Accident; Analysis and Prevention

Age-related changes and frailty are reasons for the high proportion of older drivers in certain types of crashes, such as giving right of way at intersections and turning left. The identified crash causes include the driver's demographics, driving style, cognitive function, and mental workload. This study aimed to explore the associations of demographics and scale measures with cognitive driving behavior. Thirty-nine drivers, consisting of twenty younger drivers (18-60 years old) and nineteen older drivers (above 60 years old), participated in driving simulation experiments after completing scale tests...

This editorial is both an introduction to the papers that make up this special issue (on the Relationship between Crash Causation, Countermeasures, and Policy) and an attempt at drawing conclusions. To assist the reader, we begin with a brief description of the subject matter of each paper. As expected, the authors tackle different aspects of this general topic and often differ in their conclusions. We follow up by asking: Are in-depth crash causation studies helpful? Can the need for understanding causation be defended? Does the Swiss Cheese Metaphor require revision? What are the building blocks on which the crash injury prevention programs rest? Can one really avoid comparing costs and benefits? These are some of the issues we raise and discuss...

Cycling crashes constitute a significant and rising share of traffic accidents. Consequently, exploring factors affecting cycling safety has become a priority for both governmental bodies and scholars. However, most existing studies have neglected the vision factors capable of quantitatively describing the city-level cycling environment. Moreover, they have relied on limited models that lack interpretability and fail to capture the spatial variations in the contribution of factors. To address these gaps, this research proposed a framework that used origin-destination-based cycling flow and vision factors generated from Google Street View images to identify the leading factors...

Today, cities seek to transition to more sustainable transportation modes. Cycling is critical in this shift, promoting a more beneficial lifestyle for most. However, cyclists are exposed to many hazardous circumstances or environments, resulting in accidents, injuries, and even death. Transport authorities must understand why accidents occur, to reduce the risk of those who cycle. This study applies a new modeling framework to analyze cycling accident severities. We employ a latent class discrete outcome model, where classes are derived from a Gaussian-Bernoulli mixture, applied to data from Berlin, and augmented with volunteered geographic information...

Trajectory data play a vital role in the field of traffic research such as vehicle safety, traffic flow, and intelligent vehicles. The quality of trajectory data will determine the safety effectiveness of both research and practical applications. Effectively filtering out noise and errors from trajectory data is crucial for improving data quality and further research. However, most enhancement methods only focus on the smoothness of trajectory but overlook abrupt changes. The processed trajectory still exist issues such as incomplete elimination of inconsistency and loss of driving characteristics...

This paper investigates the role of driver behavior especially head pose dynamics in safety-critical events (SCEs). Using a large dataset collected in a naturalistic driving study, this paper analyzes the head pose dynamics and driving behavior in moments leading up to crashes or near-crashes. The study uses advanced computer vision and mixed logit modeling techniques to identify patterns and relationships between drivers' head pose dynamics and crash involvement. The results suggest that driver-head pose dynamics, especially poses that indicate distraction and movement volatility, are important factors that can contribute to undesirable safety outcomes...

The long-term effects of the Vision-Zero (VZ) approach in Scandinavia are well documented. In contrast, information regarding the immediate effects of VZ at the starting phase upon gradual implementation is scarce. Taking New York City as the case study, we analyzed both the local and global effects of the Vision-Zero gradual implementation on pedestrian crashes in the early stage of implementation starting from 2014. The data analysis comprised 8,165 pedestrian injury crashes. Using location data, the crashes were matched to VZ infrastructure improvement location, start and completion dates...

No abstract text is available yet for this article.

Methodological advancements in road safety research reveal an increasing inclination toward integrating spatial approaches in hot spot identification, spatial pattern analysis, and developing spatially lagged models. Previous studies on hot spot identification and spatial pattern analysis have overlooked crash severities and the spatial autocorrelation of crashes by severity, missing valuable insights into crash patterns and underlying factors. This study investigates the spatial autocorrelation of crash severity by taking two capital cities, Addis Ababa and Berlin, as a case study and compares patterns in low and high-income countries...

Freight truck-related crashes in urban contexts have caused significant economic losses and casualties, making it increasingly essential to understand the spatial patterns of such crashes. Limitations regarding data availability have greatly undermined the generalizability and applicability of certain prior research findings. This study explores the potential of emerging geospatial data to delve deeply into the determinants of these incidents with a more generalizable research design. By synergizing high-resolution satellite imagery with refined GIS map data and geospatial tabular data, a rich tapestry of the road environment and freight truck operations emerges...

