Diagnostic Methodology for the Detection of Safety Problems at Intersections

From the past to the future: Modeling the temporal instability of safety performance functions


 
 
 Traffic and road safety is a major concern of many governments around the world as it ensures the safety of its road users. Due to an increase in the number of road users, traffic-related accidents and fatalities have increased annually. Therefore, governments have stepped up their efforts to ensure the safety of road users and to reduce the number of traffic-related accidents and fatalities. However, these efforts have proven ineffective as the data gathered to measure the effectiveness of these efforts either lack credibility or are insufficient. This has led to inconclusive findings concerning the culture of road users and the extent of their awareness of traffic safety. Therefore, this paper performs a systematic data- and text-driven analytics and review of published literature on public awareness of traffic and road safety. The selected papers were first subjected to content analysis before being grouped according to the frequency of use of keywords discussing the issue at hand. As this method enabled us to successfully identify trends as well as the efficacy of traffic and road safety research, therefore, data and text analytics is an effective method of conducting a systematic literature review. The findings of this study will help traffic authorities gain credible insights into public perception, behavior, and attitude towards traffic safety awareness. Our assessment indicates that there are two important perspectives to be considered with regard to road safety; namely human and technology; the latter of which will be the main topic of study on public awareness of road and traffic safety. Moreover, an emerging trend in the literature suggests that more efforts should be allocated to promoting public awareness of road and traffic safety.
 
 

Safety analysts are generally interested in understanding the differences in the safety performance when a two-way street is converted to a one-way operation or vice-versa. Literature exists to understand and predict the safety of two-way streets. However, safety prediction procedures are currently not available for assessing the safety performance of one-way arterials. This research was undertaken to develop safety prediction models for one-way arterials. To accomplish this objective, data collected in California, Illinois, Michigan, Oregon, and Texas were assembled that included a wide range of geometric design features, traffic control features, traffic characteristics, and crash records. The data were used to calibrate predictive models, each of which included a safety performance function (SPF) and several crash modification factors (CMFs). Separate SPFs were developed for fatal and injury crashes (i.e., fatal, incapacitating injury, non-incapacitating injury, and possible injury crash) and property-damage-only crashes. The SPFs were estimated using the negative binomial modeling structure. Severity distribution functions (SDFs) were also calibrated using the fatal and injury data. These functions can be used with the predictive models to estimate the expected crash frequency for each of four injury severity levels.

Objective: Since land surveyors working alongside live traffic encounter unique safety challenges there is a great need for innovative and effective traffic control devices (TCDs) that alert motorists approaching short-term land surveying work sites. Unlike the volume of research that has been completed on traditional work zones, however, there is a limited amount of information that has been collected on how motorists respond to TCDs at land surveying work sites. This article aims to fill the void by investigating motorists’ behaviors in response to the use of 2 supplementary TCDs at land surveying work sites: portable plastic rumble strips (PPRS) and warning lights. Method: Extensive field tests were conducted at various land surveying work sites on 2-lane 2-way urban roadways in New Jersey. Scenarios with and without the use of the supplemental TCDs were designed. Motorists’ behavior changes were then statistically examined by using surrogate safety measures including mean speed, speed variance, speed limit compliance, and braking action. Results: Statistical analyses showed that the traffic speed variations did not significantly increase when the selected supplemental TCD was used; rather, motorists significantly reduced their driving speed. When warning lights and PPRS were separately deployed at the land surveying work sites the average reduction in mean speed was 6.7 and 15.2 percent, respectively. The mean speed was reduced by 19.7 percent when both of these supplementary TCDs were used. Logistic regression models developed to examine the speeding and braking behavior also showed that motorists were more likely to comply with the speed limit and increase their braking rate when the selected TCDs were used. Conclusion: The use of supplemental TCDs can greatly contribute to the changes in motorists’ behaviors at surveying work sites. The changes in motorists’ driving behaviors imply that the motorists reacted favorably to the deployed TCDs at the land-surveying work sites.

