Road Safety Assessment Research Articles

Application of the IRAP Method Combined with GIS to Improve Road Safety on New Highway Projects in Algeria

This article aims to proactively predict high-risk accident areas in a new highway project in terms of its technical and geometric characteristics. The purpose of this study is to provide road project managers with better road safety assessment tools for the vehicle occupant category. The methodology used combines the International Road Assessment Program (IRAP) methodology and the Geographic Information System (GIS). The IRAP program enables the calculation and classification of risks for each 100-meter segment besides their categorization using a star rating; whereas, the GIS is used to map the risks and identify high-risk accident segments. This method was applied to a new highway project of 110 hectometres located in GHAZAOUET in Algeria. The results of the study revealed that this combined method is innovative in more than one way as it is an effective decision-making aid tool for road safety experts. It, therefore, makes it possible to target accident-prone sections in a faster way before carrying out on site road safety inspections. Technically speaking, this study shows that the existence of closely curved radii mixed at traffic speeds exceeding 80 km/h and the presence of road exits are the main factors affecting the safety of vehicle occupants. Speed control; thus, remains one among the cheapest measures to improve safety.

Implementation of Road Safety Audit to Highlight the Deformities in the Design and Environmental Safety Features: A Case Study on National Highway-326

Road Safety Audit is an advanced strategy for detecting the highly affected areas which are more prone to accidents and security Increase of existing and new streets. RSA is a well-organized, economical and making a move to improve road security. It is demonstrated that RSA has the capacities to rescue lives as it provides and formulates all possible safety measures and techniques which are extremely essential to have a secured journey. The RSA was first implemented in Britain and later followed by other nations like Australia, Denmark, Malaysia, Singapore, New Zealand, Canada and United Kingdom & United States of America. It is at different phases of execution in flourishing countries like India, South Africa, and Thailand. RSA plays a significant role for enhancing road security in India, as fundamental and exact information on accidents still can't seem to be gathered. The fundamental part of this study is to assess Road Safety Audit of a segment of two-path National Highway (NH) - 326 and the job of an auditor is to give autonomous suggestions in the form of written recommendation. The fundamental goal of the investigation is to recognize highly affected zones which are more prone to accidents and dark spot regions on the road from FIR, to think about the impact of geometric design of roads and influence of traffic characteristics on various parameters of roads and experimentation and establishment of statistical relationship between accidents rates and different variables causing accidents. This paper investigates the deformities in the design and other safety features.

Road safety development evaluation for the ASEAN countries by TOSPSI-RSR

This study develops a systematic methodology, TOPSIS-RSR, to assess road safety development in an integrative manner for ASEAN (Association of Southeast Asian Nations) countries. Using other methods and measures as references, 11 countries are ranked based on their overall road safety performance over the past ten years (2009-2018) and divided into several classes. This provides government officials and policy makers in all ASEAN member countries with a flexible tool to comprehensively assess road safety developments. Providing the ability to identify delays in the implementation of action plans and to proactively reallocate resources to more effective actions when needed. It helps ASEAN member countries to build capacity for sustainable safety management and supports them in developing future strategies.

Assessing the Australian occupational driver behavior questionnaire in U.S. taxi drivers: Different country, different occupation and different worker population

