Literature Reviews

When electrified, automated heavy-duty trucks can have dramatic reductions in air pollutant emissions that harm human health. A lifecycle analysis study found that the health impact costs of an automated diesel heavy duty truck were twice that of an automated electric heavy-duty truck, and that the automated electric truck caused 18 percent fewer fatalities compared to the automated diesel truck [1].

A 2024 study modeled reductions in damages from air pollution from the introduction of automation and partial electrification in long haul trucking, finding that for long haul routes under 300 miles, electrification reduces air pollution and greenhouse gas damages by 13 percent, and for routes above 300 miles, electrification of only urban segments facilitated by hub-based automation of highway driving reduces damages by 35 percent [2].

To date, much of the research related to health and vehicle automation has focused on passenger vehicles. Additional research is needed to understand potential health impacts of heavy-duty vehicle automation beyond reductions in air pollution, as well as of different types of heavy-duty vehicles and adoption scenarios.

Autonomous vehicles have the potential to reduce fuel consumption through automated acceleration and braking, platooning to reduce air resistance, vehicle design, fuel switching, routing efficiency, and traffic congestion reduction [1]. However, there is also the possibility that automation of vehicles will lead to increase in vehicle usage, and subsequently fuel consumption and emissions [1].

The effect of automation of heavy-duty vehicles can reduce energy consumption and benefit the environment, depending on the fuel source [2]. One study estimated that an automated diesel heavy duty truck reduces greenhouse gas emissions by 10 percent compared to a conventional heavy-duty truck [2]. Meanwhile, an automated battery electric heavy duty truck would reduce life cycle greenhouse gas emissions by 60 percent compared to the conventional heavy-duty truck [2]. However, there are trade-offs between fuel sources for automated heavy-duty trucks, including the mineral resource losses [2]; the battery manufacturing required for automated electric heavy-duty trucks increase mineral intensity significantly compared to automated diesel heavy-duty trucks [2]. Additionally, automation decreases energy intensity of heavy-duty trucks, which decreases through automation, the increase in power generation required for electrified heavy duty trucks may outweigh the benefits from automation [2].

Further research is needed related to the effect of different electricity generation methods on automated heavy-duty truck emissions, as well as with different vehicle design and weight assumptions. Research is also needed related to environmental effects of other heavy-duty vehicles and equipment, such as automated buses and specialized equipment.

Research is sparse regarding the effects of heavy duty applications of C(AV)s on municipal budgets. However, a research project at the University of Oregon studied how autonomous vehicles will change local government finances, using waste collection as a case study [1]. The case study analyzed costs in Asheville and Chapel Hill and found that in the long term moving solid waste refuse collection to automated vehicles and more highly automated systems could create large cost savings [1].

Heavy-duty automated vehicles (AVs) could potentially reduce emissions and improve social equity by reducing disparities of residents’ exposure to vehicle emissions and associated health risks. The environmental impacts from heavy-duty vehicles diesel exhaust are particularly severe for residents living close to roadways with heavy truck traffic, such as freeways and major arterial routes in goods movement corridors [1]. Research consistently shows that communities of color and low-income groups are disproportionately situated in areas affected by freight traffic [2], [3], [4]. Patterson and Harley [1] shows that trucks with emission control strategies could result in decreased exposure disparities for pollutants quantified by the intake differentials of two corridors in the San Francisco Bay Area. Operations for designated truck routes, and restrictions on truck parking and engine idling in or near residential neighborhoods can also mitigate the disparities of traffic-related air pollution [5], and automation of heavy-duty vehicles can facilitate the enforcement of these regulations, leading to a more equitable distribution of environmental impacts.
The advent of heavy-duty AVs could also affect employment by disproportionately affecting low-wage jobs in traditional employment sectors. A key concern is the potential displacement of truck drivers [6], [7]. Nikitas et al., [8] concluded that AVs could generate labor market disruption and new layers of employment-related social exclusion based on an online survey of 773 responses from an international audience. Fleming [9] indicated that the technological unemployment on truck drivers will have less economic impact due to the current shortage of truck drivers and aging workforce. Nevertheless, it is crucial for policymakers and urban planners to develop robust retraining programs to prevent these workers from being replaced by higher-wage tech employees.
Overall, heavy-duty AVs have multiple benefits such as reduced driver costs for freight transported by trucks [10], saved fuel consumption and emissions due to platooning and smoother driving [11], [12], [13], and increased safety [14]. However, the study on the equity impacts of heavy-duty vehicles is sparse. Current areas for future research include: 1) exploring the environmental impacts of heavy-duty AV operations, 2) examining the effects of heavy-duty AVs on job markets and identifying effective retraining programs for displaced workers, and 3) analyzing the disparities in potential benefits and risks that heavy-duty AVs pose to different socioeconomic groups.

