Literature Reviews

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].

Universal Basic Mobility (UBM) programs, to the extent they have been piloted in the United States, have been funded largely through grants. These grants may be from municipal transit organizations, such as the Alameda County Transportation Commission’s funding of Oakland’s UBM pilot [1], state programs in conjunction with municipalities, such as the California Air Resource Board’s funding of the Los Angeles Department of Transportation UBM pilot [2], or a mix of grants and corporate giving, such as Pittsburgh/Move PGH’s collaboration with SPIN [3], which offered unlimited access to the company’s micromobility vehicles for qualified residents. As of this writing, no municipality has launched a dedicated fund for Universal Basic Mobility programs.

The costs of UBM pilots vary widely, depending on both the generosity of the subsidy and the number of participants. This variability has implications for the sustainability of such programs once grant funding expires. Oakland Department of Transportation’s UBM pilot grant is $243,000 for 500 residents [1], whereas the Los Angeles Department of Transportation’s UBM pilot is currently estimated at roughly $18,000,000 - a combination of city funded transit subsidies, corporate giving, and state funding of transportation infrastructure and mobility vouchers [2]. Municipalities are weighing permanent UBM funding pending evaluation of several UBM pilots, with the first evaluations coming this year; more research will be needed to evaluate the long-term implications of UBM programs on municipal budgets and transit organization financial sustainability.

There is little research available on the environmental impacts of Universal Basic Mobility (UBM) programs. In a qualitative evaluation of eight UBM programs and pilots, UC Davis researchers concluded that UBM pilot program participants increased transit use more than shared mobility relative to shared mobility services, and decreased overall personal vehicle travel [1]. These results suggest that UBM programs may reduce environmental harms of private vehicle use, but additional research is needed.

Carshare is particularly useful for people who either live in car-dependent areas and cannot afford a private vehicle, or for urban car-less people who enjoy a diverse array of transportation options that they supplement with driving for trips that require longer-distances, multiple stops, or more storage capacity [1], [2]. Carshare benefits from economies of scale in densely populated cities, and firms can more easily adjust their prices and grow their network flexibly according to consumer demand. Carshare is particularly effective when parking is scarce, there are transit hubs nearby, and land uses are mixed [3]. In lower-density areas, carshare can be more challenging to implement, as people are more likely to enjoy plentiful parking, own their own cars, and have fewer alternatives to driving that would make them more likely to choose to forgo private vehicles [4].

The limited research on carsharing and municipal budgets largely focuses on the tax burden of services in a community. High sales taxes on carshare program might, in the short term, bolster city budgets, but may in the longer run limit the financial sustainability of carshare programs. In a cost-benefits analysis of carshare sales taxes, one study found that sales tax revenue for carshare reservations typically exceeded the nominal sales tax rate [1]. An update to this study found that in keeping with this trend, as retail taxes increased, base price rates for carsharing dropped between 2021-2016, and, as a result, limited the long term sustainability and growth of the carshare sector [2]. Research is significantly lacking in understanding the benefits or costs to city governments and municipal budgets from such services, and how to balance municipal interests with long term sustainability and profitability of services.

By shifting mobility costs to a per-trip basis, carshare offers benefits for users in two categories: those with a car seeking to drive less (by offering access to a private vehicle without the need for ownership), and those without a car seeking to drive more (by reducing the upfront costs of private automobility). Carshare users tend to be car-less yet relatively affluent [1], which can be explained in part by where carshare stations are placed. Studies find that carshare stations are more likely to be located in higher-income neighborhoods with higher-than-average rates of employment and levels of education [2], [3]. Early carshare adopters tended to be white [4]. However, as the market has matured, recent evidence suggests that after controlling for income, Black and Asian travelers are more likely to use carshare than white travelers [5]. Carshare programs with public subsidies that enable reduced rates for eligible low-income residents are a promising policy solution; they can help people who could most benefit from additional automobility, while expanding carshare stations for all users [6].

The environmental benefits of demand-responsive transit and microtransit depend on the types of trips and vehicles they are replacing and generating. In theory, microtransit programs could pool passengers and thereby reduce emissions relative to drive-alone private vehicle trips [1], particularly if they use zero-emission vehicle technology. On-demand microtransit services tend to use vans that seat between four and twelve passengers. But empty vehicles [2], combined with vehicle miles lost to deadheading (trips with no passengers), can in some cases generate more emissions than private driving tips. Microtransit programs function as paratransit in some regions, and are notoriously expensive to provide in large part because they are often underutilized.

Demand responsive transit/microtransit programs in areas with limited public transit may offer a first- last-mile connection to transit, and thus enable less intensive car use. One study of suburban microtransit programs found that the majority of microtransit trips could not have been made with fixed-route public transit, and so microtransit largely either replaced ride-hail and private driving trips or generated new trips [3]. In particular, the study identified that microtransit induced trips among people without access to their own cars, and thus generated new vehicle miles traveled. More research is needed on the emissions and energy impacts of demand responsive transit and microtransit programs.