Assessing motorcyclist safety at unsignalized intersection using automated trajectory data analysis.

Objective: In a developing country like India, where the share of motorcyclists is increasing exponentially, their road crashes are also rising at an alarming rate. The majority of these road crashes occur at unsignalized intersections. Therefore, the present study aims to analyze the safety of motorcyclists at unsignalized three-arm intersections under a heterogeneous traffic environment using a fully automated trajectory data extraction tool.

Behind the wheel: Probing into personality, skills, and driving behavior's role in bus rapid transit crashes.

Objective: Personality traits and driving skills are significantly associated with driving behaviors and crashes. In the case of professional bus drivers, the relationships amongst these variables have not been sufficiently examined in terms of road crashes. Therefore, this study seeks to examine the relationship between personality traits, driving skills, driving behaviors, and crash involvement among Bus Rapid Transit (BRT) drivers.

Evaluating overtaking and filtering maneuver of motorcyclists and car drivers using advanced trajectory data analysis.

The present paper compares motorized two-wheeler (MTW) and passenger car's interactions with the rest of the traffic in urban roads while performing overtaking and filtering maneuvers. To better understand filtering maneuvers of motorcyclists and car drivers, an attempt was made to propose a new measure, i.e.

Profiling drivers to assess safe and eco-driving behavior - A systematic review of naturalistic driving studies.

Road accidents and vehicular emissions are two significant issues related to road transportation, affecting both human life and the environment. Prior research suggests that driver behavior is a crucial factor in the majority of road crashes and is a significant factor influencing fuel consumption and vehicle emission. Significant improvement in driving behavior can be achieved by providing feedback to drivers about their driving behavior.

Examining risky riding behavior in India using Motorcycle rider behavior questionnaire.

Powered two-wheelers (PTW) constitute the most vulnerable type of road user, primarily due to their lack of protection compared to other motorized vehicles, which can lead to a severe accident in the event of crashes. A notable increase in the percentage of PTW accidents resulting in fatalities has raised a serious need for further research in understanding riding behavior. The Motorcycle rider behavior questionnaire (MRBQ) based studies have shown promising results by using MRBQ to relate riding behavior with crash risk.

Analyzing driver behavior under naturalistic driving conditions: A review.

For a decade, researchers working in the area of road safety have started exploring the use of driving behavior data for a better understanding of the causes related to road accidents. A review of the literature reveals the excellent potential of naturalistic driving studies carried out by collecting vehicle performance data and driver behavior data during normal, impaired, and safety-critical situations. An in-depth understanding of driver behavior helps analyze and implement pre-crash safety measures - the development of enforcement policies, infrastructure design, and intelligent vehicle safety systems.

Evaluating pedestrian vehicle interaction dynamics at un-signalized intersections: A proactive approach for safety analysis.

The present research demonstrates the use of advanced trajectory based data to analyze road user interactions at an un-signalized intersection under heterogeneous traffic complexities. This study demonstrates an improvement over the conventional grid-based analysis to estimate surrogate safety measures (SSM). An advanced pattern-based approach to categorize pedestrian-vehicle interactions based on the road user behavior is proposed in the study.