Assessing the collective safety of automated vehicle groups: A duration modeling approach of accumulated distances between crashes.

Ideally, the evaluation of automated vehicles would involve the careful tracking of individual vehicles and recording of observed crash events. Unfortunately, due to the low frequency of crash events, such data would require many years to acquire, and potentially place the motorized public at risk if defective automated technologies were present. To acquire information on the safety effectiveness of automated vehicles more quickly, this paper uses the collective crash histories of a group of automated vehicles, and applies a duration modeling approach to the accumulated distances between crashes. To demonstrate the applicability of this approach as a method compare automated and conventional vehicles (human drivers), an empirical assessment was undertaken using two comparable sources of data. For conventional vehicles, police and non-police-reportable crashes were collected from the Second Strategic Highway Research Program's naturalistic driving study, and for automated vehicles, data from the California Department of Motor Vehicles Autonomous Vehicle Tester program were used (105 crashes from 59 permit holders driving ∼2.8 million miles were used for the analysis). The results of the empirical study showed that automated driving was safer at the 95% confidence level, with a higher number of miles between crashes, relative to their conventional vehicle counterparts. The findings indicate that the number of miles between crashes would be increased by roughly 27% when switching from conventional vehicles to automated vehicles. Despite limited data which mandated a group-vehicle approach, this study can be considered a reasonable initial approximation of automated vehicle safety.

Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm.

Pedestrians and bicyclists from marginalized and underserved populations experienced disproportionate fatalities and injury rates due to traffic crashes in the US. This disparity among road users of different races and the increasing trend of traffic risk for underserved racial groups called for an urgent agenda for transportation policy making and research to ensure equity in roadway safety. Pedestrian and bicyclist crashes involved drivers and pedestrians/bicyclists; the latter were usually victims. Traditional safety studies did not account for the interaction between the two parties and assumed that they were independent from each other. In this study we paired the driver and pedestrian/bicyclist involved in the same crash to understand the socioeconomic and demographic make-up of the two parties involved in crashes and assessed the geographic distribution of these crashes and crash-contributing factors. For this purpose, we applied thelatent class clustering analysis (LCA) to classify different crash types and analyze the patterns of the crashes based on the income and ethnicity of both drivers and victims involved in pedestrian and bicyclist crashes. We then used random forest algorithms and partial dependence plots (PDPs) to model and interpreted the contributing factors of the clusters in both pedestrian and bicyclist models. The clustering results showed a pattern of social segregation in pedestrian and bicyclist crashes that drivers and victims with similar socioeconomic characteristics tend to be involved in one crash. Pedestrian/bicyclist exposure, driver's age, victim's age, year of the car in use, annual average daily traffic (AADT), speed limit, roadbed width, and lane width were the most influential factors contributing to this pattern. Crashes that involved drivers and victims with lower income and non-white ethnicity tended to happen in the location with higher pedestrian/bicyclist exposure, higher speed limit, and wider road. The findings of this research can help to inform the decision-making process for improving safety to ensure equitable and sustainable safety for all road users and communities.

Safe route-finding: A review of literature and future directions.

While road navigation systems seek to determine the shortest routes between a given set of origin and destination points, there are certain situations in which the fastest route increases the risk of being involved in road crashes. This implies the necessity of integrating safe route-finding into road navigation systems. This study is designed to synthesize the literature on safe route-finding and identify the gaps in the literature for future research. Specifically, a scoping literature review methodology is applied to understand how safety is incorporated in route-finding, even beyond motor vehicle navigation systems. Three databases (Scopus, Web of Science, and IEEE Xplore) are explored, and controlling for inclusion criteria, 40 studies are included in this review. The findings of this review indicated five areas through which safety was considered in route-finding: motor vehicle navigation, public safety, public health, pedestrian and cyclist navigation, and hazardous material transportation. The measurement of safety was found challenging with inconsistencies in safety quantification approaches. The safe route-finding algorithms were investigated based on their predictive/reactive, static/dynamic, and centralized/decentralized characteristics. Based on the critical review of the safe route-finding algorithms, availability of real-time data sources, accurate real-time and disaggregated crash risk prediction models, trade-off between time and safety in road navigation tools, and centralized safe route-finding are highlighted as the requirements and challenges in considering safety in road navigation systems. This study outlines a research agenda to address the identified challenges in safe route-finding.

COVID-19 impacts on mobility, environment, and health of active transportation users.

Active transportation could be an effective way to promote healthy physical activity, especially during pandemics like COVID-19. A comprehensive evaluation of health outcomes derived from COVID-19 induced active transportation can assist multiple stakeholders in revisiting strategies and priorities for supporting active transportation during and beyond the pandemic. We performed a two-step reviewing process by combining a scoping review with a narrative review to summarize published literature addressing the influence of COVID-19 on mobility and the environment that can lead to various health pathways and health outcomes associated with active transportation. We summarized the COVID-19 induced changes in active transportation demand, built environment, air quality, and physical activity. The results demonstrated that, since the pandemic began, bike-sharing users dropped significantly while recreational bike trips and walking activities increased in some areas. Meanwhile, there have been favorable changes to the air quality and the built environment for active transportation users. We then discussed how these changes impact health outcomes during the pandemic and their implications for urban planning and policymaking. This review also suggests that walking and biking can make up for the reduced physical activities during the pandemic, helping people stay active and healthy.

