Road Safety Studies Research Articles

Examining Spatial Transferability of Direct-Demand Models for Estimating Cyclist Counts at Intersections

Jurisdictions need estimates of bicycle activity levels for road safety and infrastructure planning studies. When counts are not available, direct-demand (DD) models can be used to estimate bicycle activity (often as annual average daily bicycle [AADB] counts) as a function of demographic and network data. However, not all jurisdictions have sufficient count data or resources to develop their own DD model and would benefit from applying a DD model developed in another jurisdiction. However, there is little prior evidence of the spatial transferability performance of DD models or of the factors that affect spatial transferability performance. This paper addresses this gap. The spatial transferability of five DD models from the literature was evaluated across four target jurisdictions: the City of Toronto, the City of Milton, and the Region of Waterloo in Canada, and Pima County, AZ, USA. True AADB data from continuous counts were available for all four target jurisdictions. Spatial transferability was quantified using four metrics. Results demonstrated generally poor spatial transferability, with root mean squared errors (RMSE) up to 600 times higher than those reported in the original model development data sets. Moreover, analysis showed only moderate correlation between model accuracy and the similarity of the target and development jurisdictions (both for average levels of cycling activity and site and network characteristics). These findings underscore a pressing need for enhanced methods to improve the spatial transferability of DD models for estimating bicycle counts.

Weather and road environment’s influence on multi-vehicle crashes in Romania

Abstract The aim of this paper is to assess the weather and road environment’s influence on Multi Vehicle Crashes (MVC) in Romania. In order to capture the real effect of these factors, the road accidents recorded between the months of November and March of each year from 2013 to 2018 are analysed, as this is the period of each year when most of the meteorological phenomena relevant to our research are present. The analysed data set contains complex records of a number of 8,538 accidents which are assessed at the highly disaggregation level (individual), a type of analysis which is rarely found in the road safety studies. A Binary Multiple Logistic Regression (BMLR) is employed for explanatory purposes in order to estimate the probability of MVC. Road accidents that involve at least 3 vehicles are declared as MVC and the probability of their occurrence is labelled as “1”. The rest of the crashes in the data set are assigned the value “0” of the probability. The results of this research show that the highways have the highest risk of a MCV occurring as the odds values show and the sleet is the weather phenomena which highly increase the probability of having a MCV. These risk factors should be further researched in connection with other variables that relate to the driver and weather characteristics.

“You are the company you keep”: A study of peer pressure on driving

Peers play an important role in shaping the behaviours of their counterparts at different stages of life. Studies have shown that peer influence/pressure has a significant role in the traffic environment as well as in many other social settings, especially for risky behaviours. Therefore, the aim of this study was to examine age and sex differences in respect of peer pressure and the role of peer pressure on driver behaviour in Türkiye. A total of 405 drivers with a mean age of 23.84 (SD=4.82) years completed an online survey consisting of the Peer Pressure on Risky Driving Scale (PPRDS), the Brief Sensation-Seeking Scale (BSSS) and the Short Driver Behaviour Questionnaire (S-DBQ, measuring aberrant and positive driver behaviour). First, the psychometric structure of the PPRDS, used for the first time in Turkish, was found to support the original structure, addressing direct pressure in the form of risk-encouraging or risk-discouraging and indirect pressure. Indirect pressure was seen to decrease with age for both males and females, whereas risk-discouraging direct pressure increased with age only for males. Sensation-seeking was positively related to aberrant behaviours. Risk-encouraging direct peer pressure and indirect pressure were positively associated with aberrant (risky) driver behaviour, whereas indirect peer pressure was associated with decreased aberrant driving behaviour and increased positive (helpful, polite) driving behaviour. The findings suggested that drivers feel strong peer pressure in traffic, but it varies by age and sex, and this pressure is stronger for young males. These findings have important implications for road safety and intervention studies.