Studies considering the impacts of automated trucks on the workforce find that automation may first effect long-haul trucking or over-the-road drivers [1]. These drivers travel throughout a region or the continental United States for work, typically on a limited set of federal interstates and highways. Wang et al. [2] suggest assessing the potential for job displacement by looking at growth in alternative positions with similar requirements for skills, knowledge, and abilities in a truck operator’s home state. According to their study, only 10 states have sufficient alternative employment opportunities to absorb a greater than 15% displacement in truck driving jobs, indicating a need for worker retraining if trucking displacements.

A survey of trucking logistics managers, supervisors, and drivers found that drivers were the most likely to believe that automated trucks would reduce the size of the U.S. truck driving workforce (62%), followed by supervisors (50%), and managers (25%) [3]. Interviewees in this study noted that they thought the introduction of additional technologies into trucking, such as automation, would lead to a shift towards younger drivers rather than older drivers.

Scholars have posited that freight transfer hubs will be placed near interstate highways and on the fringes of regions where automated trucks drop trailers to be picked up by human-operated trucks [1], [2]. However, this is an emerging area of practice and available research has not yet considered implications for land use.

Vehicle automation can reduce the risk of crashes from driver factors, such as fatigue, impairment, distraction, or aggression, which are the cause of or contribute to over 90 percent of all vehicle crashes [1]. Common reasons for single vehicle truck crashes include driving too fast for conditions or curves, falling asleep at the wheel, and vehicle component failures or cargo shifts [2]. For lower levels of vehicle automation, systems that include speed advisories, automatic speed adjustments, driver alertness monitoring, and safe stop ability in the event a driver becomes non-responsive could improve safety [2]. Potential negative safety effects of partial-automation systems like adaptive cruise control include a false sense of security and inattentive drivers [3].

Higher levels (Levels 4 & 5) of heavy-duty vehicle automation have potential to improve safety more dramatically by eliminating human error [3]. However, the technology is still advancing for heavy duty vehicles, and additional safety testing is needed before Level 4 freight trucks are commercially deployed at-scale [3], [4]. Vehicle platooning where trucks travel in a group and the vehicles in the center do not all require drivers is a potential intermediary step towards fully driverless vehicles [3].

Additional research is needed to understand how vehicle platooning, higher levels of vehicle automation (Levels 4 and 5), vehicle designs and weights, and types of heavy-duty vehicles (e.g., buses and specialized equipment) will impact safety and vehicle crash rates.

The transportation industry is changing rapidly due to technological advances. As a result, skillsets have diversified and expanded, requiring education and workforce development to adapt to these needs. Labor market research has shown that low-skilled workers tend to be most affected by the technological substitution of labor driven by new technologies such as automation [1]. New training tools are needed to equip the future workforce with the technical, adaptation, and capacity skills needed to react to the evolving industry [2].

There is limited research on workforce development specific to a transportation mode such as micromobility. Overall, the literature on transportation and workforce development recommends partnerships with industry and academia, increasing investment in workforce development, integrating training to pre-apprentice and apprentice programs, and collecting data to inform policies and decision-making [1], [3].

Early operations of shared e-micromobility services relied heavily on independent contractors, with one account estimating 40 percent of Bird’s operational costs at one point went towards workers to collect, charge, and distribute dockless e-scooter and bikes [4] . In 2019, California passed a law (AB5) reclassifying who could be considered independent contractors, shifting the labor market toward third party companies and away from part-time workers [5]. Future research could investigate how regulation of independent contractors has influenced the micromobility workforce.

