A framework for real-time traffic risk prediction incorporating cost-sensitive learning and dynamic thresholds.

In recent years, researchers have explored an innovative approach that leverages real vehicle trajectory data to simultaneously derive traffic state and risk level for real-time risk prediction, which is crucial for traffic safety. However, existing studies largely overlook the costs associated with incorrect predictions and the varying consequences of different misclassifications, which undermines the reliability of the obtained prediction results. To address these gaps, this study refined traffic risk classification into four levels (i.

A before-after safety evaluation of wide centerline treatment considering the simultaneous changes in lane width and sealed shoulder width.

Wide Centerline Treatment (WCLT) is typically implemented by simultaneous adjustments to lane widths and sealed shoulder widths to minimize the need for road widening. Studies to date, however, have overlooked these concurrent changes when assessing the safety effectiveness of WCLT, potentially leading to unreliable conclusions. Therefore, this paper aims to assess the safety effectiveness of WCLT while accounting for simultaneous changes in the lane width and sealed shoulder width to provide a more reliable and comprehensive evaluation.

Understanding motorcycle crash involvement: Insights from regular motorcyclists and food delivery riders in Vietnam.

Motorcycles have become a primary mode of transportation in many low- and middle-income countries, including Vietnam, where they are widely used for personal transport and commercial activities. The growing reliance on motorcycles, driven by rapid urbanization and the rise of app-based delivery platforms, has brought economic benefits but also significant public health concerns due to the high incidence of road traffic crashes. This study, based on a questionnaire survey targeting regular motorcyclists and food delivery riders in Vietnam, examines and compares the factors contributing to crash involvement between two groups, regular and delivery riders.

Multi-scale approaches to cope with scale effect issues in macroscopic crash analysis.

Traffic safety has increasingly become an important concern in developing long-term transportation planning strategies. Since transportation planning steps always involve some kinds of geographic entity, predicting crashes for those entities is not only a mere avenue of analytic methods in safety research, but also influential to practical application in road infrastructure design and management. However, the analyses using different spatial units are subjected to the modifiable areal unit problem (MAUP), which refers to the issue of inconsistent statistical results when dealing with geographic data of different aggregation configurations.

Exploration of the tram-involved crashes' characteristics and contributing factors to fatality in tram crashes in Japan.

Modern trams have garnered worldwide attention as an alternative urban rail transit system since they offer advantages such as higher accessibility, convenience, lower construction and maintenance costs compared with subways. Nevertheless, due to the system's characteristics, particularly regarding right-of-way issues, crashes involving trams often lead to severe consequences. This study utilizes traffic crash data from across Japan, including 1,121,299 crashes that occurred from 2019 to 2022, of which 304 were tram involved.

Investigating the factors influencing Repeatedly Crash-Involved Drivers (RCIDs): A Random Parameter Hazard-Based Duration approach.

Repeatedly Crash-Involved Drivers (RCIDs) pose significant challenges to traffic safety, contributing disproportionately to crash occurrences and their severe consequences. While existing research has explored factors influencing crash involvement, the literature often neglects the influence of a driver's crash history and inter-crash intervals on their evolving crash risk. Additionally, many traditional models fail to address unobserved heterogeneity, limiting their ability to capture the complex interplay of factors contributing to repeated crash involvement.

Young Motorcyclists' Behavior Analysis in Pakistan based on Modified Motorcycle Rider Behavior Questionnaire (MRBQ).

In many low- and middle-income countries, including Pakistan, young motorcyclists are overrepresented in crashes, primarily due to risky behaviors. To examine these behaviors, the Motorcycle Rider Behavior Questionnaire (MRBQ) has been modified for young motorcyclists in Pakistan to better capture the unique and culturally relevant behaviors affecting their safety. In addition, the study seeks to identify the factor structure of the MRBQ tailored for young motorcyclists in Pakistan, explore the determinants of self-reported incidents (i.

Effect of emergency medical service response time on fatality risk of freeway crashes: Bayesian random parameters spatial logistic approach.

Introduction: Emergency medical service (EMS) serves as a pivotal role in linking injured road users to hospitals via offering first aid measures and transportation. This paper aims to investigate the effect of emergency medical service (EMS) response time on the fatality risk of freeway crashes.

Methods: Crash injury severity data from Kaiyang Freeway, China in 2014 and 2015 are employed for the empirical investigation.

A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model.

For identifying the optimal model for real-time conflict prediction, there is a necessity for proposing a quantitative analysis approach that adaptively selects the optimal prediction model from a large pool of task-suited models, while simultaneously considering the computational efficiency and prediction precision. Based on this line, this study developed an innovative approach termed surrogate model-based optimal prediction model selection (SM-OPMS). This approach aims to accelerate the optimal model selection while incorporating prediction precision considerations, under the precondition of comprehensively evaluating task-suited models.

Exploring driving behavioral characteristics in pre-, in-, and post-conflict stages based on car-following trajectory data.

This study investigates driving behaviour in different stages of rear-end conflicts using vehicle trajectory data. Three conflict stages (pre-, in-, and post-conflict) are defined based on time-to-collision (TTC) indicator. Four indexes are selected to capture within-group and between-group characteristics of the stages.

Pre-crash scenarios for safety testing of autonomous vehicles: A clustering method for in-depth crash data.

Autonomous vehicles (AVs) provide an opportunity to enhance traffic safety. However, AVs market penetration is still restricted due to their safety concerns and dependability. For widespread adoption, it is crucial to thoroughly assess the safety response of AVs in various high-risk scenarios.