Literature Reviews

Mobility-as-a-service business models rely on collection of personal and financial data, creating potential privacy and safety concerns for prospective users [1] [2]. There is little available research on how MaaS programs impact safety in practice.

There is still disagreement regarding what defines Mobility-as-a-Service (MaaS) as a business model, and research on how the implementation of MaaS would affect municipal budgets is limited. Many argue that to be successful, MaaS will have to develop a model that will be able to balance public and private providers in a sustainable manner [1], [2], but currently no such path exists. Doubts around the implementation of MaaS have been exacerbated by the recent failure of MaaS global [3]. The limited existing research on the budgetary impact from MaaS is based on revenue allocation models of economic spillovers from the deployment of such systems globally, rather than the direct impact of the presence of a MaaS system in a specific municipality [4].

Studies show that Mobility-as-a-Service (MaaS) could decrease the use and ownership of private vehicles and support a switch to active travel modes and transit [1], [2], [3]. However, the magnitude of this switch is not comprehensively explored among the literature [2]. According to one simulation study, MaaS could reduce emissions by up to 54 percent, depending on the modeling scenarios [4]. Another simulation study showed that MaaS could reduce transport-related energy consumption because of the introduction of car-sharing and bike-sharing services [5]. Another study suggested that MaaS could reduce vehicle miles traveled and related negative externalities [6].

Several research directions are promising for future studies. First, there are limited studies on what drives people to use MaaS, highlighting a need to explore user incentives to adoption. Understanding these factors can inform more targeted service design and marketing strategies. Second, modeling the integration of multi-modal travel within MaaS is crucial. This could offer insights into optimizing traffic flows and enhancing the environmental and social benefits of MaaS. Third, the collaborative mechanism between the public and private sectors in the MaaS ecosystem requires further examination. Investigating how these entities can better cooperate could foster the broader application of MaaS solutions.

Demand-responsive transit (DRT) and microtransit optimization has been studied using models and theoretical networks. From a strategic design perspective, continuous approximations of demand over time and space in highly theoretical networks were used to determine optimal flexible service types as a function of demand density [1], [2], [3], [4]. For tactical decision making, studies have used optimization methods in highly theoretical networks to optimize slack times [5], [6], longitudinal velocities [7], service cycle times [8], and compulsory stop selection and sequence [9]. Finally, from an operations standpoint, previous studies have evaluated policies such as dynamic stations [10], flag stops [4], point deviations[11], and optimal cycle lengths [12] in off-line settings. Few studies have also evaluated real-time operational strategies, such as optimal shuttle departure times [13] and routing/stopping decisions for rail connector services [14]. Generally, previous studies consider highly simplified or theoretical network conditions (e.g., grid networks, uniform travel times and uniform trip types), which can lead to suboptimal decision-making and unrealistic performance estimates. Though there are a number of DRT or microtransit pilots throughout the country, analysis and evaluation of real-world microtransit systems do not necessarily improve the overall system performance on efficiency, accessibility and financial sustainability. There is potential for DRT and microtransit service to be improved by innovative technologies, such as real-time demand prediction, real-time ride requests, coordination with both fixed-route mainline public transit and privately operated ride-hailing or mobility service. Both technologies of sensing, communication and service, and AI-powered algorithms could improve DRT and microtransit performance.

Demand-responsive transit/microtransit services can prove a cost-effective alternative to fixed-route services in rural and outlying areas where people and destinations are spread across large geographies, and the great majority of residents drive [1]. In those cases, a tailored, small scale on-demand service can flexibly meet the needs of a small group of riders better than a larger bus service that operates on a fixed schedule can. For rural transit agencies with a small budget, a microtransit pilot program offers an opportunity to lease vehicles and pay a third-party service provider to operate the program without spending the capital costs, liability and long-term labor costs associated with a permanent in-house microtransit operation. In contrast, in urban regions where densely clustered populations can more efficiently use fixed-route services, a microtransit program can bloat a transit agency’s budget. Traditionally, on-demand transit services have mostly been limited to paratransit rides, which due to low ridership rates (vehicles are often largely empty) and labor costs, are some of the most expensive services to provide per passenger [2].