The social equity impacts of micromobility programs are somewhat mixed. In demographic analyses of bikeshare and scooter share riders in developed countries, studies often find that riders are, based on their income, education, youth or able-bodied status, relatively privileged [1], [2]. Though low-income travelers may be less likely to adopt bikeshare, those who do may use them more intensively and for more trip purposes than more affluent users [3], [4]. Shared micromobility programs designed with docked stations tend to be particularly unequally distributed geographically relative to dockless systems [5]. In light of these demographic and geographic imbalances, it is not uncommon for agencies to impose equity requirements in shared micromobility programs [6]. Social equity research in micromobility focuses on two main components 1) how to incentivize low-income and underrepresented groups to use the services (with a focus on policy measures or direct subsidies linked to spatial equity) and 2) how to include diverse voices in the planning process. Policy analysis is largely linked to geospatial distribution of access to bikeshare, scooter-share, and carshare [7], [8], [9].

Shared micromobility offers an alternative to private driving and thus displaces driving trips that make roads more dangerous and pollute air for everyone. And, it has the added benefit of providing job access and improved health outcomes [10], [11].

The effects of micromobility modes on sustainability goals are mixed. A literature review by
McQueen et al [1] defined micromobility modes as “small, lightweight human-powered or electric vehicles operated at low speeds, including docked and dockless e-scooters and bike share systems,” and found mixed results of the modes’ effects across three key sustainability goals – reducing greenhouse gas emissions, equitable and reliable operations, and enhancement of the human experience. Regarding greenhouse gas emissions, the review concluded that micromobility modes have the potential to decrease emissions when serving as a substitute for automobile trips. One way this can occur is by complementing transit; making it more accessible and convenient and therefore more competitive with automobile trips. However, the review also found that micromobility trips often replace walking or transit trips, thus increasing emissions [2].

Municipalities see a human benefit to offering alternative modes. Research around perceptions of new mobility has found them to be a pleasant experience, especially for electrified mobility, although many of the studies are focused on e-bikes [3], [4]. Additionally, a significant amount of research focuses on the integration of micromobility with public transportation. The body of work related to this topic generally spans four study areas - policy, sustainability, interactions between shared micromobility and public transit, and infrastructure [5]. Improving first/last mile access and network efficiency is also a major focus area [6], [7]. Future research should focus on sustainability through business models analysis, comparing public and private operations and how best to navigate regulatory burdens surrounding the deployment of such services.

Ride-hail, also known as Transportation Network Companies (TNC), may alleviate the high cost of car ownership and reduce mobility gaps across socioeconomic divides by providing people with car trips on an as-needed basis. While the socioeconomic characteristics of ride-hail users vary by region, studies often find that users earn higher incomes than the average resident [1]. However, a small portion of all ride-hail users in California suggests frequent users, those who ride more than three times per week, are more likely to not own a car and earn low-income than those who ride less or non-users [2]. Trip data suggest that most ride-hail users request service only for special occasions which averages three trips per month or less instead of relying on ride-hail for regular travel.

In addition to supporting mobility needs among car-free or car-light households, ride-hail may also address issues of racial bias among taxi drivers. Brown [3] found that Black users were more likely to have a taxi trip canceled or a longer wait than white users; ride-hail exhibited no such ethnic/racial gap in service quality. However, important gaps in access to ride-hail services remain. The benefits of ride-hail can only be seen in jurisdictions that allow them and in markets that support them. For instance, users in rural areas with low population densities and destinations spread far apart account for a small minority of riders [4].

Ride-hail drivers, part of the gig economy, are contracted as independent employees and often lack legal protection on labor rights and employment benefits that would be offered to traditional employees [1]. Existing research on ride-hail drivers focuses on the labor conditions of the workforce and understanding the motives behind becoming a ride-hail driver. Research reveals ride-hail drivers attract a diverse group of populations. According to Benner [1], 78 percent of the workforce is people of color and 56 percent are immigrants. Hall [2] concludes drivers are attracted to gig work due to schedule flexibility and additional income outside of their full-time or part-time jobs. There is limited research on the interests and capabilities of current workers in order to develop effective workforce development programs that will empower drivers to take collective action [3]. The current research suggests workforce development tools should also be aimed towards individuals outside the gig workforce, self-employed individuals, or platform workers [3]. While the industry lacks widespread collective action among drivers, many drivers have taken to various strategies to advocate for themselves such as business planning, leveraging platform competition, activism through social media, and using technology to manage the workforce [3].

