Literature Reviews

Carshare may, relative to private auto travel, confer some safety benefits. For example,users generally have to go through a screening process to sign up for the programs and establish valid licenses. Safe driving behavior does, of course, vary by individual; a study of Australian carshare users found that infrequent users, users in households that owned other cars, and users that had fewer previous accidents, chose more expensive vehicle insurance, and had been licensed for longer, were less likely to be in a vehicle crash [1]. To enhance safety, the study recommended establishing incentives for carshare users with more driving experience and more extensive insurance [1].

More research may be necessary to better establish safety differences among carshare users, whether carshare users travel more safely relative to private vehicle owners, and if so, what the mechanisms are that promote additional precautions while driving.

Inequality is embedded in our transportation systems and land use patterns, which reinforces unequal access to opportunities. Mobility inequality can be racialized, gendered, or based on income. The inequalities between those with and without private vehicles deepened during the COVID-19 pandemic [1], [2], [3]. Universal Basic Mobility (UBM) programs aim to address this and in turn create more equitable transportation systems. Based on qualitative evaluation of eight UBM programs and pilots, UC Davis researchers found that UBM pilot programs have had success in enrolling low-income people of color and increasing transit use [4].

Additional research related to equity impacts of mobility wallet pilot program outcomes is ongoing. For example, researchers at UCLA and UC Davis are evaluating the South LA mobility wallet pilot, where 1,000 people in South Los Angeles are receiving $150 per month for a year for use on transit needs [5]. Researchers at UC Davis are also evaluating pilot UBM programs in Oakland and Bakersfield, with a focus on economic, social, and environmental impacts [6]. However, there is little completed research on how effective university mobility programs are in addressing inequality in transportation access. Additional research is needed on the equity impacts of UBM programs, as well as how the programs compare to alternatives like free or reduced fare transit programs.

The introduction and potential proliferation of highly automated vehicles (AVs) present the classic challenge of balancing the freedom of private manufacturers to innovate with the government's responsibility to protect public health. AVs raise many public health issues beyond their potential to improve safety, ranging from concerns about more automobile use and less use of healthier alternatives like biking or walking, to concerns that focusing on autonomous vehicles may distract attention and divert funding from efforts to improve mass transit. There are, additionally, issues of access, especially for the poor, disabled, and those in rural environments [1].

As the classic Code of Ethics for Public Health recommends [2], public health advocates can advocate for the rights of individuals and their communities while protecting public health by helping to establish policies and priorities through “processes that ensure an opportunity for input from community members.” Public health thought leaders can ensure that communities have the information they need for informed decisions about whether and how autonomous vehicles will traverse their streets, and they can make sure that manufacturers who test and deploy autonomous vehicles obtain “the community’s consent for their implementation.” Finally, public health leaders can work for the empowerment of the disenfranchised, incorporating and respecting “diverse values, beliefs, and cultures in the community” and collaborating “in ways that build the public’s trust” [2].

Emerging micromobility options such as e-bikes and e-scooters can improve accessibility and connectivity for vulnerable population groups, such as those with physical limitations or without access to a car [1], [2]. Compared to biking or walking, electric micromobility (EMM) vehicles are often more accessible to users with lower interest in or capacity for physical activity, while still providing exercise and outdoor enjoyment [1], [2], [3]. For instance, e-bikes are favored by older adults as a form of physical activity and can encourage micromobility use for distances over 3 miles typically covered by cars [4], [5], [6]. An observational study found that starting to e-bike may increase overall biking frequency among older adults, potentially extending the number of years they are able to bike [4], [5], [6]. Despite being less physically demanding than conventional biking, e-biking offers many of the same cardiovascular benefits [5], [7].
In addition to health benefits from access, physical activity, and outdoor enjoyment, increased EMM vehicle usage has the potential to reduce air pollution from cars by substituting car trips and improving access to public transit. EMM vehicles can address the first-mile-last-mile problem, supporting the use of public transit [8], [9]. They also provide an alternative mode of transportation for short trips, which can help alleviate overcrowding on public transport and support social distancing when necessary [8]. Moreover, EMM vehicles may contribute to noise pollution reduction, which is linked to adverse health effects such as cognitive impairment in children and sleep disturbance [9]. However, studies indicate that not all EMM vehicles have the same environmental health benefits; e-scooters, for instance, may have a negative environmental impact compared to the modes they replace (for example, they may replace pedestrian trips) [9], [10], [11]. Additionally, the collection vehicles used for relocating and charging EMM vehicles in shared vehicle programs can contribute to emissions, particularly in less densely populated areas [9].
Safety remains a primary concern for public health regarding EMM usage, and is discussed in more detail in the section devoted to safety impacts. Cyclists, including e-bike users, are vulnerable to injuries and fatalities from collisions with cars. Electric scooter usage can also result in serious injuries, especially head and limb injuries, exacerbated by low helmet usage [9], [12]. Injuries to pedestrians from e-scooter riders on sidewalks are another significant concern [9]. Providing separate, designated infrastructure for EMM can enhance safety [1].
Future research could include the development of best practices for maximizing public health benefits of micromobility programs, as well as further analysis of the health impacts of different micromobility modes.

