carsharing systems [2]. Today, carsharing evolving with the rise of shared autonomous vehicles have created a gap in research. More research is needed to understand how drivers, barriers, and carsharing will be impacted with autonomous vehicles [3]. Chan and Shaheen [4] predict that carsharing in the next decade will include greater interoperability among services, technology integration and stronger policy support [4]. Understanding how carsharing will develop and its impact can help inform policy related to education and workforce development. However, literature explicitly related to education and workforce development was nonexistent which reveals a major research gap.

A review of the literature yielded no social equity concerns that were independent of workforce-related issues. Those issues are covered under the heading “Education and Workforce.”

A review of the literature using Google Scholar and ProQuest yielded no applicable research, indicating a probable gap in the literature.

No specific literature was found; rather the focus of the literature was on the general concerns of how workers with low skills and low wages will be affected by technological substitution and how to manage the transfer of skills.

There is no available literature studying the effect of Universal Basic Mobility programs on safety.

While delivery service may impact wages and establishment creation, a review of the literature found no studies that considered impacts to municipal revenue through effects on municipal expenses, tax revenue, or nearby businesses.

Collectively referred to as connected and automated vehicles (CAVs), connected vehicles (CVs), which communicate wirelessly with one another, and automated vehicles (AVs), in which a computer partially or entirely replaces the driver, have the capacity to revolutionize road maintenance and transportation operations [1]. According to Egan Smith (Managing Director of the Intelligent Transportation Systems (ITS) Joint Program Office of the United States Department of Transportation), "Successful deployment and operation of these new technologies depend largely on a knowledgeable, trained, and skilled workforce to support them” [2].

According to the California Department of Transportation's (Caltrans) strategic strategy, workforce development is a key action plan for CAV deployment [3]. Caltrans emphasized the importance of identifying labor difficulties and needs, as well as encouraging state efforts to recruit and retain the future workforce, in order to continue CAV. It could necessitate developing proper job categories, role descriptions, hiring procedures, and competitive salary ranges. Another option is to create a pool of highly skilled individuals (such as data scientists and network engineers) who can be housed in one functional unit and then transferred to other functional units or districts to share their technical expertise.

As CV and V2X technology advances, the Intelligent Transportation Systems (ITS) transportation workforce will require advanced knowledge, skills, and abilities. As a result, new and modified training opportunities are important for the ITS workforce to develop the advanced skill sets required to maintain a transportation network populated by evolving technologies [2].

Workforce development is essential not just for CAV deployment, but also for maintenance and repair (M&R). To stay up with technological advances, employees in this field must be upskilled and trained on a regular basis [4]. Crane et al. [5] also acknowledged that there is an increasing need to comprehend middle-skill positions, such as technicians, engineers, systems architects, managers, and IT specialists (that require at least a bachelor’s degree).

According to Parikh et al. [1], the most significant expense associated with CV deployment is the cost of labor for CV installation/deployment and people training. According to the author, operations and maintenance expenditures only account for about 20 percent of time, while the complexity of personnel training accounts for the other 80 percent.

The expansion of on-demand delivery services has been made possible by ghost kitchens and dark stores – grocery fulfillment centers which are located near consumers but are not open to customers [1]. These fulfillment centers have created new real estate opportunities. Several major ghost kitchen operators are known for building large portfolios out of warehouses, empty strip malls, or other storefronts near areas with growing on-demand food-delivery markets [1]. Restaurants are dispersing away from ground-floor locations in popular retail districts as ghost kitchens increase their urban real estate [1], [2].

One emerging area of study is the impact of on-demand delivery services on restaurant formation and viability. The services charge participating restaurants delivery fees as high as 30 percent of order value, though some cities have imposed caps of 15 percent [3].

Increasing adoption of automated vehicles (AVs) is likely to impact municipal budgets through reduced revenues, increased expenditures, and possible reduction in operating expenditures by adopting automated vehicles for municipal services.

Revenue reductions are likely to be caused by reduced parking revenues and traffic fines. A discussion of the potential impact of autonomous vehicle adoption on government finances for eight Canadian governments suggests a reduction in municipal parking revenues [1]. A combination of accelerated adoption of electric vehicles through a transition to automated vehicles would reduce tax receipts from gasoline and diesel fuels as well as parking, traffic violations, and other revenues by a range of 3 - 51 percent across 7 combinations of AV/EV/Shared scenarios in five Oregon Cities [2].