Background: Promoting safe driver behaviors is an important aspect of road safety. To better understand road safety behaviors, there is a role for practical instruments that can validly measure typical road safety behaviors among occupational drivers. The Occupational Driver Behavior Questionnaire (ODBQ) was developed to assess road safety behaviors among home health nurses in Australia. Methods: We administered a cross-sectional survey to a sample of taxi drivers in two U.S. metropolitan areas. The survey included Newnam’s ODBQ-12 and a study-specific 15-item version (ODBQ-15) assessing 4 different road safety behaviors with 3 more items added and motor-vehicle crashes in the past year. Logistic regression analyses examined the association of the road safety behaviors with motor vehicle crashes. A series of confirmatory factor analysis (CFA) models assessed the construct validity of the ODBQ-12 and ODBQ-15. Results: We pooled survey data from 497 Houston drivers and 500 Los Angeles drivers to assess study aims. CFA models examining the 12-item and the 15-item ODBQ versions had good model fit (Comparative Fit Index > 0.95, Tucker Lewis Index ≥ 0.95, root mean square error of approximation < 0.06, standardized root mean square residual ≤ 0.05). The ODBQ’s road safety behaviors were significantly associated (p < 0.001) with crashes while working (ORs 0.51–0.75) and not working (ORs 0.57–0.84). Conclusions: The ODBQ-12 and ODBQ-15 were both significantly associated with motor vehicle crashes among taxicab drivers in two large U.S. metropolitan areas. Researchers studying occupational drivers who transport passengers may want to consider using the ODBQ-15. The 3 additional items are meaningful to this workforce and are priority areas for international road safety efforts.

Using the Eye Tracking Method to Determine the Risk of Advertising Devices on Drivers’ Cognitive Perception

The paper focuses on road safety assessment. The main objective was to assess the impact of different types and sizes of advertising devices as a potential distraction for drivers. Distraction of driver’s attention in real traffic was evaluated using the “Wiener Fahrprobe” structured observation method. As a method for reliable data collection, the eye tracking method was used to accurately define the time delay caused by the observation of the advertising device. As part of the assessment of the direct impact on drivers, test runs were carried out with 40 drivers on a pre-defined section of road on which different types of advertising devices were present. As an additional, supporting measurement, a vehicle simulator was also used. From the obtained knowledge it was possible to create a unique classification system that can be used to assess the severity of any installed advertising device in general. In the case of distraction, it was found that dynamic advertising devices attract the most attention than conventional static devices and appear to be a significant risk to road safety.

The applicability of Smeed’s Law in an evaluation of national road safety management systems: Montenegro versus Great Britain

The need for an evaluation and international comparison of the effectiveness of national road safety management systems (NRSMSs) is growing, since international goals aim to reduce road deaths from decade to decade, which should be achieved at the national level. In addition, an NRSMS evaluation model needs to be as simple as possible to be applicable in all countries of the world. To this end, an NRSMS evaluation model was generated in this study based on Smeed’s Law, which used population, vehicles, and road deaths and applied the definite integral to the following hypothesis: The effectiveness of an NRSMS is equal to the difference between the adjusted Smeed’s Law assessment of road safety and the current road safety condition achieved by implementing the NRSMS. Applying this model to Montenegro and Great Britain, it was found that Smeed’s Law could be used to assess the effectiveness of NRSMSs with respect to the turning point from an increasing to a decreasing number of road deaths.

Combining Traffic Safety Exposure Indicators in a Composite Exposure Index

The problem of choosing only one relevant safety performance indicator for the purpose of comparing and assessing road safety situations has been the subject of many recent research studies. This paper shows the concept of creating a composite exposure index based on available data. The procedure of creating a model for calculating this indicator is based on the analysis of quality of individual exposure indicators and the size of their impact on the direct safety performance indicators – number of road crashes and their consequences. The following four models (TOPSIS EQUAL, TOPSIS CRIT-IC, PROMETHEE EQUAL, PROMETHEE CRITIC) for determining weighted coefficients of the individual indi-cators that participate in the creation of the composite exposure index have been analysed in this paper. The method used for defining the composite exposure index is the “high-efficiency method” based on which the final shape of the model for defining the composite exposure index has been defined. The main aim of this paper is to create a model for defining the composite index of traffic exposure. The final outcome is to provide an opportuni-ty to evaluate and rank traffic safety levels based on the unique road traffic risk.