The success of demand-responsive transit (DRT) and microtransit programs, measured by ridership, depends in part on land use. Whereas traditional fixed-route public transit services are most efficient in densely-populated areas, DRT/microtransit programs offer a smaller-scale alternative that can prove a more cost-effective solution in lower-density suburban and rural areas [1]. There are some exceptions: DRT/microtransit programs in urban areas are sometimes designed to complement fixed-route transit by providing a first-last mile solution to connect riders to transit, or by offering supplemental services for gaps in the transit network (during off-hours, or expanding the service area) [2]. However, evidence that DRT/microtransit can increase transit ridership in cities is mixed [3]. In rural and suburban areas, however, DRT/microtransit may serve particular demographic groups, such as older adults as a form of paratransit, and commuters who can collectively share a service to a specific jobs center [1]. In areas where vehicle ownership and use is high, and the DRT/microtransit service operates in a limited area, ridership can be low [1].

On-demand microtransit programs address four aspects of equity: geographic, temporal, economic and social equity. First, microtransit expands transit service by providing flexible routes in lower-density suburban and rural areas where fixed-route services are inefficient or cost-ineffective. Second, microtransit fills gaps in transit operating hours, such as late nights or weekends. Third is economic equity. Microtransit programs are often specifically designed to facilitate commutes and provide a lower-cost alternative to private driving to get to work [2]. Lastly, microtransit placed in disadvantaged neighborhoods can improve mobility access for people who can least afford cars, or people who face mobility barriers, such as elders, low-income individuals, and people with disabilities [3]. Findings are mixed as to whether microtransit programs offer a first-last mile solution to enhance transit ridership, or replace transit trips. Ridership outcomes depend on the specific demands and existing transportation alternatives [1].

Carsharing may reduce air pollution (and thus provide public health benefits) by complementing public transit use and providing a substitute for private car-ownership. While some people use carsharing to replace public transit, more people increase their public transit and non-motorized trips (like walking and biking) after joining carsharing [1]. A case study of carsharing in Palermo showed a 25 percent reduction in particulate matter (PM10) and 38 percent reduction in carbon dioxide emissions from the shift from private to shared cars [2]. Survey-based estimates have shown that a carshare vehicle tends to replace roughly 15 private vehicles [3], [4].
Carsharing may have also provided public health benefits related to the COVID-19 pandemic. At the beginning of the COVID-19 pandemic public transit was seen as high-risk for exposure, and people with high incomes disproportionately switched from public transit to cars [5], [6], [7]. Carsharing may have provided an alternative for people without a private vehicle, as surveys show that car sharing was preferred over public transit and taxis due to reduced exposure risk [8].
Areas for further research include the impact of carsharing on access to healthcare and other basic needs and services, as well as accessibility of carsharing across groups.

The rise in ride-hail apps and Transportation Network Companies (TNCs) has had mixed effects on public health.
One benefit of TNCs is the enhanced mobility they offer to people who have difficulty driving or navigating public transit, such as seniors and people with disabilities [1], [2]. Access to transportation constitutes a significant obstacle to medical care, particularly for older, lower-income, and non-white patients [3]. Piloted TNC non-emergency transportation initiatives have shown promise in addressing this issue [3]. One study found that ridesharing services make it easier for certain groups—young, low-income, and non-critical patients—to get to the emergency room [4]. Subsidized TNC programs can also help fill gaps in public transit service, providing a way to access medical care and groceries for lower-income travelers [5]. While there is potential for the use of TNC services in transit and special needs ride programs, significant barriers remain. For example, most vehicles cannot accommodate a full wheelchair and require the use of a smartphone app to request a ride [1], [6], [7]. Additionally, drivers may lack training in assisting people with disabilities [8].
Studies have correlated TNC ridesharing availability with decreased fatalities from alcohol-related collisions, particularly if ride subsidy programs are available [9], [10]. However additional research is needed, as there is not a consensus in the literature [11].
Driver health is a significant concern when it comes to TNCs. Health risks include stress, fatigue, musculoskeletal disorders, and urinary disorders [12], and are compounded by job insecurity and the absence of traditional employment benefits [12], [13]. The absence of designated rest areas akin to taxi-stands further exacerbates these challenges [12].

Further research is needed regarding the TNCs’ potential for filling gaps in non-emergency medical transportation, as well as mechanisms to protect driver health and wellbeing.

Researchers have theorized about potential effects of Mobility-as-a-service (MaaS) programs and public health. A study in Transportation Research highlighted health concerns related to possible reductions in active transport like walking and biking, since MaaS products are based on monetizable modes of transport and emphasize door-to-door service [1]. However, another study in Research in Transportation Business & Management argues that MaaS has the potential to incentivize use of active transport [2].

There is a lack of research studying how MaaS models have impacted public health in practice.