Self-reported drink driving, enforcement, crashes, and crash reporting: A 6-country comparison

Objective The objective of this study was to compare drink driving and related road safety issues in 2 urban areas of 6 countries and develop an equation for estimating the rate of crash underreporting to the police in urban areas of countries that lack this information. Methods This study is a secondary analysis of 1 to 2 waves of surveys in pairs of matched medium-sized cities in Belgium, Brazil, China, Mexico, South Africa, and Ohio, United States; the surveys supported evaluation of local alcohol harm reduction efforts. Data were from 2017 to 2019 except 2023 for Mexico. Mailed surveys in Ohio and household interviews elsewhere of quota samples matched to census data yielded 23,240 completed interviews. Relevant questions covered drinking, driving under the influence of alcohol (DUI), DUI enforcement, and, except in South Africa, road crashes. GLM regression provided an equation for estimating police reporting rates of urban injury and no-injury crashes from a country’s purchasing-power parity–adjusted gross domestic product (GDP) per capita. Results The percentage of drivers driving unlicensed was 30% in Mexico and South Africa, 15% in Brazil, 8% in China, and <1% elsewhere. Among adults who both drove and drank, self-reported urban DUI rates ranged from 12% in China to 53% in South Africa, with 4 countries between 18% and 26%. Among those reporting DUI, the percentage stopped by police for doing so was 14% in Belgium, 15% in Brazil, 25% in China, 31% in Mexico, 45% in South Africa, and only 3% in Ohio. The surveys yielded data on 380 urban crashes. Past-year crash involvement was 2% to 3% in Belgium and China and 5% to 6% elsewhere. The 10% injury rate in Ohio crashes was significantly below the 24% to 35% rates elsewhere. Injury crashes were almost universally reported except in Brazil (60% reported). Only 49% to 56% of non-injury crashes were reported, except in Ohio (73%). Perceived alcohol-involved crash rates of 18% to 19% in Belgium and Ohio were significantly lower than the 32% reported in Brazil, 41% in China, and 57% in Mexico. In the regression, GDP per capita and injury involvement were positively associated with police crash reporting. Conclusions Our equation more closely approximates urban police crash reporting rates than prior studies that assumed that they matched U.S. data. DUI enforcement is weak/ineffective in urban Ohio. With suggested adjustments, our survey questions should be usable in other international road safety and DUI studies.

Dynamic object detection using sparse LiDAR data for autonomous machine driving and road safety applications

Light Detection And Ranging (LiDAR) sensor offers solutions to extract real-time information of vehicle’s surroundings and can provide a new opportunity to improve the road safety related issues in mixed traffic conditions. This paper explores fundamental principles and combination of algorithms, to accurately detect, classify and track surrounding vehicles on expressways using a vehicle mounted cost-effective LiDAR sensor in dynamic conditions. The proposed approach employs algorithms such as Density-Based Spatial Clustering of Applications with Noise, Random sample consensus, and Kalman filter on 3D LiDAR data for ground and non-ground points segmentation, object clustering, road boundary identification, vehicle classification and tracking. Trajectory data of the tracked vehicles is used to estimate their relative positions and speeds. The surrounding vehicles were classified into three categories: two-wheelers, four-wheelers and heavy-vehicles. The proposed method achieves an accuracy, precision, recall, and F1-score above 94 %, demonstrating its robustness. The resilience and versatility of the proposed algorithm were validated through comprehensive evaluations on a significant dataset spanning over 8000 km (around 3.6 million frames) of driving data collected under varied environmental scenarios on expressway. Further, various applications of proposed methodology in road safety studies are discussed. The approach can extract real-time micro-level trajectory data for surrounding vehicles on expressways in any lighting condition, which is useful for researchers and can be used in connected and autonomous vehicles. It also provides valuable information for assessing surrogate safety measures and identifying road safety issues and suggesting countermeasures, including advanced and cost-effective driver assistance system applications.

Experimental road safety study of the actual driver reaction to the street ads using eye tracking, multiple linear regression and decision trees methods

The article describes the results of a naturalistic driving study done in Kuwait performed by 34 participants wearing a mobile eye tracker to monitor the effect of roadside advertisements on user attention. Eye-tracking (fixations) are the main dependent variable and examined as a function of driving/roadside characteristics, particularly billboards, speed, and so forth. The results obtained were analyzed using traditional statistics (ANOVA test and multiple linear regression) and machine learning (decision tree estimation methods). From the results, it was found that road advertisements negatively affect driver attention and thus road safety. How the level of safety varies with the type and size of advertisement was also investigated. As a consequence, all of the estimates revealed that different aspects of advertising have a detrimental impact on drivers' behavior, and that the duration of fixation and the rate of acceleration before viewing an ad are impacted by advertising type and size, respectively.