Application of Fuzzy Linguistic Rating and Entropy-Based GRA to Address Uncertainty in Safety Performance Index Construction

As the need to assess the level of road safety grows, there is a noticeable tendency of experts to use one overall composite index that contains information on a number of safety performance indicators (SPIs). Indicators commonly used in road safety assessment are numerical, and their natural uncertainty and vagueness are often overlooked. However, there are also SPIs that are rather linguistic, such as data on driver behavior, which are most often collected through questionnaires and are considered qualitative, imprecise, and fuzzy. Together with inappropriate selection of weighting and aggregation methods, such data can be a source of uncertainty and can lead to unreliable results and erroneous conclusions. In this regard, the present study provides a systematic and efficient hybrid method that integrates three different procedures to deal with unavoidable uncertainty in each step of index construction. The application of fuzzy linguistic rating grasp insight into the ambiguity that is intrinsic in drivers’ self-assessment. Entropy describes each observed behavior by quantifying the disorder of a system. Grey relational analysis aggregates behavioral indicators into a composite index, doubting their sufficiency and completeness. A case study of Montenegro has been provided to demonstrate the practical applicability of the proposed method in safety assessment under uncertainty. Results abstracted not wearing the seatbelt as the most common negative behavior among drivers in Montenegro, followed by using the telephone while driving, speeding, and driving under the influence of alcohol. In addition, municipalities are ranked according to the level of road safety.

Use of Parallel ResNet for High-Performance Pavement Crack Detection and Measurement

In the process of road pavement health and safety assessment, crack detection plays a pivotal role in a preventive maintenance strategy. Recently, Convolutional Neural Networks (CNNs) have been applied to automatically identify the cracks on concrete pavements. The effectiveness of a CNN-based road crack detection and measurement method depends on several factors, including the image segmentation of cracks with complex topology, the inference of noises with similar texture to the distress, and the sensitivity to thin cracks. The presence of shadows, strong light reflections, and road markings can also severely affect the accuracy in detection and measurement. In this study, a review of the state-of-the-art CNN methods for crack identification is presented, paying attention to existing limitations. Then, a novel deep residual convolutional neural network (Parallel ResNet) is proposed with the aim of creating a high-performance pavement crack detection and measurement system. The challenge and special feature of Parallel ResNet is to remove the noise inference, identifying even thin and complex cracks correctly. The performance of Parallel ResNet has been investigated on two publicly available datasets (CrackTree200 and CFD), comparing it with that of competing methods suggested in the literature. Parallel ResNet reached the maximum scores in Precision (94.27%), Recall (92.52%), and F1 (93.08%) using the CrackTree200 dataset. Similarly, for the CFD dataset the novel method achieved high values in Precision (96.21%), Recall (95.12%), and F1 (95.63%). Based on the crack detection and image recognition results, mathematical morphology was then used to further minimize noise and accurately segment the road diseases, obtaining the outer contours of the connected domain in crack images. Therefore, crack skeletons have been extracted to measure the distress length, width, and area on images of rigid pavements. The experimental results show that Parallel ResNet can effectively minimize noise to obtain the geometry of cracks. The results of crack characteristic measurements are accurate and Parallel ResNet can be assumed as a reliable method in pavement crack image analysis, in order to plan the best road maintenance strategy.

Mathematical analysis of certain road safety indicators in the Russian Federation

Introduction. A large number of fatalities on the roads of the Russian Federation is a significant state problem; to reduce this indicator, various target programs are being developed at the national level. To assess the achievement of the established target programs, it is necessary to perform a mathematical analysis of certain road safety indicators, which makes it possible to establish the most accurate mathematical model describing certain interrelationships of the indicators under consideration for the subsequent forecast and assessment of the achievement of the established target indicators, which will allow assessing the effectiveness certain guidelines of state policy in the area under consideration.Methods and materials. In the research work, methods of statistical and mathematical analysis, models for determining the state of road safety at different levels - world, state and regional, depending on various parameters are used.Results. A mathematical model for assessing road safety indicators based on the analysis of transport and social risk parameters is proposed, their low relationship is determined and the possibility of using them as independent parameters for assessing the state of road safety at the national level is established.Conclusion. The absence of a close relationship between the indicator of social risk and motorization, measured in the number of cars per 1000 inhabitants (the value of the correlation coefficient is 0,422), was found, which allows one to judge the presence of other factors influencing the parameter under consideration.