Safety is a paramount concern - and barrier to more use - for people who want to travel by bike or scooter, motorized or not. Street connectivity and dedicated bike routes offer some of the strongest safety protections for micromobility users [1]. In places without protected infrastructure for active transportation, where cars compete for the road with all other vehicle types, the most vulnerable travelers are the people outside of automobiles. To avoid the dangers of the road, scooter users and cyclists sometimes resort to traveling on sidewalks, which in turn can create conflicts with pedestrians.Younger riders (under 18 years old) are most likely to injure themselves riding scooters [2], while pedestrians who are older adults and children are particularly at risk of sustaining injuries in sidewalk collisions [3]. Experience with micromobility, too, can impact rider behavior and safety. Regular cyclists, for example, are more likely to take longer detours to avoid dangerous routes than infrequent cyclists [4].

Payment structures may also affect how safely people use a shared mobility service. When users pay per minute, rather than by distance, they may choose to speed and compromise road safety [5]. A global study of bikeshare programs found that, in cities with bikeshare programs, bikeshare users were less likely than private cyclists to sustain fatal or severe injuries [6]. However, bikeshare users were less likely than private cyclists to wear helmets [7].

Infrastructure policies to improve road safety for micromobility users may involve establishing separate travel networks for automobiles and micromobility, or, when users share the roads, designing streets that slow motorized traffic and thus reduce the severity of crashes [8].

Ride-hail may improve general road safety by providing an alternative to drivers who would otherwise drive inebriated. A study from Great Britain found that the introduction of Uber was associated with a nine percent decrease in severe traffic-related injuries, which the authors hypothesized resulted from fewer drunk-driving trips [1]. Dills and Mulholland (2018) similarly found a decrease in drunk driving incidents, fatal car crashes, and arrests for assault and disorderly conduct with the introduction of Uber [2].

Ride-hail services can also provide an alternative travel mode for users who feel safer taking ride-hail trips than public transit [3]. However, safety concerns can also discourage people from using ride-hail services, particularly women [4].

Relative to taxis, a study in Chicago found that ride-hail trips may be more likely to result in minor injury crashes, though equally likely to result in severe crashes [5]. The authors attributed these crash differences to three potential factors: 1) drivers may be more distracted by the ride-hail app that may abruptly change routes for new passengers, 2) taxi drivers may have more experience than semi-professional ride-hail drivers, and 3) taxi driver regulations may encourage safer driving, such as limited overtime [5]. Job insecurity may explain riskier behavior on the part of ride-hail drivers; Lefcoe et al (2023) found that ride-hail drivers juggling multiple jobs engage in riskier driving behavior than full-time ride-hail and taxi drivers [6].

Passenger safety is one factor that may encourage people to use demand-responsive transit or microtransit, particularly people who are hesitant to take fixed-route public transit. In cases where walking to a fixed route transit stop can be dangerous, like in areas with limited sidewalks and high-speed arterial routes, a door-to-door service can offer a safer journey [1]. In cases where microtransit algorithms select stops for passengers to most efficiently route them, the algorithms may not incorporate local traffic and pedestrian infrastructure, leaving riders exposed to dangerous intersections [2]. Drivers may use their discretion, however, to bring passengers to a safer stopping point and ignore the algorithm’s recommendation. Even microtransit services that are not door-to-door can offer safety improvements over fixed-route services when walk routes are hazardous; microtransit programs designed around a smaller customer base can flexibly tailor their stations to ensure they are both in safe areas to wait and located close to where riders need them, and nimbly alter them based on customer feedback. Ensuring driver competency is key for safe microtransit systems; in cases where pilot programs use private contractors, rather than operators that must follow Federal Transit Administration standards, driver screening may be less stringent [2].

Mobility-as-a-service (MaaS) applications may have mixed impacts on measures of social equity. Research on the impact of digital apps to facilitate ride-hail shows they lowered transportation inequities for seniors in Japan [1], but maintained existing regional rural-urban disparities in Finland [2]. Unbanked users and those without smartphones may also be left out of use, as well as non-native English speakers, which may exacerbate barriers to mobility faced by those groups [3]. Market dominance by private MaaS companies may also lead to monopolization and price discrimination, which may impact those most reliant on public transportation [3]. Public transportation is crucial for low-income groups, who, paradoxically, find it harder to access than people in wealthier neighborhoods. While MaaS presents an opportunity to enhance accessibility and equity, it's essential for policy makers to address and eliminate barriers that maintain the status quo of exclusion for these communities [4].

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.