Investigating factors that affect conflicts between bicyclists and right turning vehicles at signalized intersections

Due to the recognition of active transportation as a beneficial alternative to more traditional modes of transportation, there is increasing interest in designing infrastructure systems to promote the safe use of forms such as bicycling. Collision data, which are typically used in road safety studies, may be incomplete and the use of such data is reactive as it requires collisions to have already taken place. Additionally, collisions involving bicyclists and vehicles are less common than those between vehicles. As a result, alternative approaches for evaluating the safety effect of various infrastructure attributes, such as the use of surrogate safety measures, specifically traffic conflicts, could be considered. Most collisions between bicyclists and vehicles occur at intersections and of these, collisions between right turning vehicles and bicyclists form the majority. The main objective of this study was to use cross-sectional regression models to investigate various intersection characteristics, including geometry, signal phasing, and bicycle infrastructure, with a view to determining which attributes are associated with a significant effect on right turning conflicts and how this may vary with different conflict severity levels. Using data for 65 signalized intersection approaches across Canada, various intersection attributes were found to be associated with the frequency of right turning conflicts such as exposure levels, the lateral offset of the bicycle facility and the grade of the approach to the intersection. The significance levels of the associated effects of certain attributes were observed to change with the severity level of the conflicts.

A meta-analysis of safety countermeasures for vulnerable users on urban roads

This paper presents a meta-analysis of road interventions for vulnerable users, namely pedestrian traffic lights at crosswalks, bicycle lanes, and bicycle paths. The results of this meta-analysis can be compared with safety effects presented in other publications, such as the HSM (Highway Safety Manual). The results presented here can serve as another source of information for better estimating the safety effects of these interventions to enhance accident prediction modeling. The meta-analysis in this paper evaluated the effects on road safety of the mentioned three interventions, segregated into accidents with fatalities, injuries, as well as their occurrence rate. The meta-analysis demonstrated a reduction in the number of accidents in all three intervention studies. For future work, it is suggested that before-and-after safety studies follow standardized procedures for data collection and provide individualized data for each location, along with open data accessibility, to enhance the use of meta-analysis in road safety studies.

Hot spot analysis and evaluation of influencing factors on regional road crash safety and severity indices: insights from Iran

Inadequate regional road safety studies have been conducted in developing countries like Iran. Regarding regional road safety indices (RSIs), a significant disparity between Iranian provinces was observed. Thus, it was aimed to evaluate the regional RSIs in Iran and identify their influencing factors and potential hot spots. Data on regional road crashes, fatalities, demographics, transportation, health institutions, economics, education, and fuel consumption rates were collected. The association between the variables was evaluated using correlation analysis. Using Moran’s I and local Moran indices, provinces’ spatial distributions were evaluated. Hot spot analysis was used to identify factors influencing RSIs. Significant correlations between the variables were detected. A vast local cluster in terms of fatality per injury (as a crash severity index) was identified in the country’s southeast. The distribution patterns of provinces in terms of seven RSIs were cluster-like. Variable groups, including road length, demographic, income, education, and geographic, influence RSIs in hot or cold spot regions. Crashes were severe in underdeveloped and remote provinces. Increasing income and education levels make it possible to reduce crash severity indices in this country. A positive Moran’s I index does not guarantee the existence of significant local cluster cores in a country.

Road safety evaluation with multiple treatments: A comparison of methods based on simulations

This paper focuses on ex-post road safety evaluation with multiple treatments. The potential outcome framework for causal inference is introduced to formalize the causal estimands of interest. Various estimation methods are compared via performing simulation experiments based on semi-synthetic data constructed from a London 20 mph zones dataset. The methods under evaluation include regressions, propensity score (PS) based methods, and a machine learning-based method termed generalized random forests (GRF). Both PS-based methods and GRF show higher flexibility with respect to functional specifications of outcome models. Moreover, GRF shows great superiority in the cases where road safety treatments are assigned following specific criteria and/or where there are heterogeneous treatment effects. Considering the ex-post evaluation of combined effects of multiple treatments has significant practical value, the potential outcome framework and the estimation methods presented in this paper are highly recommended for road safety studies.