In this study, a traffic safety management system (TSMS) for asphalt surfaced roads is developed to improve safety on different classes of roads in Bulgaria. TSMS is a strategic and systematic procedure to improve traffic safety when funding is limited and it is important to determine the most appropriate combinations of traffic safety improvement projects to provide maximum benefits to the population in terms of reducing traffic accidents. The factors included in the developed optimization methodology are the annual report on traffic accidents of the National Institute of Statistics, a study of the roads, a functional classification of the same, historically established, countermeasures to improve safety, costs and determinants of crash reduction in relation to measures to positively modify safety and average daily traffic (ADT). This study demonstrates how the proposed model can determine the best combination of activities to increase safety in order to maximize the benefits in terms of reducing overall crash frequency. The proposed methodology has been applied to highways in Bulgaria. It can be modified for application to roads in a particular city, region and district.

Signalized intersections that interface with light rail transit (LRT) pose a unique challenge to traffic operations and safety. Although progress toward mitigating light rail vehicle–motor vehicle crashes has been made, many agencies still face safety issues at these intersections. Few empirical studies of the effectiveness of the safety measures currently being used have been conducted. Safety measures have not been tested with drivers; rather, the measures they have evolved over time through practice. The objectives of this research were to assess driver comprehension and to explore drivers’ perceptions and opinions of a variety of traffic control devices (TCDs) that were or that might be used at signalized intersections that interface with LRT. The research approach was to present simulated scenes to drivers with a variety of intersection scenarios and TCDs in a focus group setting. The results showed that drivers had preferences for TCDs at these intersections. Although existing TCDs are understood and generally liked, ongoing issues with safety suggest that improvements can still be made. The use of green arrow signal displays or supplemental pavement markings could improve driver situational awareness at some intersections. Enhancements to the existing Manual on Uniform Traffic Control Devices W10-7 (i.e., train icon) activated blank-out sign, such as adding the word “train” or alternating the train icon with the no left turn icon, could result in increased driver compliance. Finally, providing an active warning device on the cross street, such as the W10-7 sign, could increase safety by making drivers and pedestrians more aware of the arrival of a train.

Introducing a multi-variate classification method: Risky driving acceptance among different heterogeneous driver sub-cultures

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Background: Road traffic accidents have been one of the main causes of death worldwide. However, most of the crashes are both predictable and preventable. With the widespread use, mobile phone is considered as a major road safety risk. However, varieties of apps have recently been developed for improving the road traffic health and safety. Despite an increasing trend of these apps, there is no comprehensive analysis of their features and no taxonomy or classification of them based on the traffic safety theories or frameworks. The aim of this study was to explore characteristics of available mobile apps on the road traffic health and safety and classify them based on the Haddon’s Matrix. Methods: To conduct this research, a comprehensive and systematic review of the mobile applications developed for the road traffic health and safety was carried out through using the qualitative content analysis. Google Play was searched through using a combination of the keywords to retrieve the road traffic apps. In order to extract the app features, their description was examined, and their content was analyzed. Then they were classified in four main categories including Road Traffic Health & safety, Road Traffic Training, Road traffic Navigation, and Other Road Traffic apps. Finally, the Haddon’s matrix was applied to analyze and classify those mobile apps residing in two main categories of the traffic health & safety, and the traffic training. Haddon’s matrix is a relevant framework for structured analyses of traffic injury events. In the Haddon’s matrix, the contributions of human, vehicle/equipment and environmental factors to the injuries in the three phases pre-crash, crash and post-crash has been presented. Results: In this study, 916 mobile apps met the inclusion criteria and were included in the final analysis.19 subcategories were identified for classifying the included apps based on their features and functionalities. In total, 620 of the mobile apps were grouped on the basis of Haddon’s matrix. About 61.76% of these apps were categorized as the traffic health & safety group. Among the subcategories of the traffic health & safety group, the highest number of apps (194) was the real-time traffic alerting apps. Behavior based feed-backing subcategory was ranked the second among the traffic health and safety apps with 75 apps. When the researchers applied factors and phases of Haddon’s framework for analyzing the apps, it was found that the Haddon’s factors have been taken into account in the apps grouped into two main categories of the apps including traffic training and traffic health & safety applications. The highest percentage of Haddon’s matrix factor considered for the intervention through the apps was related to the physical & social environment (23.69%) in the traffic training category. The features of 259 apps were classified in the Event/ Driving phase of Haddon’s matrix. Most of the apps in this phase was related to the real-time traffic alerting subgroup. These apps performed in real time while driving. The lowest number of the apps in driving phase was observed in the behavior based feed backing subcategory, which inform drivers about their driving behavior in real-time. Sixteen apps had features that categorized them in both Pre-event / Pre-driving and Event / Driving phases. In addition, 20 apps were found to have feature for intervening in both Event / Driving and Post-event / Post –driving phases. All 235 apps in the traffic training category were in pre-event/ pre-driving phase, which requires users to use these apps before driving. Conclusion: Applying Haddon’s matrix on analyzing and classifying the traffic health and safety apps revealed strengths and weaknesses of the existing related mobile apps in terms of the factors that must be considered for intervention toward prevention and reduction of road traffic accidents as the main public health issue.

