Literature Reviews

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.

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.

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 (CVs), connected autonomous vehicles (CAVs), and Vehicle-to-Everything (V2X) technologies can improve transportation system operations and efficiency. For example, Guler et. al [1] used simulations to assess the potential for CVs to optimize intersection efficiency, finding that CVs could reduce delays by up to 60 percent. The reductions in intersection delays were up to 7 percent greater for CAVs compared to CVs controlled by drivers [1]. Platooning technology can reduce fuel consumption and smooth traffic oscillation [2], and there is a growing body of research around autonomous intersection management [3], [3], [4], [5]. Vehicle to infrastructure (V2I) technology can also improve traffic efficiency by optimizing traffic signal control [6].

A significant body of research exists on how to optimize traffic and safety using connective technology, but it is primarily based on simulations since real-world data is limited [1].