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.

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.

Assessment of inequity in bicyclist crashes using bivariate Bayesian copulas.

Introduction: Physical activity associated with active transport modes such as bicycling has major health benefits and can help to reduce health concerns related to sedentary lifestyles, such as cardiovascular disease, Type II diabetes, and obesity, as well as risks of colon and breast cancer, high blood pressure, lipid disorders, osteoporosis, depression, and anxiety. However, as a vulnerable user group, bicyclists experience negative health impacts of transportation policies and infrastructure, such as traffic crashes and exposure to air and noise pollution that is disproportionately distributed within low-income and underserved areas.

Method: This study used aggregated (block-group) bicyclist crash data from Harris County, Texas, to analyze how various equity measures are associated with both fatal and injury (FI) and no injury (property damage only) bicyclist crashes that occurred from 2010 to 2017.

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.

Do underserved and socially vulnerable communities observe more crashes? A spatial examination of social vulnerability and crash risks in Texas.

Understanding the relationship between social vulnerability and traffic crashes is a cornerstone for promoting social justice in transportation planning and policymaking. However, few studies have examined the disparities in traffic crashes by systemically considering the influence of social vulnerability via spatial analysis approaches. This study puts forward a new approach to assess the inequity in transportation safety by spatially examining the relationships between crash risks and the social vulnerability index (SVI) established by the Centers for Disease Control and Prevention (CDC).

Health-Based Geographic Information Systems for Mapping and Risk Modeling of Infectious Diseases and COVID-19 to Support Spatial Decision-Making.

Infectious diseases remain an essential global challenge in public health. For instance, novel coronavirus (COVID-19) has resulted in significant negative impacts on public health, infecting more than 214 million people and causing 4.47 million deaths worldwide as of August 2021.

Evaluating the Performance of Low-Cost Air Quality Monitors in Dallas, Texas.

The emergence of low-cost air quality sensors may improve our ability to capture variations in urban air pollution and provide actionable information for public health. Despite the increasing popularity of low-cost sensors, there remain some gaps in the understanding of their performance under real-world conditions, as well as compared to regulatory monitors with high accuracy, but also high cost and maintenance requirements. In this paper, we report on the performance and the linear calibration of readings from 12 commercial low-cost sensors co-located at a regulatory air quality monitoring site in Dallas, Texas, for 18 continuous measurement months.

Mining patterns of autonomous vehicle crashes involving vulnerable road users to understand the associated factors.

Autonomous or automated vehicles (AVs) have the potential to improve traffic safety by eliminating majority of human errors. As the interest in AV deployment increases, there is an increasing need to assess and understand the expected implications of AVs on traffic safety. Until recently, most of the literature has been based on either survey questionnaires, simulation analysis, virtual reality, or simulation to assess the safety benefits of AVs.

Toward a crowdsourcing solution to identify high-risk highway segments through mining driving jerks.

Traffic crashes have become a leading cause of preventable deaths globally. Identifying high-risk segments not only benefits safety specialists to better understand crash patterns but also reminds road users to be aware of driving risks. This study reports on a new crowdsourcing solution to identify high-risk highway segments by analyzing driving jerks.

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.

Investigating the safety and operational benefits of mixed traffic environments with different automated vehicle market penetration rates in the proximity of a driveway on an urban arterial.

Traffic congestion is monotonically increasing, especially in large cities, due to rapid urbanization. Traffic congestion not only deteriorates traffic operation and degrades traffic safety, but also imposes costs to the road users. The concerns associated with traffic congestion increase when considering more complicated situations such as unsignalized intersections and driveways at which maneuvers are entirely dependent upon drivers' judgment.

Analysis of crash injury severity on two trans-European transport network corridors in Spain using discrete-choice models and random forests.

The objective of this paper is to identify the list of crash severity contributing factors and evaluate their impact on multiple-vehicle crashes on two high use Trans-European interurban, freight corridors in Spain (southern Europe): Madrid - Irùn and Barcelona - Almerìa. We have used both logistic regression and random forests to identify crash severity predictors and estimate their impacts on crash outcomes. Although both statistical methods can provide useful information to help explain the safety implications of highway crashes, using both methods may further enable a more comprehensive understanding of this phenomenon.

Methodological development for selection of significant predictors explaining fatal road accidents.

Identification of the most relevant factors for explaining road accident occurrence is an important issue in road safety research, particularly for future decision-making processes in transport policy. However model selection for this particular purpose is still an ongoing research. In this paper we propose a methodological development for model selection which addresses both explanatory variable and adequate model selection issues.