Driver behavior has been considered as the most critical and uncertain criteria in the study of traffic safety issues. Driver behavior identification and categorization by using the Fuzzy Analytic Hierarchy Process (FAHP) can overcome the uncertainty of driver behavior by capturing the ambiguity of driver thinking style. The main goal of this paper is to examine the significant driver behavior criteria that influence traffic safety for different traffic cultures such as Hungary, Turkey, Pakistan and China. The study utilized the FAHP framework to compare and quantify the driver behavior criteria designed on a three-level hierarchical structure. The FAHP procedure computed the weight factors and ranked the significant driver behavior criteria based on pairwise comparisons (PCs) of driver’s responses on the Driver Behavior Questionnaire (DBQ). The study results observed “violations” as the most significant driver behavior criteria for level 1 by all nominated regions except Hungary. While for level 2, “aggressive violations” is observed as the most significant driver behavior criteria by all regions except Turkey. Moreover, for level 3, Hungary and Turkey drivers evaluated the “drive with alcohol use” as the most significant driver behavior criteria. While Pakistan and China drivers evaluated the “fail to yield pedestrian” as the most significant driver behavior criteria. Finally, Kendall’s agreement test was performed to measure the agreement degree between observed groups for each level in a hierarchical structure. The methodology applied can be easily transferable to other study areas and our results in this study can be helpful for the drivers of each region to focus on highlighted significant driver behavior criteria to reduce fatal and seriously injured traffic accidents.

The frequency of road traffic accidents (RTAs) is higher in China than in other countries, and traffic safety is a more complex issue. Our study of RTA severity differences among 31 provinces in China identifies the most important traffic management issues in each province. Provinces with the highest RTA risk were identified from RTA mortality data for 31 provinces during 2004–2013. Then provinces with the highest risk according to mortality/per capita GDP, mortality/10,000 vehicles, and mortality/100,000 persons were determined to eliminate the effect of differences in economic development, vehicle numbers, and population on mortality rates. The results show that Guangdong, Zhejiang, Jiangsu, Shandong, Sichuan, Anhui, Fujian, and Hebei have the highest RTA risk in China. Tibet, Anhui, Guangxi, Qinghai, Sinkiang, Gansu, Zhejiang, and Guangdong have the highest risk trend. Thus, Anhui, Zhejiang, and Guangdong have both the highest RTA risk and the highest risk trend. We discuss environmental, road, vehicle, and human causes of RTAs for the province groups. Improving traffic management, strengthening the deterrent effect of legal penalties, and establishing a road safety culture could reduce RTAs in China.

Since out of a lot of traffic problems traffic accidents cause damage to life and properties of people, it stands out as one of traffic problems which needs improvement, and the loss due to traffic accident negatively affects not only the parties to the accident but also the national economy. Thus, continual concern of the government toward traffic safety is getting bigger and lately each local government is preparing a basic plan for traffic safety and vitalizing traffic safety policies. As expanding the responsibility and role of local governments for traffic safety, traffic safety measures which are based on the characteristics of each local government should be studied. Most of analytical methods in the existing traffic accidents prediction models with macroscopic vision focus on socioeconomic variables such as local population and the number of registered vehicles, and present a great deal of prediction error when they are applied in practice. In this context, this study proposed a traffic accident prediction model in respect of macroscopic level for autonomous districts (administrative districts) of Seoul City. The model development was not based on the entire city but on the type of local land usage (development density) whose relationship with traffic accident frequency was analyzed.