The Transition of Land Use and Road Safety Studies: A Systematic Literature Review (2000–2021)

About 1.3 million deaths occur every year due to road traffic crashes, making road safety a growing concern in many cities. This study considers the extent to which road safety challenges contribute to the built environment. In this paper, we applied the visualization technology of Bibliometrics supported by VOSviewer software and CitesSpace to develop a systematic review to understand the research status and identify gaps in road safety related to built environmental issues. This method has advantages in comprehensive quantitative statistics, visual information display, accurate description, and evaluation. Data was gathered from Scopus databases between 2000 to 2021, and a final number of 437 publications were retrieved. Road safety and land use were the primary keywords to locate relevant publications and identify their relationship. The analysis included the number of publications, research areas, and keywords for an in-depth evaluation. The result was visualized and bibliographically analyzed by demonstrating the existing occurrences between crucial terms, keywords and research areas. The findings revealed that road safety plays a vital role in significant issues, among others, that relate to land use and urban planning in the particular area associated with road safety. Therefore, it is essential to deliberately consider road safety in the very beginning to ensure that proper future solutions can be implemented through appropriate planning and design that is consistent with the surrounding city.

IDENTIFICATIONS AND IMPROVEMENTS OF ACCIDENT BLACK SPOTS OF NATIONAL HIGHWAYS: A REVIEW

India is a large country, and highways play a signicant role in the country's development. As the world's population grows, so does the number of automobiles on the road. As the number of vehicles on the road grows, so does the number of accidents. As a result, there must be reduction is accidents proving the safety of roads. The research of accidental black spots revolves around the study of road safety, and if road safety is addressed, the number of accidents is likely to decrease. So, the identication of Blackspots and improvements to the specic locations in terms of geometrics of road. Indian National Highways are thus to be engineered considering all these inputs related to safety and design standards. All of these aspects were considered in a critical review, which depicted the scenario of accidents occurring on various Indian roadways, as well as their causes and solutions

The Role of Sex in Evaluating Other Male and Female Drivers’ Driving Skills and Behavior

Countries with wider gender gap might host sex-based stereotypical evaluations, which might be present from the early ages and persist. It might also hinder the stereotyped groups’ performance on the stereotyped field, such as driving (Granié and Papafava, 2011; Moè, Cadinu, &amp; Maass, 2015; Steele &amp; Aronson, 1995). Although the effect of sex and gender roles in self-evaluated driving skills and behaviors are studied, the possible effect of sex in evaluating other male and female drivers in their driving skills and behaviors was not a topic of interest in road safety studies. Thus, the aim of the current study was to investigate whether sex of the participants was a significant factor in evaluating other male and female drivers’ driving skills and behavior. 92 Female and 70 male drivers participated in the study. Participants were asked to fill forms of Driver Skill Inventory (DSI) and Driver Behavior Questionnaire (DBQ) to assess driving skills and behaviors of other male and female drivers. The results indicated significant differences between male and female participants in evaluations of other female drivers’ perceptual-motor skills, safety skills, and driving errors. In line with the literature, female drivers were endorsed for their safety skills, and male participants were endorsed for their perceptual motor skills. Similarly, female drivers received higher mean values for lapses and male drivers received higher mean values for errors, aggressive violations, and ordinary violations. Results are important in raising awareness regarding sex-based stereotypical evaluations of drivers, especially towards female drivers, in Turkey.