A methodology for the characterization of urban road safety through accident data analysis

As is known, road accidents essentially depend on four interrelated factors: human behaviour, vehicle efficiency, environmental conditions and the characteristics of the infrastructure. Although the vast majority of accidents is attributable to the first three factors, almost always attributable to improper user behaviour, it is of fundamental importance to try to reduce that part of the risk attributable to the infrastructure. In this research, the problem concerning the assessment of urban road safety in existing roads, in order to identify the dangerous sections on which to concentrate resources to make the functional adjustments deemed necessary, is addressed through the analysis of the accident rate found in operation. In particular, the research proposes a new model that characterizes the intrinsic risk of a urban road infrastructure through the assessment of accidents, disaggregated into different types, with a risk index that is a function of the two most significant variables that represent the accidents: the frequency with which they occur and the severity of the damages produced. The study also provides three aspects for achieving improved urban road safety. The first identifies the critical road sections (blackspots), through the application of the new model to the classic methods of accident assessment. The second defines the functional adjustments necessary to reduce the causes of accidents by comparing the risk of accidents, determined with the new methodology for each type of accident, and the technical characteristics of the road network in question. The third establishes the intervention priorities, based on an economic planning linked to the available budget, among the functional adjustments identified to reduce the risk of accident.

A Framework for Road Safety Assessment and Behavioral Change

A Framework for Road Safety Assessment and Behavioral Change

What Do Traffic Simulations Have to Provide for Virtual Road Safety Assessment? Human Error Modeling in Traffic Simulations

Will Advanced Driving Assistance Systems (ADAS) and Highly Automated Driving (HAD) perform in the expected manner? Will they actually make road traffic safer, or will they potentially introduce new critical situations or road accidents? It is almost impossible to address these questions solely through real-world tests. A promising tool to provide appropriate answers in a time-and cost-efficient way without exposing subjects to risk are virtual assessment methods. Reliable safety assessments are only possible, if the traffic simulations provide realistic traffic, including critical situations and road accidents. This paper provides a review of how human error contributes to critical situations and accidents in road traffic. The focus is on the causes and mechanisms of human error, which driver behavior models must address in order to simulate realistic traffic. For this purpose, Rasmussen’s error taxonomies are applied to the traffic context and extended with further research. The paper shows the causes of those human errors and that the underlying mechanisms thereof should be taken into account in order to obtain more transparent and realistic driver behavior models. It is shown, which concepts for modelling realistic traffic exist and how virtual safety assessment could benefit from this development. In addition, the driver behavior model DReaM (Driver Reaction Model) is presented to address the issues resulting from existing cognitive driver models.

Mobility Trends and their effect on Traffic Safety During the Covid-19 Pandemic: Case Study Republic of Croatia

Safety analysis focuses on how traffic safety can change while mobility analysis is used to determine how people change travel behavior. The integration of mobility, safety and behavioral data related to COVID-19 can provide valuable insights to decision makers. Wide availability of mobile sensors has given us the opportunity to be able to assess changes in the performance and mobility of transport systems in, almost real time. The researchers also measured the impact of COVID-19 on human mobility using public mobile location data available from many companies such as Google and Apple, which is very useful for changing human mobility. The platforms produce aggregated metrics of daily mobility, including the purpose of travel, the mode of travel, and imputations of social demographics. Based on a comprehensive data set of people who participated in the collected accident data and mobile device data, we record the impact of COVID-19 on traffic safety. The paper systematically and statistically approaches the assessment of road safety in Croatia during the COVID-19 pandemic.

