Road Safety Perception Questionnaire (RSPQ) in Latin America: A Development and Validation Study

"It's okay because I'm just driving": an exploration of self-reported mobile phone use among Mexican drivers

Introduction

Technological advancements have the potential to enhance people's quality of life, but their misuse can have a detrimental impact on safety. A notable example is the escalating issue of distracted driving resulting from the use of mobile phones behind the wheel, leading to severe crashes and injuries. Despite these concerns, both drivers' usage patterns and their risk-related associations remain scarcely documented in Mexico. Therefore, this descriptive study aimed to examine the mobile phone usage of Mexican drivers, its relationships to risk awareness and near-miss/crash involvement, and the self-reported underlying reasons for this behavior.

Methods

This cross-sectional study utilized a sample of 1,353 licensed Mexican drivers who took part in a nationwide series of interviews regarding their onboard phone use settings.

Results

A significant percentage of drivers (96.8%) recognize using a mobile phone while driving as high-risk behavior. However, only 7.4% reported completely avoiding its use while driving, with 22.4% identified as high-frequency users. Frequency was also found positively associated with the self-reported rate of near-misses and crashes. Furthermore, qualitative data analysis highlights the emergence of a 'sense of urgency' to attend to phone-related tasks in response to daily demands and life dynamics, offering a potential explanation for this behavior.

Conclusion

The results of this study suggest common patterns of onboard mobile use among Mexican drivers concerning driving situations and associated risks. This underscores the need for increased efforts to discourage onboard phone use in the country.

A Crash Data Analysis through a Comparative Application of Regression and Neural Network Models

One way to reduce road crashes is to determine the main influential factors among a long list that are attributable to driver behavior, environmental conditions, vehicle features, road type, and traffic signs. Hence, selecting the best modelling tool for extracting the relations between crash factors and their outcomes is a crucial task. To analyze the road crash data of Milan City, Italy, gathered between 2014–2017, this study used artificial neural networks (ANNs), generalized linear mixed-effects (GLME), multinomial regression (MNR), and general nonlinear regression (NLM), as the modelling tools. The data set contained 35,182 records of road crashes with injuries or fatalities. The findings showed that unbalanced and incomplete data sets had an impact on outcome performance, and data treatment methods could help overcome this problem. Age and gender were the most influential recurrent factors in crashes. Additionally, ANNs demonstrated a superior capability to approximate complicated relationships between an input and output better than the other regression models. However, they cannot provide an analytical formulation, but can be used as a baseline for other regression models. Due to this, GLME and MNR were utilized to gather information regarding the analytical framework of the model, that aimed to construct a particular NLM.

Quantitative indicators of street lighting with mood, fatigue, mental workload and sleepiness in car drivers: Using generalized structural equation modeling

Inadequate lighting will be associated with some degree of perceptual error such as sleepiness. The main purpose of this study was to investigate the interactions between mood, fatigue, mental workload, and sleepiness and their relationship with quantitative indicators of street lighting in passenger car drivers. The present study was a cross-sectional study that was performed on 270 drivers of passenger cars. The quantitative indices of lighting studied were illuminance, luminance, uniformity, and disability glare which were calculated using the Hagner device (EC1-L) and according to EN 13201 standard. Alertness and mood indices, fatigue scale (SAMN-PERELLI), mental workload (NASA-TLX), positive and negative affect schedule (PANAS) were used. Generalized structural equation modeling (GSEM) was used to investigate the relationship between mood, fatigue, mental workload, and drivers' sleepiness. Data analysis was performed in version 26 of SPSS software and version 14 of Stata software There is a significant relationship between illuminance and mood (P < 0.001). There is a significant relationship between the degree of disability glare on the streets and the mood (P = 0.006). There is a significant relationship between fatigue score and mood (P < 0.001) so that with increasing one unit in fatigue scale, mood score decreases by 0.669 units (P < 0.001). Finally, it can be assured that lighting interventions can be done as an effective way to increase alertness and reduce fatigue and the mental workload of drivers with the aim of reducing night traffic accidents.

A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning

According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor vehicle accidents. Preventing highly intoxicated persons from driving could potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from six alcohol sensors (MQ-3 alcohol sensors) using an optimizable shallow neural network (O-SNN). The experimental evaluation results exhibit a high-performance detection system, scoring a 99.8% detection accuracy with a very short inferencing delay of 2.22 μs. Hence, the proposed model can be efficiently deployed and used to discover in-vehicle alcohol with high accuracy and low inference overhead as a part of the driver alcohol detection system for safety (DADSS) system aiming at the massive deployment of alcohol-sensing systems that could potentially save thousands of lives annually.

A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and Recommendations

With the continuous emergence of new technologies and the adaptation of smart systems in transportation, motorcyclist driving behaviour plays an important role in the transition towards intelligent transportation systems (ITS). Studying motorcyclist driving behaviour requires accurate models with accurate and complete datasets for better road safety and traffic management. As accuracy is needed in modelling, motorcyclist driving behaviour analyses can be performed using sensors that collect driving behaviour characteristics during real-time experiments. This review article systematically investigates the literature on motorcyclist driving behaviour to present many findings related to the issues, problems, challenges, and research gaps that have existed over the last 10 years (2011-2021). A number of digital databases (i.e., IEEE Xplore®, ScienceDirect, Scopus, and Web of Science) were searched and explored to collect reliable peer-reviewed articles. Out of the 2214 collected articles, only 174 articles formed the final set of articles used in the analysis of the motorcyclist research area. The filtration process consisted of two stages that were implemented on the collected articles. Inclusion criteria were the core of the first stage of the filtration process keeping articles only if they were a study or review written in English or were articles that mainly incorporated the driving style of motorcyclists. The second phase of the filtration process is based on more rules for article inclusion. The criteria of inclusion for the second phase of filtration examined the deployment of motorcyclist driver behaviour characterisation procedures using a real-time-based data acquisition system (DAS) or a questionnaire. The final number of articles was divided into three main groups: reviews (7/174), experimental studies (41/174), and social studies-based articles (126/174). This taxonomy of the literature was developed to group the literature into articles with similar types of experimental conditions. Recommendation topics are also presented to enable and enhance the pace of the development in this research area. Research gaps are presented by implementing a substantial analysis of the previously proposed methodologies. The analysis mainly identified the gaps in the development of data acquisition systems, model accuracy, and data types incorporated in the proposed models. Finally, research directions towards ITS are provided by exploring key topics necessary in the advancement of this research area.

Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019

Background: Road safety is a significant public health problem because it causes negative consequences on victims and families. The objective was to analyze the most significant changes in traffic crashes in Ecuador during the period from 2000 to 2019. With data obtained from the National Institute of Statistics and Census, we performed the analysis to identify: the number of traffic crashes, the number of victims, and other study variables. Methods: Descriptive and analytical statistics and the contrast of proportions were used to analyze data from 2000 to 2019. Results: According to the ideal joinpoint analysis model, there was a significant decrease in the number of recorded traffic accidents from 2015 to 2019 of −8.54 per year, while the tendency to die increased in females (2.05 per year) and males (3.29 per year). The most common crash was a collision, and the automobile appeared as the most involved vehicle from 2015 to 2019. The hypothesis test contrast is used to determine if statistically significant differences exist between age groups by gender of the driver injured in the period 2017–2018. Conclusions: This study determines the most significant changes in the variables related to traffic crashes, where mortality due to this cause in the last four years has had a growth rate of 1.8% compared to collisions that presented a rate of −31.12%. The contrast of the hypothesis test shows significant differences in the injury level between males and female drivers, depending on the age group.