The influence of traffic-infrastructure factors on pedestrian accidents at the macro-level: The geographically weighted regression approach

Introduction: Walking is an active way of moving the population, but in recent years there have been more pedestrian casualties in traffic, especially in developing countries such as Serbia. Macro-level road safety studies enable the identification of influential factors that play an important role in creating pedestrian safety policies. Method: This study analyzes the impact of traffic and infrastructure characteristics on pedestrian accidents at the level of traffic analysis zones. The study applied a geographically weighted regression approach to identify and localize all factors that contribute to the occurrence of pedestrian accidents. Taking into account the spatial correlations between the zones and the frequency distribution of accidents, the geographically Poisson weighted model showed the best predictive performance. Results: This model showed 10 statistically significant factors influencing pedestrian accidents. In addition to exposure measures, a positive relationship with pedestrian accidents was identified in the length of state roads (class I), the length of unclassified streets, as well as the number of bus stops, parking spaces, and object units. However, a negative relationship was recorded with the total length of the street network and the total length of state roads passing through the analyzed area. Conclusion: These results indicate the importance of determining the categorization and function of roads in places where pedestrian flows are pronounced, as well as the perception of pedestrian safety near bus stops and parking spaces. Practical Applications: The results of this study can help traffic safety engineers and managers plan infrastructure measures for future pedestrian safety planning and management in order to reduce pedestrian casualties and increase their physical activity.

Safety Aspects in Road Construction Projects, Study of Safety Measures used by Workers at Site

Road construction industry has wide expansion growth in the world particularly in past few years. The India’s road network is, in need of extensive construction, reconstruction and maintenance of constructed roads. High traffic rush on roads runs to be safe with minimum time take to reach at their destinations as well as the road construction workers are to be safe during working and playing of traffic. The scope of safety in road construction is to be standardize and develop before starting, during construction and after completion of road project. This road safety study is to identify the critical safety practices in major accident areas with the safety of construction workers. The provision of minimum safety standards is mentioned in IRC: SP: 55-2014 “GUIDLINE ON TRAFFIC MANAGEMENT IN WORK ZONS. This is for making the road route smooth with advance information so road users can understand the road features easily by seeing the advance sign boards, cautionary board, road marking and blinkers etc. Many suggestions have been made by researchers for improvement of safety measures during execution of projects and after completion to road users. Key Words: IRC, SP, FY

Design, Implementation, and Evaluation of a Roadside Cooperative Perception System

Recently, with the advancement in autonomous driving, artificial intelligence, and vehicle-to-everything (V2X) communications, cooperative perception between roadway infrastructure and connected and automated vehicles via V2X communications has attracted increasing attention. However, most of the systems and prototypes reported are still at an initial stage. This paper introduces a newly developed and deployed roadside cooperative perception system with an edge-cloud structure and multiple kinds of sensors, including fisheye cameras, thermal cameras, and long-distance radars. The system is deployed at a roundabout at the intersection of State St. and W. Ellsworth Rd. in Ann Arbor, Michigan, U.S. With an edge-cloud structure, it can execute high-performance detection algorithms on edge devices at the field and collect data and analyze it on the cloud. The performance, in relation to detection rate, localization accuracy, and latency, is analyzed using the data collected from the field. These results show that the system has high potential for a wide range of applications, including infrastructure-vehicle cooperative perception, traffic data collection, and road safety studies. Two example applications—traffic volume monitoring and road safety warning—are introduced in the latter part of the paper to demonstrate the system’s capability.

Assessment of road safety intervention implementation in Benin in 2019

The study aims to assess the level of implementation of road safety interventions in Benin. The research is based on an evaluative study of road safety aimed to analyze the implementation and logic of road safety interventions, conducted in Benin in 2019. It combined a review of the gray literature and a qualitative component. The data were collected through documents and interviews in structures involved in road safety management. Road safety was a national priority with one lead institution and several structures involved. There was a lack of consensus among stakeholders, insufficient framework documents, resources, legislative texts, and study data. Few roads were in good condition and very few allowed the separation of two-wheeled vehicles. The vehicle fleet was outdated. Various activities were carried out to raise awareness, to educate the population and to enforce the texts but they were insufficient and poorly coordinated. Reference hospitals had the minimum service to deal with trauma cases. The interventions had not yet resulted in a reduction in the number of injuries and fatalities by accidents, which was increasing. Benin has made great efforts in the area of road safety. However, there are still some shortcomings to take into account.