Transportation Network Companies (TNCs), or ride-hail companies, have the potential to reduce emissions by reducing single-occupancy trip distances through pooled rides and reducing the need for private vehicle ownership. Ride-hail services can also support transit use by providing riders with an option to connect to transit stations, and by complementing transit in times and places it does not operate.

Ride-hail services can, in theory, reduce emissions by linking passengers traveling in similar directions. In practice, however, those benefits are limited. Most trips are not pooled; one study found just ten percent of trips were pooled, and 27 percent involved multiple passengers [1]. Deadheading, or trips made with no passenger in the vehicle (often between where one passenger is dropped off and the next is picked up), contributes to additional emissions. A significant portion of ride-hail trip miles (40 percent, from a study of TNCs in Canada) are deadheading trips. Both pooled and unpooled ride-hail trips emit more pollutants relative to trips taken by single-occupancy vehicles [1].

Ride-hail might also reduce emissions by offering an alternative to private vehicle ownership, or by connecting riders to transit stations. The evidence is mixed regarding the extent to which riders substitute ride-hail for public transit, with studies finding that ride-hail reduces net transit ridership between 14 and 58 percent, depending on the city studied and the type of transit [2], [3]. The more abundant and reliable ride-hail becomes, particularly in urban areas with a rich array of alternative travel modes, the more likely people are to willingly shed their private vehicles [4]. Moreover, electrifying ride-hail can go a long way toward reducing greenhouse gas emissions, particularly electrifying vehicles for full-time ride-hail drivers [1], [5].

Ride-hail use varies both by land use and demographics. In general, people are more likely to use ride hail services in dense, urban areas [1], [2]. Ride-hail users in the United States tend to own fewer cars, and are more likely to use public transit, than the average resident [2]. There are exceptions, notably Los Angeles, where ride hailing is popular in both urban and lower-density neighborhoods [3]. A separate study from California found that people in lower density suburban and rural areas who used ride hail services tended to earn higher incomes; in contrast, urban ride hail users tended to earn lower-incomes [4].

Given that ride-hail trips are more frequent in urban areas, it is unsurprising that places with high rates of ride-hail use also tend to have high rates of street parking occupancy [5]. Ride-hail has the potential to alleviate curb congestion if a sufficient threshold of car trips are replaced. Ride-hail users may select the service specifically to avoid cruising for parking where few curb spots are available, and thus free up a longer-term parking spot [5]. However, those freed up spots may quickly be taken up by drivers who would otherwise have parked elsewhere, parked at a different time, or not made the trip by private vehicle at all. Moreover, ride-hail drivers must compete for curb access when dropping off riders, and thus temporarily congest the curb. Additional research is needed to better understand the impacts of ride-hail on land use and curb congestion.

Several researchers have tried to understand the effects of ride-hailing on transportation system performance related metrics such as vehicle miles traveled (VMT) [1], [2], [3]. Most studies are in agreement that Transportation Network Companies increase VMT and decrease public transit ridership [1], [2], [3], [4]. For example, Wu and MacKenzie (2021) used the 2017 National Household Travel Survey (NHTS) along with causal inference to estimate the effect of ride-hailing services on VMT. They concluded that a net 7.8 million daily VMT or 2.8 billion annual VMT were added nationwide due to ride-hailing services at the time of the 2017 NHTS data collection [1]. Other studies have tried to understand the effect of congestion pricing strategies on ride-hailing ridership [1]. For example, Zheng et al. (2023) estimated the effects of ride-hailing congestion pricing policy on ridership in Chicago and concluded that the policy led to a growth in shared trips and a decline in single trips. Some studies have also tried to understand the effects of ride-hailing on transit and other modes of transportation [1], [2], [3].
Current opportunities for future research include: 1) using more updated data (e.g., 2022 NHTS) to assess the effects of ride-hailing on VMT and travel behavior, as the impact of ride-hailing services changes dynamically, and 2) assessing the impact of ride-hailing services in rural areas and less studied regions of the country, which could provide insights for local and state policies.

