Disi Tian

The first exposure most people will have to fully automated vehicles will be Low-speed Automated Vehicles (LSAVs) on select corridors. LSAVs may disrupt traffic due to their slower speeds and experience increased risky overtaking by nearby drivers which could decrease safety for their occupants and surrounding road users. A field observational study compared driver behavior near an LSAV and a human-driven research vehicle across 94 passes of a 1.3-mile urban downtown route (4 straight and 4 right-turn sections). GEE analyses indicated a higher likelihood of other drivers queuing behind the LSAV when stopped, overtaking the LSAV while turning at signalized intersections, and overtaking the LSAV along straight road segments. To test whether the low speed of the LSAV was the key factor for this difference, 85 participants in a second study watched videos from the perspective of a driver following a simulated LSAV and a simulated passenger vehicle traveling at either low speeds (16 km/h) or high speeds (40 km/h). Once the simulated vehicle stopped midblock at a marked crosswalk, the participants responded whether they would, as a driver, attempt to pass the stopped vehicle. GEE analyses indicated participants were more willing to pass the vehicle if it had been portrayed as driving at a slower speed, irrespective of vehicle type. These results indicate a disruptive effect of LSAVs in mixed traffic and a need to investigate alternative communication signal designs for LSAVs, along with policy changes to mitigate these risks such as regulations on LSAV speeds and traffic separation.

Electric scooters (or e-scooters) are among the most popular micromobility options that have experienced an enormous expansion in urban transportation systems across the world in recent years. Along with the increased usage of e-scooters, the increasing number of e-scooter-related injuries has also become an emerging global public health concern. However, little is known regarding the risk factors for e-scooter-related crashes and injury crashes. This study consisted of a two-phase survey questionnaire administered to a cohort of e-scooter riders (n = 210), which obtained exposure information on riders’ demographics, riding behaviors (including infrastructure selection), helmet use, and other crash-related factors. The risk ratios of riders’ self-reported involvement in an e-scooter-related crash (i.e., any crash versus no crash) and injury crash (i.e., injury crash versus non-injury crash) were estimated across exposure subcategories using the Negative Binomial regression approach. Males and frequent users of e-scooters were associated with an increased risk of e-scooter-related crashes of any type. For the e-scooter-related injury crashes, more frequently riding on bike lanes (i.e., greater than 25% of the time), either protected or unprotected, was identified as a protective factor. E-scooter-related injury crashes were more likely to occur among females, who reported riding on sidewalks and non-paved surfaces more frequently. The study may help inform public policy regarding e-scooter legislation and prioritize efforts to establish suitable road infrastructure for improved e-scooter riding safety.

Objective Intersection conflict warning systems (ICWSs) have been implemented at high-risk two-way stop-controlled intersections to prevent right-angle crashes and associated injuries. This study involved investigation of the impacts of ICWSs on crash reductions. Methods The study used a quasi-experimental design to analyse the potential causal relations between Minnesota’s ICWSs and various crash rate outcomes (including total, injury, non-injury, targeted right-angle and non-right-angle crashes) in pre-post analyses. A restricted randomisation method enabled identification of three controls to each ICWS treatment intersection, and included as many comparable intersection characteristics as possible. Annual crash rates (per year per intersection) were analysed over the same periods before and after system activation for treatment and control intersections in each matched group. Pre-crash data for 3 years and post-crash data for up to 5 years were included, ranging from 2010 to 2018. Negative binomial regression models with generalised estimating equations were applied to estimate the average, immediate and continuing treatment effects of ICWSs, through the difference-in-differences and difference-in-difference-in-difference approaches, respectively. Results The ICWS treatment was significantly associated with a decreasing trend for targeted right-angle crash rates posttreatment. Although not statistically significant, most crash rate outcomes appeared to be elevated immediately after treatment (statistically significant for sideswipe crashes only). Pre–post differences in average crash rates (over entire periods), except for incapacitating injury-related crashes, were not statistically significant between treatment and control intersections. Conclusions The study provided important insight into potential causal associations between intersection safety countermeasures and crashes at high-risk rural two-way stop-controlled intersections.