Mobility Trends and their effect on Traffic Safety During the Covid-19 Pandemic: Case Study Republic of Croatia

Safety analysis focuses on how traffic safety can change while mobility analysis is used to determine how people change travel behavior. The integration of mobility, safety and behavioral data related to COVID-19 can provide valuable insights to decision makers. Wide availability of mobile sensors has given us the opportunity to be able to assess changes in the performance and mobility of transport systems in, almost real time. The researchers also measured the impact of COVID-19 on human mobility using public mobile location data available from many companies such as Google and Apple, which is very useful for changing human mobility. The platforms produce aggregated metrics of daily mobility, including the purpose of travel, the mode of travel, and imputations of social demographics. Based on a comprehensive data set of people who participated in the collected accident data and mobile device data, we record the impact of COVID-19 on traffic safety. The paper systematically and statistically approaches the assessment of road safety in Croatia during the COVID-19 pandemic.

Development of Models for Express Analysis of Road Safety

Results of a study of an actual scientific and technical problem of determining the road safety level on road sections are presented in the paper. The aim of the study is to develop an express method for assessing road safety based on improving the method of the final accident rate by reducing the partial accident rate with the required accuracy of determining the safety level on public roads. The formalization of the relationship between the parameters of road traffic conditions has been carried out to study the level of safety on highways. Latent factors of the parameters of traffic conditions have been obtained on the basis of reduction of their aggregate by the method of principal components, taking into account the relationship of partial accident rates. This condition has allowed to develop analytical models for determining road safety for implementation in the practice of conducting a road safety audit. A method for identifying hazardous areas on highways has been proposed in the paper. It includes 11 parameters of road conditions out of 18 partial accident rates proposed in the prototype and regulatory documents of Ukraine. The adequacy of the model is determined by deviation values of the obtained data according to the prototype model from the values of the relative accident rate, which were 3.22 and 18.61, respectively. The proposed model reduces the deviation value by six times and affects the accuracy of determining the safety level. Experimental studies have been conducted on 79 km of highways (on 385 sectors with constant values of traffic conditions parameters). The results of a comparative analysis of the safety level for the prototype model and the developed model have been identical and shown a decrease in time and labor costs by 1.6 times without loss of accuracy. Thus, the developed methodology is recommended to be applied for determining the potential danger of road sections during an express analysis of the road safety or when conducting a safety audit at various stages of the road life cycle.

Roadway Safety Assessment and Star Rating using iRAP along SH-11A (Jind-Kaithal) in Haryana

Figures cross 3,500 deaths and casualties on roads all around world every day in low- and middle-income countries and contribute about 90% of the 1.25 million road deaths. This number of road deaths is projected to increase by 50 percent by 2020. The compound problem for developing countries is caused by the rapid development of roads, irrespective of design or security, lack of attention to vulnerable road users and lack of a culture of road safety (i.e., safe behaviour, vehicle safety regulations, road safety policy, road safety assessment, and enforcement). This paper deals with the star rating and road safety assessment of State Highway-11A, Jind-Kaithal, Haryana though the section is straight but numerous causalities were reported on the route in recent years. ViDA, an online road safety assessment and star rating analysis tool is used to get the Star Rating Scores and Safer Roads Investment Plans.

The System Dynamic and Compram Methodologies for Modelling, Simulation and Forecasting of Road Safety of Uzbekistan