As the detrimental impact of the commonly recommended centered driving mode for autonomous trucks on road longevity is gaining attention, more lateral control modes are being proposed to enhance road sustainability. However, there is currently a lack of research on the lateral safety analysis of autonomous trucks with different lateral control modes, especially in complex driving scenarios (such as overtaking) and adverse weather conditions. Therefore, this study developed a safety assessment framework to comparatively analyze the risk probability differences in lateral accidents during overtaking maneuvers by autonomous trucks with different lateral control modes under adverse weather conditions...

The use of partially-automated or SAE level-2 vehicles is expected to change the role of the human driver from operator to supervisor, which may have an effect on the driver's workload and visual attention. In this study, 30 Ontario drivers operated a vehicle in manual and partially-automated mode. Cognitive workload was measured by means of the Detection Response Task, and visual attention was measured by means of coding glances on and off the forward roadway. No difference in cognitive workload was found between driving modes...

Human drivers are gradually being replaced by highly automated driving systems, and this trend is expected to persist. The response of autonomous vehicles to Ambiguous Driving Scenarios (ADS) is crucial for legal and safety reasons. Our research focuses on establishing a robust framework for developing ADS in autonomous vehicles and classifying them based on AV user perceptions. To achieve this, we conducted extensive literature reviews, in-depth interviews with industry experts, a comprehensive questionnaire survey, and factor analysis...

Transportation researchers have long been using the statistical analysis of traffic crash data to create a proactive awareness of traffic safety, make important decisions about the design of vehicles and highways, and develop and implement safe preventive strategies to improve safety. Despite significant progress toward maintaining and analyzing traffic crash data, researchers still encounter several challenges and methodological barriers when conducting statistical analysis. One of these challenges is dealing with the issue of unobserved heterogeneity in crash data...

Simulation and observational studies have identified the importance of intersection geometries and vehicle speeds in collisions. However, the causal mechanisms of such collisions in low-speed areas and for different collision types remain unclear. This observational study investigates the complex relationships between geometries, speeds, visibilities, and road traffic collisions in the context of low-speed urban areas.Data were collected from 120 three-arm priority intersections in Portsmouth, UK. In 2007, Portsmouth became the first city in the UK to adopt a 20mph speed limit on all residential streets...

Identifying hazardous crash sites (or hotspots) is a crucial step in highway safety management. The Negative Binomial (NB) model is the most common model used in safety analyses and evaluations - including hotspot identification. The NB model, however, is not without limitations. In fact, this model does not perform well when data are highly dispersed, include excess zero observations, or have a long tail. Recently, the Negative Binomial-Lindley (NB-L) model has been proposed as an alternative to the NB. The NB-L model overcomes several limitations related to the NB, such as addressing the issue of excess zero observations in highly dispersed data...

BACKGROUND: Road traffic deaths are increasing globally, and preventable driving behaviours are a significant cause of these deaths. In-vehicle telematics has been seen as technology that can improve driving behaviour. The technology has been adopted by many insurance companies to track the behaviours of their consumers. This systematic review presents a summary of the ways that in-vehicle telematics has been modelled and analysed. METHODOLOGY: Electronic searches were conducted on Scopus and Web of Science...

Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial dependence and spatial heterogeneity in the crash data. This study makes the best of Geographically Weighted Random Forest (GW-RF) model to explore the local associations between crash frequency and various influencing factors in the US, including road network attributes, socio-economic characteristics, and land use factors collected from multiple data sources. Special emphasis is put on modeling the spatial heterogeneity in the effects of a factor on crash frequency in different geographical areas in a data-driven way...

Horizontal curves are locations that, as a result of the changing alignment, may be a contributing factor in roadway departure crashes. One low-cost countermeasure to mitigate crashes at these locations is the installation of the high friction surface treatment (HFST), which increases roadway friction and is intended to help keep drivers on the roadway when traversing a horizontal curve. This treatment has been implemented at numerous curves in Pennsylvania, but the overall safety effectiveness is not known...

Pedestrians represent a group of vulnerable road users who are at a higher risk of sustaining severe injuries than other road users. As such, proactively assessing pedestrian crash risks is of paramount importance. Recently, extreme value theory models have been employed for proactively assessing crash risks from traffic conflicts, whereby the underpinning of these models are two sampling approaches, namely block maxima and peak over threshold. Earlier studies reported poor accuracy and large uncertainty of these models, which has been largely attributed to limited sample size...