Demand-responsive transit and microtransit can benefit public health by improving accessibility. Microtransit services are often more direct or even door-to-door and can serve users with limited mobility. They typically target users whose transportation needs are not met by traditional public transit, including shift workers, low-income individuals, the elderly, disabled, and communities with low levels of fixed-route public transit service [1], [2]. A study on demand-responsive microtransit programs’ return on social investment found that social benefits can outweigh costs by 4 to 6 times, due to their ability to increase access to essential services, foster social inclusion, and improve sustainability [1].
While there are some case studies on microtransit programs, there is limited research on public health impacts. Additional research is needed to understand the extent to which microtransit can meet transportation needs that are not filled by public transit, and how it can best serve different populations and uses, and how it impacts public health. Some of this research is in progress. For example, the "Safety and Public Health Impacts of Microtransit Services" research initiative at the University of Massachusetts Amherst is currently evaluating safety and public health impacts of microtransit services [3].
Finally, on-demand transit/microtransit programs are often meant to improve equitable access, but there is little research on how to design programs to best meet that goal. Survey data from four US cities found that men, younger riders, the highly educated, and transit riders were more likely to be interested in using microtransit. Additional research is needed to understand who on-demand transit/microtransit most frequently serves, and how that impacts public health across demographic groups.

A scoping review of public health impacts from on demand food and alcohol delivery published in SSM Population Health found that on-demand delivery services increase geographical access to food but tend to market unhealthy and discretionary foods, and are likely increasing existing health issues and inequities [1]. The review also highlighted concerns over poor age verification processes potentially allowing minors to access alcohol more easily [1].

Budgetary impacts from micromobility include costs of permits, operating licenses and fines for risky behavior. The rise of shared dockless micromobility led to reactive policy making and regulations that largely constrained operations [1]. The use of such regulation has been motivated by the desire to control the presence of shared micromobility devices in cities, rather than viewing them as a promising line of municipal revenue. In fact, in many cases, municipalities are addressing the need to subsidize riders, especially when it comes to low-income users [2]. A 2024 study by the Transportation Research and Education Center assessed taxes and fees on micromobility, and found that they vary dramatically by city and are typically higher than taxes and fees on ride-hailing and private vehicles [3].

In general, the literature suggests that while micromobility has the potential to enhance quality of life and access to mobility [4], there are also externalities of social harm such as (mis)parking [5]. There is little available research related to how micromobility could influence the tax burden or base of a locality.

Increased access to education and job opportunities are cited as benefits of Universal Basic Mobility (UBM), based on robust existing research demonstrating the relationship between mobility and access to opportunity and early research on UBM pilot programs [1], [2]. Research assessing how effectively UBM policies and programs improve access to education and job opportunities is sparse.

The University of California Institute of Transportation Studies recently released a technical report that summarizes Universal Basic Mobility (UBM) pilot programs in California along various design dimensions, including eligibility requirements, monetary assistance value, and allowable travel modes [1]. For example, Los Angeles, CA offered 2,000 residents $150 per month for use of public transit, private taxi, transportation network company (e.g., Uber), electric bikeshare, and carshare. The Pittsburgh, PA program gave 50 residents unlimited access to transit and bikeshare along with a monthly credit for scooter and carshare [2]. Other U.S. cities that have implemented a UBM pilot include Portland, OR; Sacramento, CA; Oakland, CA; and Stockton, CA.