Abstract In Uzbekistan, about 2,000 people die every year as a result of a traffic accident. At the same time, according to the Pulitzer Centre on Crisis Reporting, the Republic has the lowest rate in road mortality among the countries in the Central Asian region - for every 100,000 people, it is 11.32 people. Losses from road accidents in Uzbekistan equivalent up to 2.8% of GDP that is also one of the lowest indicators. But according to traffic safety experts, the losses from accidents are greater than reported data. Nowadays there are a lot of methods to analyse and ensure road safety and traffic management on the roads. The authors believe that road safety is a complex societal problem not only in Uzbekistan but all over the world. One of these methods is System Dynamic (SD) and COMplex PRoblem hAndling Methodology (COMPRAM). In this work, the Vensim PLE SD software tool (it is one SD tool amongst many others) has been used to perform the SD modelling of the case study at hand. In the methods of system dynamics, a computer model is created using a graphical technique for constructing flow diagrams and causal relationships of the system under study and then simulated on a computer. COMPRAM allows us to figure out the way to handle complex societal problems while involving a System Dynamics (SD) simulation option. There are similarities between COMPRAM and the traditional way of analysing road safety. In traditional ways, each element or factor is studied as a separate phenomenon. These indicators are studied in the stages of COMPRAM. This article has been studied a different aspect of how road accidents happen. The developed a comparison (according to six criteria) of the different modelling paradigms which have been historically used to assess road safety. Also, the authors made a comparison of the COMPRAM methodology with the traditional road safety assessment approach to highlight similarities and differences.

Proactive pedestrian safety evaluation at urban road network level, an experience in Kolkata City, India

In developing nations, road traffic crashes involving pedestrians have become a foremost worry. Presently, most of the road safety assessment projects and selection of interventions are still restricted to traditional methods that depend on historical crash data. However, in low and middle-income countries such as India, the availability, reliability, and accuracy of crash data are uncertain. Alternatively, Post Encroachment Time (PET) has added attention as a proximal indicator to examine pedestrian-vehicular potential crashes and address pedestrian risk under mixed traffic conditions. Hence, it will be meaningful to examine if the PET is a good substitute for pedestrian-vehicular crashes and if so, what built environment and pedestrian-level factors influence PET. In this background, the present study establishes a mathematical association between the average PET value of the urban road network level and actual crashes. Afterward, multiple linear regression models are developed to study the impact of the built environment, traffic parameters, and pedestrian-level attributes on PET. The outcomes indicate that vehicle speed, lack of enforcement, absence of traffic signal (for traffic as well as pedestrians), land use type, slum population, inadequate sight distance, pedestrian’s state of crossing, and pedestrian’s risky crossing behaviour substantially affect the average PET at road network-level.

Factors affecting motorcyclists’ behavior in car-following condition

Car-following (CF) maneuver plays an important role in both traffic flow analysis and road safety assessment. However, many studies on this issue have focused on the drivers’ behaviour, neglecting the different driving behaviour of the motorcycle riders and the vulnerability of this type of user. The aim of the present paper is to analyse the factors affecting the riders’ behaviour in CF condition, by using two surrogate safety indicators, Time Headway (TH) and Time To Collision (TTC). A field experiment was conducted to collect data. Twenty young motorcyclists participated in the study driving their own motorcycles, which were instrumented with a camera and a global positioning system device (GPS). A digital image processing algorithm was used to obtain CF related indicators. The results of the study showed that Time Headway (TH) and Time To Collision (TTC) are independent of each other, extending the result obtained in the literature for the four wheeled vehicles to the motorcycles. TTC and TH were therefore analyzed separately. As for TTC, it was observed that only in few cases (0.8%) the riders were found in risky condition (TTC < 5 s), confirming the role of this parameter for the detection of an impending risk. On the other hand, TH gives information about a potentially risky situation, and 99% of the riders were found in such condition (TH < 2 s). Due to the few cases in TTC risky conditions, only TH was further modelled by using the hazard-based duration analysis. Accelerated Failure Time (AFT) model specification was used, and the Weibull distribution provided the best fit of the data. The model identified four covariates affecting TH and all of them had the effect of reducing TH, increasing the collision risk. The time spent on CF condition, the speed of the motorcycle, the willingness of overtaking and the engine size of the motorcycle were the factors that reduced TH.The findings of this study could be considered in the development of microsimulation models; the factors that were found affecting the motorcyclists’ behavior in car-following condition could be considered for a more realistic simulation of the riders’ choice of Time Headway.