A Spatial Autoregressive Quantile Regression to Examine Quantile Effects of Regional Factors on Crash Rates

Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution, but rarely both. To overcome the research gap, this study utilizes the spatial autoregressive quantile (SARQ) model to estimate how contributing factors influence the total and fatal-plus-injury crash rates and how modelling relationships change across the distribution of crash rates considering the effects of spatial autocorrelation. Three types of explanatory variables, i.e., demographic, traffic networks and volumes, and land-use patterns, were considered. Using data collected in New York City from 2017 to 2019, the results show that: (1) the SARQ model outperforms the traditional quantile regression model in prediction and fitting performance; (2) the effects of variables vary with the quantiles, mainly classifying three types: increasing, unchanged, and U-shaped; (3) at the high tail of crash rate distribution, the effects commonly have sudden increases/decrease. The findings are expected to provide strategies for reducing the crash rate and improving road traffic safety.

Organisational structural predictors of ambulance crashes: an analysis

Objective: This study investigated the association between three organisational structural factors—organisational type, organisation status (staffing model), and level of service—and ambulance crash rates. The null hypothesis was that there would be no difference in ambulance crash rates during response or transport between any of the three factors. Methods: A cross-sectional design with a sample drawn from the 2019 National Emergency Medical Services Information System dataset was used to examine the relationship between these systems-level factors and ambulance crashes. After applying inclusion criteria, a sample of 2207 cases was drawn and analysed using the χ2 test of association and multiple logistic regression. Results: There was a statistically significant but small association between level of service and ambulance crashes during transport, using the χ2 test of association (P<0.05). Emergency medical technician (EMT)-level services had lower crash rates and paramedic-level services had higher rates than expected. There was no statistically significant association between organisational type or organisational status and ambulance crashes during either the response or transport phase of an emergency medical service (EMS) call, using the χ2 test of association (P>0.05). Of the two logistic regression models performed, only EMT-level services had a statistically significant association with ambulance crashes during transport (P<0.05; OR 0.208 [0.050, 0.866]). Conclusion: The organisational structural factors examined in this study failed to explain most of the variance in ambulance crash rates. However, EMT-level services were associated with lower rates of ambulance crashes than paramedic level services. EMS healthcare administrators and researchers should continue to explore potentially modifiable factors to reduce the incidence of these events and promote positive social change by reducing the risk of injury to patients, EMS workers and the public at large.

Critical Analysis of Prototype Autonomous Vehicle Crash Rates: Six Scientific Studies from 2015–2018

Will Automated Vehicles be Safer than Conventional Vehicles? One of the critically important questions that has emerged about advanced technologies in transportation is how to test the actual effects of these advanced systems on safety, particularly how to evaluate the safety of highly automated driving systems. Richard Young's Critical Analysis of Prototype Autonomous Vehicle Crash Rates does a deep dive into these questions by reviewing and then critically analyzing the first six scientific studies of AV crash rates.

Evaluation of the efficacy of an intersection conflict warning system at two-way stop-controlled rural intersections: difference-in-differences and triple-difference analytical approaches

ObjectiveIntersection 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.MethodsThe 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.ResultsThe 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.ConclusionsThe study provided important insight into potential causal associations between intersection safety countermeasures and crashes at high-risk rural two-way stop-controlled intersections.

A Methodology for Normalizing Safety Statistics of Partially Automated Vehicles

The safety of increasingly automated vehicles is of great concern to regulators, yet crash rates are generally reported by manufacturers with proprietary metrics. Without consistent definitions of crashes and exposure, comparing automated vehicle crash rates with baseline datasets becomes challenging. This study investigates the reported on-road crash rates of one manufacturer’s partially automated driving system. Their reported crash rates are adjusted based on roadway classification and driver demographics to allow for direct comparison with the manufacturer’s own advanced driver assistance systems. Recommendations for uniform crash reporting standards are provided.

Takeover performance evaluation using driving simulation: a systematic review and meta-analysis

Introduction In a context of increasing automation of road transport, many researchers have been dedicated to analyse the risks and safety implications of resuming the manual control of a vehicle after a period of automated driving. This paper performs a systematic review about drivers’ performance during takeover manoeuvres in driving simulator, a tool that is widely used in the evaluation of automated systems to reproduce risky situations that would not be possible to test in real roads. Objectives The main objectives are to provide a framework for the main strategies, experimental conditions and results obtained by takeover research using driving simulation, as well as to find whether different approaches may lead to different outcomes. Methodology First, a literature search following the PRISMA statement guidelines and checklist resulted in 36 relevant papers, which were described in detail according to the type of scenarios and takeover events, drivers’ engagement in secondary tasks and the assessed takeover performance measures. Then, those papers were included in a meta-analysis combining PAM clustering and ANOVA techniques to find patterns among the experimental conditions and to determine if those patterns have influence on the observed takeover performance. Conclusions Less complex experiments without secondary task engagement and conducted in low-fidelity simulators are associated with lower takeover times and crash rates. The takeover time increases with the time budget of the first alert, which reduces the pressure for a driver’s quick intervention.

The Effects of Speed and Flow Characteristics on Crash Rates for Wasit Multi-lane Highways in Iraq

This study describes traffic crash rates in selected multilane rural highways in Wasit governorate in Iraq. The main objective of this research is to investigate relationships between total, fatal crash rates and their kinds and factors such as hourly traffic flow and average spot speed. The study is based on data collected from two sources: police stations and traffic surveys. Three highways are selected to cover the locations of the accidents. The selection includes Kut – Suwera with five segments, Kut – ShekhSaad with three segments, and Kut – Hay with two segments multilane divided highways. Multiple linear regression analysis is applied to the data by using SPSS software to attain the relationships between the dependent variables and the independent variables in order to identify elements that are strongly correlated with crashes rates and severity. Seven regression models are developed which verify weak and strong statistical relationships between crashes types and average spot speed with hourly traffic flow respectively. As the hourly traffic flow of automobile grows, the need for safe traffic facilities also grown.