Objective Racial and ethnic disparities and/or inequities have been documented in traffic safety research. However, race/ethnicity data are often not captured in population-level traffic safety databases, limiting the field’s ability to comprehensively study racial/ethnic differences in transportation outcomes, as well as our ability to mitigate them. To overcome this limitation, we explored the utility of estimating race and ethnicity for drivers in the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse using the Bayesian Improved Surname Geocoding (BISG) algorithm. In addition, we summarize important recommendations established to guide researchers developing and implementing racial and ethnic disparity research. Methods We applied BISG to estimate population-level race/ethnicity for New Jersey drivers in 2017 and evaluated the concordance between reported values available in integrated administrative sources (e.g., hospital records) and BISG probability distributions using an area under the receiver operator curve (AUC) within each race/ethnicity category. Overall AUC was calculated by weighting each AUC value by the population count in each reported category. In an exemplar analysis using 2017 crash data, we conducted an analysis of average monthly police-reported crash rates in 2017 by race/ethnicity using the NJ-SHO and BISG sets of race/ethnicity values to compare their outputs. Results We found excellent or outstanding concordance (AUC ≥0.86) between reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers. We found poor concordance for American Indian/Alaskan Native drivers (AUC= 0.65), and concordance was no better than random assignment for Multiracial drivers (AUC = 0.52). Among White, Hispanic, Asian/Pacific Islander, and American Indian/Alaskan native drivers, monthly crash rates calculated using both NJ-SHO reported race/ethnicity values and BISG probabilities were similar. Monthly crash rates differed by 11% for Black drivers, and by more than 200% for Multiracial drivers. Conclusion Findings of excellent or outstanding concordance between and mostly similar crash rates derived from reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers (98.9% of all drivers in this sample) demonstrate the potential utility of BISG in enabling research on transportation disparities and inequities. Concordance between race/ethnicity values were not acceptable for American Indian/Alaskan Native and Multiracial drivers, which is similar to previous applications and evaluations of BISG. Future work is needed to determine the extent to which BISG may be applied to traffic safety contexts.

Driving behavior is one of the most critical factors in traffic accidents. Accurate vehicle acceleration prediction approaches can promote the development of Advanced Driving Assistance Systems (ADAS) and improve traffic safety. However, few prediction models consider the characteristics of individual drivers, which may overlook the potential heterogeneity of driving behavior. In this study, a vehicle acceleration prediction model based on machine learning methods and driving behavior analysis is proposed. First, the driving behavior data are preprocessed, and the relative distance, relative speed, and acceleration of the subject vehicle are selected as feature variables to describe the driving behavior. Then, a finite Mixture of Hidden Markov Model (MHMM) is used to divide the driving behavior semantics. The model can divide heterogeneous data into different behavioral semantic fragments within different time lengths. Next, the similarity of different behavioral semantic fragments is evaluated using the Kolmogorov–Smirnov test. In total, 10 homogenous drivers are classified as the first group, and the remaining 20 drivers are classified as the second group. Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) are used to predict the vehicle acceleration for both groups. The prediction results show that the proposed method in this study can significantly improve the prediction accuracy of vehicle acceleration.

Unskilled Driving is one of the reasons for traffic accidents. The accidents could be reduced if drivers' skills are tested accurately. In this research, XuanAi QJ4B high fidelity and immersive driving simulator was used as the experimental platform that included testing roads and controlling software. Ten roads based on real roads in China were prepared to be the test ones that including five types of sections: highway, city, country, Mountain and their mixed one. For each road, there were two driving conditions: driving without traffic, driving with heavy traffic and emergencies that probably caused accidents. The software could record the situation of the vehicle and driver such as the time of finishing task, times of out of road, times of collision and so on. Nineteen drivers participated in the test, and then evaluated driving skills by each other. Comparing the driving situation with or without traffic and analyzing the roads the authors found out that the time of finishing task and times of out of road were sensitive to distinguish the drivers. In addition, under the situation with heavy traffic, comparing the behavior of accident drivers and behavior of drivers without accidents, the result showed that city road, country road and mountain road were sensitive to distinguish the drivers.