An integrated text mining, literature review, and meta-analysis approach to investigate pedestrian violation behaviours

Investigating the contributing factors to autonomous Vehicle-Road user Conflicts: A Data-Driven approach

With the imminent widespread integration of Autonomous Vehicles (AVs) into our traffic ecosystem, understanding the factors that impact their safety is a vital research area. To that end, this study assessed the impact of a wide range of factors on the frequency of AV-road user conflicts. The study utilized the Woven prediction and validation dataset, which contains over 1000 h of data collected from the onboard sensors of 20 AVs in California. Two Copula-based models were developed to investigate the contributing factors to total and severe AV conflicts in road segments (model M1) and intersections (model M2). For road segments, results indicated that road characteristics (direction, number of lanes, road length, speed limit, the presence of a dividing median) and road infrastructure (presence of bus stops, presence of cycle lanes, and presence of on-street parking) have a significant impact on the hourly conflict rates. Regarding the rate of severe conflicts, road user volume, road characteristics (direction, road type, access point density, the presence of a dividing median), and the presence of cycle lanes were identified as the most influential factors. For intersections, the road user volume and the presence of a physical median were found to be positively associated with the hourly conflict rates, while road user volume, intersection characteristics (posted speed limit, lack of traffic control signals, presence of pedestrian crossing, presence of cycle lane, presence of a dividing median, and truck percentage), and the dominant land use at the intersection area were the most impactful variables on the frequency of severe conflicts.

Trends in Road Traffic Injuries Mortality in India: An Analysis from the Global Burden of Disease Study 1990-2021

Road traffic injury (RTI) is one of the most common causes of accidental deaths in India. The study investigates the changes in trends in age-standardised RTI mortality rates in India by sex and age groups, using data from the Global Burden of Disease (GBD) Study 2021. The trend segments are estimated from 1990 to 2021 employing a joinpoint regression model. Additionally, the influence of age, time period, and birth cohort on mortality rate trends was assessed using the age-period-cohort model. Over the past 32 years, the RTI mortality rates have experienced multiple trend segments. RTI mortality rate in the ≤ 14-year-old population has declined remarkably, dropping from 5.71 (4.65 to 6.88) per lakh population in 1990 to 3.66 (3.01 to 4.35) per lakh population in 2010, and further declining to 1.98 (1.65 to 2.37) per lakh population in 2021. The study found a positive correlation between RTI mortality rates and age, with rates consistently lower for women compared to men across all age groups. The variation in RTI mortality rates across Indian states has widened over time, with the coefficient of variation increasing from 30.58% in 1990 to 32.36% in 2010, and further to 35.11% in 2021. Despite efforts, Indian states are unlikely to achieve the goal of halving RTI deaths by 2030, based on 2010 levels. To address this, road conditions and road safety policies aimed at preventing the incidence of RTIs should be further intensified.

The association between health literacy and pedestrian safety behavior among adults: a cross-sectional study

INTRODUCTION

Pedestrians are considered the most vulnerable and complex road users as human behavior constitutes one of the fundamental reasons for traffic-related incidents involving pedestrians. However, the role of health literacy as a predictor of Pedestrian safety behavior remains underexplored. Therefore, the current study was designed to examine the level of health literacy and its association with the safety behavior of adult pedestrians in the city of Tabriz.

METHODS

This cross-sectional analytical study was conducted among individuals aged 18 to 65 years in the metropolitan area of Tabriz from January to April 2023. Data were collected using the HELIA standard questionnaire (Health Literacy Instrument for adults), comprising 33 items across 5 domains (access, reading, understanding, appraisal, decision-making and behavior), as well as the Pedestrian Behavior Questionnaire (PBQ) consisting of 29 items. Data were analyzed using descriptive and analytical statistics (independent t-tests, ANOVA, and Pearson correlation coefficient) via SPSS-22 software.

RESULTS

Based on the results, 94% (376 individuals) had excellent health literacy levels, and their safety behavior scores were at a good level. Health literacy and safety behavior were higher among the age group of 31 to 45 years, women, married individuals, those who read books, and individuals with higher education. However, safety behavior showed no significant association with education level (P > 0.05). There was a significant and positive relationship between health literacy and all its domains and pedestrian safety behavior (r = 0.369, P < 0.001).

CONCLUSION

This study underscores the significant impact of health literacy on pedestrians' safety behavior. The findings reveal that higher levels of health literacy are associated with better safety behavior among individuals aged 18 to 63. Demographic factors such as age, gender, marital status, and education level also play a role in shaping both health literacy and safety behavior. By recognizing these relationships, interventions can be tailored to improve health literacy levels and promote safer pedestrian practices, ultimately contributing to a healthier and safer community in Tabriz city.

Investigating the behavior of pedestrians and related factors in northern Iran in 2021

Among all road users, pedestrian behavior is most unpredictable, and our knowledge of pedestrian behavior and compliance in northern Iran is limited. The aim of the study was to determine the self-reporting behavior of pedestrians and related factors in northern Iran in 2021. This cross-sectional study's research tool included demographic characteristics, social characteristics, and a pedestrian behavior questionnaire (PBS - 43 questions). Data collection was randomly performed in 30 different passages around Rasht (a city in the north of Iran). We used the Poisson regression model and statistical software STATA version 15 for data analysis. With increasing age, pedestrians showed better crossing behavior (p < 0.001, β = 0.202), and the behavior of female pedestrians was better than that of male pedestrians (p < 0.001, β -4.79). As pedestrians, those with private jobs had worse crossing behavior than others (p < 0.045, β = 9.380), and those pedestrians who mentioned they were motorcyclists before had worse crossing behavior than others (p < 0.045, β = 9.380). The findings of this study can be used to establish pedestrian safety and preventative planning. In behavior change intervention programs, it is best to target young male pedestrians aiming to walk to their workplace, a private business. In addition, the behavior of pedestrians whose predominant means of transportation is the motorcycle must be corrected. It is necessary to conduct information campaigns and educational programs for pedestrians with common high-risk behaviors, especially errors and violations.

How yielding cameras affect consecutive pedestrian-vehicle conflicts at non-signalized crosswalks? A mixed bivariate generalized ordered approach

Yielding cameras are considered to be an effective means of preventing drivers' non-yielding behavior. Notably, as pedestrians' perceived risk and behavior change dynamically during the crossing, the safety effectiveness of such facility could also vary across the consecutive conflicts. This study contributes to the literature by assessing the safety effectiveness of yielding camera from a novel perspective, focusing on the consecutive pedestrian-vehicle conflicts (primary conflict and secondary conflict), using Unmanned Aerial Vehicle (UAV) and roadside camera data. Another key contribution lies in the consideration of primary conflict related factors in the secondary conflict analysis, providing new insights into conflict analysis. The mixed bivariate generalized ordered probit model is proposed to analyze the consecutive conflicts simultaneously. The model results indicate that the yielding camera could decrease both slight and severe conflict probability in primary conflict. However, in secondary conflict, the yielding camera would lower severe conflict probability but increase slight conflict probability. Moreover, several primary conflict related factors reveal significant effects on the secondary conflict severity. Specifically, higher pedestrian speed and driver's yielding behavior in primary conflict could lead to higher crossing risks in the secondary conflict. Conversely, more unsuccessful attempts before primary conflict could decrease the severity level of secondary conflict. Based on the results, several practical implications are provided to improve the effectiveness of yielding camera and enhance pedestrian safety.