Evaluations of most UBM programs are still underway, though some results are available for Oakland and Portland. The Oakland Department of Transportation and Alameda County Transportation Commission surveyed 66 participants pre-program and mid-program, and they observed that 66 percent of these participants used the extra mobility funds for commuting. They also found that 90 percent of funds were spent on transit, and the number of participants who self-reported driving as their primary mode declined by 6 percent for commuting trips [3]. Researchers at Portland State University also evaluated the Portland program based on surveys. Their results revealed that participants had positive UBM perceptions: 89 percent of participants reported greater travel flexibility and 66 percent of participants reported the ability to reach work-related activities that would have been otherwise unreachable. Regarding travel mode shift, over 50 percent of participants agreed that they increased their usage frequency of Uber/Lyft, taxi, bikeshare, and e-scooter [4].

In addition to survey results, policymakers would benefit from studies that analyze how UBM affects system-level efficiency, accessibility and equity. However, there is limited completed research to this end. Most studies focus on analysis based on surveys that are only reflective of stated preferences from participants. Those stated preferences may not be generalizable or accurate in practice, and they are limited to a small spatio-temporal scope. Research gaps lie in tracking and understanding the actual (revealed) preferences of UBM participants, in regards to how UBM, by various levels of support, enables those participants to select mobility options to improve efficiency, accessibility and equity. In particular, research is needed to understand how those improvements vary by neighborhood and population groups. This would help public agencies and private service providers to jointly design a UBM program that is tailored for population groups with a vital business model to scale/group in the future.

Many researchers have used agent-based simulation to assess the effects of Automated Vehicles (AV)s on transportation system operations and efficiency (e.g., congestion and Vehicle Miles Traveled (VMT)) [1], [2], [3], [4], [5], [6], [7]. For example, Yan et al. (2020) simulated and then evaluated the performance of a shared autonomous vehicle fleet serving requests across the Minneapolis-Saint Paul region [1]. Yan et al. [1], [2], [3], [4], [5], [6], [7] estimated that the average shared AV could serve at most 30 person-trips per day with less than a 5 minute wait time but generates 13 percent more VMT. Yan et al. [1], [2], [3], [4], [5], [6], [7] also concluded that dynamic ridesharing could reduce shared AV VMT by 17 percent on average and restricting shared AV parking on the busiest streets could generate up to 8 percent more VMT.
Other methods such as static traffic assignment models and scenario analysis, have also been used to to understand the effect of AVs on congestion and VMT [8], [9], [10], [11], [12], [13]. For example, Harper et al. (2016) estimated the upper bound increase in travel with AVs for the non-driving, elderly, and people with travel-restrictive medical conditions by creating demand wedges and assuming that these traditionally underserved populations would travel as much as younger and/or healthier populations [9]. Harper et al. (2016) estimated that vehicle automation addressing latent demand for underserved population could increase VMT by as much as 14 percent, with females and non-drivers making up most of this increase [9].

Most studies are in agreement that AVs are likely to increase VMT and congestion, due to increased trip making, the ability for AVs to search for more distant and cheaper parking, and the additional VMT generated from people switching from personally owned vehicles to shared autonomous vehicles, generating empty travel [5], [9], [14]. Current opportunities for future research in this area include: 1) simulating AVs considering a heterogeneous population of travelers with different values of travel time (VOTT) and 2) incorporating parking to estimate the impact of AVs on transportation system operations [15].

The environmental impact of Mobility-as-a-Service (MaaS) and related business models depends on how the services are offered, and the incentives of the operator [1]. For example, if ride hailing is incentivized over public transit and bike-shares, there would be fewer environmental benefits [2]. Additionally, private operated mobility services are generally focused on maximizing revenue, while public transport operators may focus more on public benefits including reduced environmental impact [3]. A study assessing welfare impacts of MaaS found that MaaS schemes with shared mobility have the potential to substantially reduce energy consumption, and even greater reductions were possible with improved cost transparency for use of cars and inclusion of externalities such as greenhouse gas emissions in the generalized cost [4].