Increases in expenditures may come due to a decision to subsidize mobility and from infrastructure improvements. A survey of US officials' perspectives and preparations for automated vehicles suggests that cities and transportation agencies may seek to subsidize automated mobility for low-income individuals [3]. An Australian study found that some investment in infrastructure will be needed to accommodate AVs, but there is still uncertainty about needed expenditures [4].

However, cities may also reduce operating costs by performing municipal services with automated vehicles. One study estimates that using automated vehicles for trash collection could reduce operating costs by 32 - 63 percent [5].

On-demand delivery services can lead to an increase in demand for curb space, leading to congestion and double parking which can pose safety risks to pedestrians and other curb users [1], [2]. Existing research primarily considers the impacts of ride-hail/transportation network companies (TNCs) on demand for curb space and associated safety impacts [2]. Common TNC traffic violations that impact safety include not yielding to pedestrians or obstructing public transit lanes and driveways, which can cause other drivers or travelers to move into less safe areas [2]. Study on the safety impacts unique to on-demand delivery service may not be needed.

From limited observations of robotic delivery services in the City of Pittsburgh, there were only 17 incidents involving vehicles or pedestrians reported throughout the program. However, the limited number of devices deployed makes it challenging to ensure their safety at larger scales [3].

States, rather than local governments, have largely assumed responsibility for regulating ride-hail companies. Many states regulate how ride-hail companies can be taxed, and limit cities’ abilities to enact taxes and add fees to ride-hailing operations. Municipalities are thus constrained in their ability to leverage ride-hail services to generate revenues [1]. States vary in the extent to which they limit local control over ride-hail fees and taxes; Lehe et al. [2] examined the U.S. market and created a taxation taxonomy of five regimes: the first was a “hands-off” approach, the second, a tax-free regime to enact prohibit local and state taxes, the third, a state tax only system, fourth, a revenue sharing agreement based on state tax distributed to local jurisdictions, and lastly, a local options where local governments may levy a tax regulated by the state.

Clark and Brown found that repurposed parking spaces to accommodate ride-hail pickup and dropoff and falling parking occupancy reduce on-street and off-street parking occupancy revenues, which are often municipally-owned [3]. A study of New York City area airports found single-digit percentage reduction in parking demand attributable to the introduction of ride-hailing services [4].

Connected autonomous vehicles (CAVs) are expected to optimize energy efficiency due to improved operational efficiencies and by moderating movements of automated vehicles (AVs) through Cooperative Adaptive Cruise Control (CACC), platooning, eco-driving strategies, Vehicle-to-Everything (V2X) communication and incorporation of various dynamic routing systems [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]. For example, Djavadian et al., [16] proposed a dynamic multi-objective eco-routing strategy for connected & automated vehicles (CAVs) and implemented in a distributed traffic management system which shows the potential of reducing GHG and NOx emissions by 43 percent and 18.58 percent, respectively. Similarly, the eco-drive system for connected and automated vehicles proposed by Ma et al., [17] shows that more than 20 percent of fuel consumption can be saved. Mattas et al., [147] shows that while AVs lacking interconnectivity would likely increase emissions, a network of CAVs could lead to a decrease in carbon dioxide emissions of up to 5 percent.

V2X technology has potential to improve energy efficiency through applications such as traffic-light-to-vehicle communication, which can create energy savings and increased driving range [18]. However, vehicular communication systems also require infrastructure and energy to support [19]. Additional research is needed to understand potential environmental impacts of V2X technology, and whether there will be a net benefit when it comes to energy efficiency.

There is a body of research related to connected autonomous vehicles (CAVs) and land use, but with a focus on the implications of automating vehicles as opposed to the connective technology. There is little research focusing on how connected vehicles (CVs) and Vehicle to Everything (V2X) technology will impact land use demand and what policies may be needed to better integrate (CAVs) into existing transportation systems.