Quantifying the automated vehicle safety performance: A scoping review of the literature, evaluation of methods, and directions for future research.

Vehicle automation safety must be evaluated not only for market success but also for more informed decision-making about Automated Vehicles' (AVs) deployment and supporting policies and regulations to govern AVs' unintended consequences. This study is designed to identify the AV safety quantification studies, evaluate the quantification approaches used in the literature, and uncover the gaps and challenges in AV safety evaluation. We employed a scoping review methodology to identify the approaches used in the literature to quantify AV safety. After screening and reviewing the literature, six approaches were identified: target crash population, traffic simulation, driving simulator, road test data analysis, system failure risk assessment, and safety effectiveness estimation. We ran two evaluations on the identified approaches. First, we investigated each approach in terms of its input (required data, assumptions, etc.), output (safety evaluation metrics), and application (to estimate AVs' safety implications at the vehicle, transportation system, and society levels). Second, we qualitatively compared them in terms of three criteria: availability of input data, suitability for evaluating different automation levels, and reliability of estimations. This review identifies four challenges in AV safety evaluation: (a) shortcomings in AV safety evaluation approaches, (b) uncertainties in AV implementations and their impacts on AV safety, (c) potential riskier behavior of AV passengers as well as other road users, and (d) emerging safety issues related to AV implementations. This review is expected to help researchers and rulemakers to choose the most appropriate quantification method based on their goals and study limitations. Future research is required to address the identified challenges in AV safety evaluation.

Burden of Disease Assessment of Ambient Air Pollution and Premature Mortality in Urban Areas: The Role of Socioeconomic Status and Transportation.

With recent rapid urbanization, sustainable development is required to prevent health risks associated with adverse environmental exposures from the unsustainable development of cities. Ambient air pollution is the greatest environmental risk factor for human health and is responsible for considerable levels of mortality worldwide. Burden of disease assessment (BoD) of air pollution in and across cities, and how these estimates vary according to socioeconomic status and exposure to road traffic, can help city planners and health practitioners to mitigate adverse exposures and promote public health. In this study, we quantified the health impacts of air pollution exposure (PM2.5 and NO2) at the census tract level in Houston, Texas, employing a standard BoD assessment framework to estimate the premature deaths (adults 30 to 78 years old) attributable to PM2.5 and NO2. We found that 631 (95% CI: 366-809) premature deaths were attributable to PM2.5 in Houston, and 159 (95% CI: 0-609) were attributable to NO2, in 2010. Complying with the World Health Organization air quality guidelines (annual mean: 10 µg/m3 for PM2.5) and the US National Ambient Air Quality standard (annual mean: 12 µg/m3 for PM2.5) could save 82 (95% CI: 42-95) and 8 (95% CI: 6-10) lives in Houston, respectively. PM2.5 was responsible for 7.3% of all-cause premature deaths in Houston, in 2010, which is higher than the death rate associated with diabetes mellites, Alzheimer's disease, or motor vehicle crashes in the US. Households with lower income had a higher risk of adverse exposure and attributable premature deaths. We also showed a positive relationship between health impacts attributable to air pollution and road traffic passing through census tracts, which was more prominent for NO2.

Burden of disease from transportation noise and motor vehicle crashes: Analysis of data from Houston, Texas.

BACKGROUND: Transportation systems have an essential role in satisfying individuals' needs for mobility and accessibility. Yet, they have been linked to several adverse health impacts, with a large, but modifiable, burden of disease. Among the several transportation-related health risk factors, this study focused on transportation-related noise as an emerging exposure whose burden of disease remains partially recognized. We compared premature deaths potentially attributable to transportation-related noise with deaths from motor vehicle crashes, a well-researched and widely recognized transportation risk factor. METHOD: We employed a standard burden of disease assessment framework to quantify premature cardiovascular diseases mortality attributable to transportation-related (road and aviation) noise at the census tract level (n = 592) in Houston, Texas. The results were compared to motor vehicle crash fatalities, which are routinely observed and collected in the study area. We also investigated the distribution of premature deaths across the city and explored the relationship between household median income and premature deaths attributable to transportation-related noise. RESULTS: We estimated 302 (95% CI: 185-427) premature deaths (adults 30-75 years old) attributable to transportation-related noise in Houston, compared to 330 fatalities from motor vehicle crashes (adults younger than 75 years old). Transportation-related noise and motor vehicle crashes were responsible for 1.7% and 1.9% of all-cause premature deaths in Houston, respectively. Households with lower median income had a higher risk of adverse exposure and premature deaths potentially attributable to transportation-related noise. A larger number of premature deaths was associated with living in the central business district and the vicinity of highways and airports. CONCLUSION: This study highlighted the significant contribution of transportation-related noise and motor vehicle crashes to premature deaths in the city of Houston. The analogy between the estimated premature deaths attributable to transportation-related noise and motor vehicle crashes showed that the health impacts of transportation-related noise were as significant as motor vehicle crashes. The estimated premature death rate attributable to transportation-related noise was also comparable to the death rate caused by suicide, influenza, or pneumonia in the US. There is an urgent need for imposing policies to reduce transportation noise emissions and human exposures and to equip health impact assessment tools with a noise burden of disease analysis function.