A shift from dining in to at-home consumption can produce additional food packaging waste [1]. On-demand meal delivery may also affect travel activity, potentially increasing emissions. A study of delivery data in London, United Kingdom found that meal delivery by vehicle is “highly energy inefficient, producing 11 times more GHG [greenhouse gas emissions] per meal delivered by vehicle than by bicycle” [2]. However, this study did not identify if any travel activity was displaced by the substitution of meal delivery services; future research could explore if customers order from locations further away or substitute meal delivery for home cooking, activities that would increase energy consumption and resultant emissions. Policies to support bicycle use for delivery services can mitigate these increases [3], [4].

For robotic delivery services, the literature shows that the energy consumption and emissions of robotic delivery services do not necessarily outperform traditional ones, and are related to delivery distance, electrification, and operation [1], [5], [6].

On-demand delivery services have been shown to have a significant impact on transportation systems, both positively and negatively [1]. On the positive side, modern delivery services could reduce shopping trips to physical stores and related energy consumption [2] and greenhouse gas emissions [3]. Emissions from delivery services vary based on delivery scheduling [4], service coverage area [5], engine type (e.g., combustion or electric), and efficiency of cooling equipment [6]. On the negative side, the increasing number of delivery vehicles adds to crash risk in the transportation system, particularly for road users [7]. In addition, the delivery vehicles compete for limited curbside space in the urban area [8], [9].

Research on the impact of robotic delivery services on transportation systems is predominantly theoretical, due to scarce empirical evidence. The City of Pittsburgh [10] operated a six-month pilot program with Kiwibot and deployed a limited number of devices (less than 10 at any time) to deliver packages. Different from package delivery robots, which mostly operate on sidewalks and have a limited influence on the road traffic, future autonomous delivery vehicles could exert a huge impact on the traffic systems. Studies showed mixed results about the effects of autonomous vehicles on traffic flow efficiency, both positive and negative, depending on their modeling conditions [11].

Ghost kitchens, or restaurants without dining space that focus on online food orders, can reduce overhead costs from front-of-house staff and single-facility expenses [1]). This may affect the demand for hospitality workers and food service establishments in a jurisdiction.

One workforce-related concern for gig economy workers, who are independent contractors, is that they will be exploited if they become overly-dependent on a single platform [2] . Delivery service workers can increase their revenues by strategically switching between services (known as multihoming) and repositioning their locations to areas of high demand [3].

On-Demand Delivery Services can provide ride hail drivers with an alternative platform for gig work, and ride hail and delivery platforms must compete for workers, as Liu and Li [4] illustrate below:

Some researchers indicate that environmental impacts of automated vehicles (AVs) strongly depend on the connectivity and market penetration rates [1], [2], [3], [4]. For example, Mattas et al., [5] shows that with dense traffic, AVs that lack interconnectivity are likely to reduce speed in adherence to safety and comfort guidelines, consequently producing an additional 11 percent in emissions. Wadud et al. [6] developed an energy decomposition framework and quantified the potential percentage change of greenhouse gas (GHG) emissions from AVs depending on energy intensity effect, travel demand effects and net effects of automation. Wadud et al. [6] concluded that vehicle automation offers the potential to reduce light-duty energy consumption by nearly half, but this decrease is dependent on several factors including the degree to which energy-saving algorithms and design changes are implemented into practice and policy responses at federal, state, and local agencies, among others.