No studies were found looking at the direct impact between connected vehicles (CVs) and public health. However, a great deal of literature has studied how various CV applications, such as eco-driving, traffic signal optimization, and platooning, can reduce carbon dioxide emissions and various pollutants. For example, research indicates that eco-driving can lead to a reduction in fuel consumption by up to 10 percent [1]. Traffic signal optimization through Vehicle-to-Everything (V2X) communication can reduce fuel consumption and emissions by approximately 15 percent [2]. Additionally, platooning can reduce fuel consumption by up to 8 percent for trailing vehicles due to decreased aerodynamic drag [3]. The US Department of Transportation also developed a suite of eco-CV applications, including eco-approach and departure at signalized intersections, eco-traffic signal timing, and eco-lanes, which collectively could reduce carbon dioxide emissions by up to 12 percent [4]. Among these applications, half of them rely on human responses to various messages while the other half relies on automation. Emission reductions are primarily achieved through enhanced situational awareness (e.g., traffic signal status) ahead of time, allowing vehicles or humans to respond in a more eco-friendly way.

Pourrahmani et. al [5] conducted a health impact assessment of connected and autonomous vehicles (CAVs) in the San Francisco Bay Area, finding that road traffic injuries and deaths could be reduced significantly, that emissions could be reduced by CAV-enabled mechanisms like eco-driving, platooning, and engine performance adjustment. However, the study also found that CAV adoption could create negative health effects from reduced physical activity due to mode shift to car travel, in the absence of policies/efforts to mitigate potential health-related risks [5].

Because vehicle-to-everything (V2X), connected vehicle (CV), and connected autonomous vehicle (CAV) technologies have not been widely applied, there is little empirical evidence available about their social equity impacts. However, researchers have used scenario analysis to understand potential impacts. For example, a study from the Urban Mobility & Equity Center at Morgan State University investigated the mobility and equity impacts of connected vehicles as they relate to congestion through a simulated urban network, finding that “the gradual deployment of CVs can significantly improve mobility and equity while saving energy and reducing emissions” [1].
CAVs have the potential to foster an equitable future for disadvantaged communities by improving accessibility, or to create a transportation network that is accessible only to the privileged [2]. Past research [3] has suggested that if CAV policies with regards to social equity are not regulated, disadvantaged populations will face the burdens of lower accessibility and climate impacts. People with lower income, mobility challenges, and historically disadvantaged groups were identified by Cohen and Shirazi as the groups with significant potential benefits from social equity policies of CAVs [4]. Households with low income spend disproportionate amounts of income on transportation expenses [5], which can be reduced by social equity related policies of CAVs including the use of sharing policies for cost distribution over several passengers and/or having policies for the use of non-car modes for sharing such as buses and walking [6]. Shaheen et al. emphasized the importance of expanding active modes of transportation and transit to avoid the replacement of these services by CAVs [7]. Paddeu et al utilized survey participants for acceptability of CV and AVs. They observed in-vehicle security, safety, and affordability as critical factors for acceptability of these technologies [8].

People with age related disabilities would be greatest beneficiaries of CAVs. Claypool et al. proposed that designing CAVs with accommodation for disabilities could serve as a key factor for reducing the accessibility gap between people with and without disabilities [9]. People living in rural areas face challenges of limited walking and biking infrastructure, and transit inaccessibility. Furthermore, people without vehicle ownership or seniors and children have no mobility whatsoever. CAVs have the potential to improve accessibility in such rural areas and make travel more comfortable for rural residents [10]. Lempert et al. performed a scenario analysis to study the equity and accessibility benefits of connected vehicle technology in the United States by 2035, exploring three different scenarios: Mobility for All, Mobility in Transition, and Fragmented Mobility [11]. The Mobility for All scenario represented a future where CV, automated vehicle (AV), and electric vehicle (EV) technology transformed transportation to the benefit of the entire population by 2035, while in the Fragmented Mobility scenario benefits were assumed to accrue only at higher income levels. Mobility in Transition represented a scenario where technology was less advanced and widespread, but there was political commitment to reach underserved populations. The study found that connected vehicles have potential for significant social benefits, apart from the Fragmented Mobility scenario which would result in degradation of health, equity, and accessibility for most of the population [11].This was attributed to the fact that most benefits of CV arise from integration with automation and electrification.

Additional research is needed to understand the full range of how vehicle connectivity could influence social equity. This could include research on social equity benefits of using CAVs for shared mobility and their influence on active modes of travel. Furthermore, there is limited literature on social equity benefits of integrating CVs/CAVs with electric vehicles.

