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.

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].

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.

There is no available literature studying the effect of Universal Basic Mobility programs on safety.

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.

On-demand delivery services can lead to an increase in demand for curb space, leading to congestion and double parking which can pose safety risks to pedestrians and other curb users [1], [2]. Existing research primarily considers the impacts of ride-hail/transportation network companies (TNCs) on demand for curb space and associated safety impacts [2]. Common TNC traffic violations that impact safety include not yielding to pedestrians or obstructing public transit lanes and driveways, which can cause other drivers or travelers to move into less safe areas [2]. Study on the safety impacts unique to on-demand delivery service may not be needed.

From limited observations of robotic delivery services in the City of Pittsburgh, there were only 17 incidents involving vehicles or pedestrians reported throughout the program. However, the limited number of devices deployed makes it challenging to ensure their safety at larger scales [3].

Automated Vehicles (AVs) have the potential to prevent 95 percent of pedestrian injury crashes in the US, particularly when a driver violation or pedestrian visibility occurred more than one second before crossing [1]. Vehicles equipped with Advanced Driver Assistance Systems, such as front collision prevention/warning, lane departure prevention, emergency braking, and adaptive cruise control, are already accessible for purchase by consumers. These systems are believed to provide safety benefits due to their ability to reduce human errors in driving and minimize the likelihood of accidents. For example, Scanlon et al. [2] found that lane departure warning and lane departure prevention systems could prevent 28 to 32 percent of road departure crashes in the United States under current road infrastructure conditions. Cicchino [3] observed a 27 percent reduction in front-to-rear crash rates and a 20 percent decrease in front-to-rear injury crash rates with the use of forward collision warning systems. Furthermore, Cicchino [3] noted a 43 percent decrease in front-to-rear accident rates and a 45 percent reduction in front-to-rear injury crash rates with the implementation of low-speed autonomous emergency braking. It is estimated that over 400,000 injuries and nearly a million collisions could have been prevented in 2014 if forward collision warning with autonomous emergency braking had been installed in all vehicles nationwide [3].

Recent safety studies have focused on comparing Autonomous Driving Systems (ADS) safety with that of human drivers. For example, Kusano et al. [4] compared Waymo (an SAE Level 4 ADS) rider-only crash data to human drivers, and found a human crash rate 6.7 times higher compared to the ADS for crashes that caused injuries, and 2.2 percent higher compared to the ADS for policed-reported crashed vehicle rates. In 2024, a working group of industry, academic, and insurance experts developed the Retrospective Automated Vehicle Evaluation (RAVE) checklist, which sets out 15 recommendations to ensure the quality and validity, transparency, and accurate interpretation of retrospective ADS performance comparisons [5].