While AVs could induce demand due to easier travel and the empty travel generated from shared AV fleets [7], [8], [9], most studies show energy savings despite the Vehicle-Miles-Traveled (VMT) increase [10], [11], [12]. For example, Fagnant and Kockleman [11] estimated that shared autonomous vehicles (SAVs) may save 10 times the number of cars needed for personally owned vehicles travel but increase daily VMT by about 11 percent from empty vehicle travel. The energy use and GHG emissions could be reduced by 12 percent and 5.6 percent respectively, owing to changes in total number of vehicle starts, lower proportion of cold starts, and reduced parking needs. However, some studies also indicated an increase of emissions considering different AV penetration rates [13], [14], [15]. For example, Harper et al. [16] estimated that privately owned AVs searching for cheaper parking could increase light-duty energy use in Seattle by up to 2 percent.

In general, most studies conclude that AVs would reduce energy consumption and GHG emissions per mile driven due to improvements in operational efficiencies such as automated eco-driving, changes in vehicle size, and traffic smoothing, but there is not a clear consensus that these efficiency improvements will reduce total energy use and emissions. Current areas for future research include: 1) studying the full lifecycle environmental impacts of AVs, 2) investigating models that capture the full complexity of real-world scenarios such as dynamic traffic patterns, diverse weather conditions, varying road types, and unpredictable human behavior, 3) exploring how a fleet of electric AVs might interact with power grids, especially concerning charging demands and renewable energy integration, 4) exploring if the operational efficiencies gained from AVs, lower emissions and energy use remain as trip making and VMT increases due to empty, longer, and/or easier travel [17], [18].

Many studies show that Autonomous Vehicles (AVs) could change the layout of urban areas [1], [2], [3], potentially leading to dispersed development or densification of cities. By lowering travel expenses, AVs could influence residential and work locations, potentially leading to more pronounced urban sprawl. For example, Moore et al., [4] used a web-based survey of commuters in 2017 in the Dallas-Fort Worth Metropolitan Area (DFW) and predicted a substantial extent of urban sprawl up to a 68 percent increase in the horizontal spread of cities due to AVs. AVs could also increase urban density by decreasing the need for parking, leading to more dense and mixed use development.

AV could increase trip lengths and induce suburban and exurban development [5], [6], [7], [8]. Nadafianshahamabadi et al., [9] utilized an integrated model of land use, travel demand, and air quality. The modeling is designed for the Albuquerque, New Mexico metropolitan area to demonstrate that AVs encourage development at the urban fringe. While jobs and population typically migrate outward in tandem, trip lengths and overall travel demand continue to rise due to the relatively low density in these emerging areas compared to traditional urban employment centers. Similarly, Gelauff et al. [10] used equilibrium model to simulate spatial effects of AVs and found that population tends to increase in large metropolises and their suburbs, at the expense of smaller cities and non-urban regions given high automation with good public transport systems in Netherlands. Carrese et al. [11] used discrete choice modeling and traffic simulation to study the residential relocation due to different time perception. Results show that about 40 percent of respondents would move to the suburbs under the AV regime in Rome, Italy, and travel time would increase by 12 percent for suburban resident commuters.

Besides contributing to the development of new peripheral centers, AV has the potential to densify the existing urban landscape by reallocating space for residential, economic, and leisure activities [12]. Zakharenko [1] concluded that with the introduction of AVs, the need for daytime parking may shift to outlying areas, which would allow for denser economic activity and increased land rents in downtown areas. As AVs potentially reduce car ownership, it's anticipated that less space will be required for parking, which could give rise to more high-density and mixed-use developments [13], [14], [15]. Zhang and Guhathakurta [16] developed a discrete event simulation model to assess the impact of Shared AVs (SAVs) on urban parking land use in Atlanta, Georgia and concluded that SAV can reduce parking land by 4.5 percent at a 5 percent market penetration level and each SAV can emancipate more than 20 parking spaces. However, some research indicates that vehicles are traveling longer distances daily, and there could be an increase in parking space on the outskirts [17], [18].

In general, most studies found that private AVs can potentially lead to dispersed urban development, while SAVs are expected to contribute to densification of city centers. Current areas for future research include: 1) AV effects on people's residential and employment location decisions, recreation spaces and supply of infrastructure. 2) long-term effects of AVs on urban land use patterns to promote AV adoption with efficient use of land. 3) infrastructure adaptation to fully accommodate the new traffic dynamics and parking needs introduced by AVs [19].