Estimating heterogeneous treatment effects in road safety analysis using generalized random forests

Numerous evaluation studies have been conducted on a variety of road safety measures. However, the issue of treatment heterogeneity, defined as the variation in treatment effects, has rarely been investigated before. This paper contributes to the literature by introducing generalized random forests (GRF) for estimation of heterogeneous treatment effects (HTEs) in road safety analysis. GRF has high functional flexibility and is able to search for complex treatment heterogeneity. We first perform a series of simulation experiments to compare GRF with three causal methods that have been used in road safety studies, i.e., outcome regression method, propensity score method, and doubly robust estimation method. The simulation results suggest that GRF is superior to these three methods in terms of model specification, especially with the existence of nonlinearity and nonadditivity. On the other hand, a large dataset is required for accurate GRF estimation. Then we conduct a case study on the UK’s speed camera program. Our results indicate significant reductions in the number of road accidents at speed camera sites. And the heterogeneity in treatment effects is found to be statistically significant. We further consider the associations between the baseline accident records, traffic volume, local socio-economic characteristics, and the safety effects of speed cameras. In general, the effect of speed cameras is larger at the sites with more baseline accident records, higher traffic volume, and in more densely-populated and deprived areas. Several policy suggestions are provided based on these findings. The evaluation of HTEs likely offers more comprehensive information to local authorities and policy makers, and improves the performance of speed camera programs. Moreover, GRF can be a promising approach for revealing treatment effect heterogeneity in road safety analysis.

Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain.

Knowledge of the kilometers traveled by vehicles is essential in transport and road safety studies as an indicator of exposure and mobility. Its application in the determination of user risk indices in a disaggregated manner is of great interest to the scientific community and the authorities in charge of ensuring road safety on highways. This study used a sample of the data recorded during passenger vehicle inspections at Vehicle Technical Inspection stations and housed in a data warehouse managed by the General Directorate for Traffic of Spain. This study has three notable characteristics: (1) a novel data source is explored, (2) the methodology developed applies to other types of vehicles, with the level of disaggregation the data allows, and (3) pattern extraction and the estimate of mobility contribute to the continuous and necessary improvement of road safety indicators and are aligned with goal 3 (Good Health and Well-Being: Target 3.6) of The United Nations Sustainable Development Goals of the 2030 Agenda. An Operational Data Warehouse was created from the sample received, which helped in obtaining inference values for the kilometers traveled by Spanish fleet vehicles with a level of disaggregation that, to the knowledge of the authors, was unreachable with advanced statistical models. Three machine learning methods, CART, random forest, and gradient boosting, were optimized and compared based on the performance metrics of the models. The three methods identified the age, engine size, and tare weight of passenger vehicles as the factors with greatest influence on their travel patterns.

Propensity score methods for road safety evaluation: Practical suggestions from a simulation study

The propensity score (PS) based method has been increasingly used in road safety evaluation studies. However, several major considerations regarding its implementation arise when using the PS method. First, as is well known, the PS method is ‘data hungry’ in terms of the number of treated and control units, however, it is sometimes difficult and time-consuming to construct a large sample in road safety studies. It would be helpful to better understand how to choose a proper sample size, as well as the ratio of the number of treated units to the control ones. Second, the criteria used for covariates selection of the PS model were not fully consistent across the existing road safety evaluation studies. Due to the complicated mechanisms behind the implementation of road safety measures and policies, including all relevant covariates that affect both the selection into treatment (i.e., implementation of road safety measures) and the outcomes (i.e., road accidents) is impossible. In this paper, we conduct a simulation study to investigate such issues and provide some practical suggestions for using PS methods in road safety evaluations. The estimator considered in this study is the inverse probability weighting estimator based on the PS. Our results suggest that the bias and variance of the estimated treatment effect will remain stable when the sample size reaches a certain level. A proper sample size is the one that ensures relevant covariates achieve acceptable balance. Regarding the issue of covariates selection, including the covariates that significantly affect the road accidents is recommended, regardless of whether they affect the implementation of road safety measures. This study also proposes practical procedures for using the weighting approach to evaluate the effects of road safety treatments.