The rollout of connected vehicles (CVs), connected autonomous vehicles (CAVs), and vehicle-to-everything (V2X) technology will likely create new infrastructure and maintenance costs for cities, particularly in the short term. A discussion of the potential impact of autonomous vehicle adoption on government finances for eight Canadian governments suggests an increase in expenses for conduits and signals needed for connected infrastructure systems [1]. Additionally, platooning behavior may increase vehicle density, increasing the mass of vehicles on bridges and requiring additional inspection and possible retrofit, or new design approaches to accommodate increased weight [2].

However, connected vehicles will also bring new revenue opportunities, such as a VMT fee based on vehicle class enabled by vehicle-to-infrastructure (V2I) data transmission [2].

Connected Vehicles (CV) and Vehicle-to-Everything (V2X) communication systems are integral to modern transportation infrastructure, enhancing safety and efficiency by enabling vehicles to communicate with each other and with traffic management systems [1]. Historically, there has been uncertainty about the timeline for deployment of this technology, which stalled market adoption. Now that there is more clarity on the use of the safety spectrum (e.g., 30 MHz within the 5.9 GHz spectrum), and that the technology platform will include Long-Term Evolution (LTE) Cellular-V2X (LTE C-V2X), the time has come to accelerate the deployment of interoperable V2X connectivity to save energy and enhance safety. In October 2023, the U.S. Department of Transportation (DOT) released a draft deployment plan (“Savings Lives with Connectivity: A Plan to Accelerate V2X Deployment”) with short-,medium-, and long-term goals and targets to achieve interoperable connectivity at a national scale [2].

C-V2X has emerged as a more advanced technology, leveraging cellular networks for broader and more reliable communication. C-V2X includes both direct communication (device-to-device) and network communication (through cellular networks). Direct C-V2X, using PC5 mode (direct communication with vehicles or infrastructure, as described in the SAE J3161 family of standards [3]) in the 5.9 GHz band, enables real-time communication between vehicles and infrastructure without relying on cellular networks, ensuring low latency for critical safety applications. Network C-V2X (Cellular Uu mode (communications are transmitted through a cellular network, either 4G or 5G) utilizes cellular networks to connect vehicles with cloud-based services, providing a wider range of applications, including traffic management and infotainment [4].

Other forms of interoperable V2X connectivity includes unlicensed Wi-F, satellite and other emerging options such as ultra-wideband. The core of deployment has always been interoperability between diverse technologies and ensuring performance requirements for different applications.

Connected Automation represents the integration of connectivity and automation in vehicles, leading to the development of Connected Automated Vehicles (CAVs). This synergy enhances the capabilities of automated driving systems (ADS) by leveraging real-time data exchange.

The USDOT and the Federal Highway Administration (FHWA) are advancing cooperative driving automation through programs like CARMA [5], which focuses on enabling vehicles and infrastructure to work together using connected technology. This approach improves traffic flow and safety by allowing vehicles to share information about their movements and the surrounding environment. The Society of Automotive Engineers (SAE) also defines Cooperative Driving Automation (CDA) as systems that enable vehicles to cooperate through communication, enhancing the effectiveness of automated driving technologies [6].

Connected automation is not merely a combination of connectivity and automation; it involves sophisticated communication protocols and data sharing that enhance the automated driving stack. Key aspects include cooperative perception and cooperative maneuvering.

Cooperative perception involves sharing sensor data between vehicles and infrastructure to improve situational awareness. This is a non-trivial process because there are many real-world challenges in fusing data between multiple agents, such as delays and differences in data formats (i=e.g., different outputs of different autonomy stack).
Cooperative maneuvering involves coordinating vehicle actions to optimize traffic flow and safety. Applications include platooning, where vehicles travel closely together at coordinated speeds to improve roadway capacity and reduce aerodynamic drag (for trucks) and increase fuel efficiency; cooperative signal control, where traffic signals and vehicles communicate to optimize signal timings for smoother traffic flow; and speed harmonization, where vehicles adjust their speeds based on real-time traffic conditions to prevent congestion and reduce accidents. By integrating these applications, connected automation aims to create a more efficient, safer, and responsive transportation system.