Road Safety Performance Research Articles

Effective road safety measures in Greece

The paper presents road safety actions and measures proposed in the framework of the development of the Greek Strategic Road Safety Plan for the period 2021-2030. Five road safety pillars that concern all road crash factors are outlined. For each pillar, actions and particular measures are defined, adapted to the Greek reality and based on the principles of Vision Zero and the Safe System Approach. In total, 44 actions and 200 road safety measures are foreseen and implementation priorities are defined. Next, the framework for monitoring the implementation of actions is presented, taking into account the elements of the Safe System Approach. The use of a set of key performance indicators for road safety is proposed and the methodological framework for the collection of the necessary data and the calculation of road safety performance indicators is provided.

The Baseline project: Key Performance Indicators for Road Safety in EU countries, based on a common methodology for data collection and analysis

The European Commission has made grants available for supporting the collection of road safety performance indicators (called ‘KPIs’) within the EU. 18 EU Member States are participating in this project. For each of the eight KPIs considered, a common methodology for data collection and analysis has been developed by European experts; the methodological guidelines are available at the Baseline website (baseline.vias.be). Data collection and analysis is currently being finalized; the KPIs will be available in October 2022. They will constitute the basis for monitoring road safety progress at national and EU level, will facilitate the formulation of targets at European an national level, and will support decision makers on the most appropriate measures to be taken to improve road safety. Several EU countries have already integrated the monitoring of the KPIs in their road safety strategy.

Measuring road safety advance by IW-EDAS-KNN: A case study for OAS countries

Road Safety is an important aspect when considering the public health of a nation. Road Safety is becoming an increasing concern among nations, and road safety hazards remained a huge problem in some nations, causing financial loss and fatalities. Estimated results demonstrates that road traffic injuries cost the government approximately 3% of GDP, and up to 5% in low- and middle-income countries [1]. Since the road traffic incidence created substantial financial burden, it is necessary to control risk factors and improve the road safety advance. By evaluating the road safety conditions of each individual states, or by comparing the road safety status of a group of states, we can identify the potential strengths and weaknesses of each state hence give suggestions for future improvements. This paper looks at a ten-year data (2009-2019), source from the World Health Organization, the Pan American Health Organization, and the Traffic Safety Data and Analysis Group. The aim of this research is to evaluate and benchmark the road safety conditions of the members states of the Organization of American States by applying Evaluation Based on Distance from Average Solution, a Multi-Criteria Decision-Making (MCDM) technique for classification; identify the continuity in development and advancement of road safety conditions for each state; group the member states of OAS into classes by applying K-Nearest Neighbor Analysis, an algorithm for classification and regression that group objects into classes based on its k nearest neighbors; and make suggestion for future improvements for each member states with respect to their group members. It is concluded that certain indicators are underperformed by a majority of OAS members, which allowed policymakers to identify the potential weakness in road safety performance and offer effective intervention. Additionally, suggestions are made for under-performing countries of the OAS to refer to the road safety procedure from the best-performing nations for future road safety advances.

Towards an Integrated Road Safety Management: The Institutional-Strategy-Environment (ISE) Model

The safe system approach is considered the best practice. Different international organizations highly recommend the implementation of such an approach to significantly improve their road safety performance. The safe system has become a normative approach, a way of thinking. It rests upon different pillars, which represent the framework to be implemented. Among the different dimensions, the management pillar is crucial, which calls for a detailed investigation. First, the aim of this contribution is to understand the implications and requirements of implementing a safe system management model (SSMM). This contribution presents this model and challenges its aims and its limits, especially the promoted management framework. Six main limits of the SSMM are discussed. The different limits of the SSMM emphasize the importance of thinking about singularities and contingencies of the institutional order as well as considering and understanding the context in which a policy is implemented. Second, this contribution proposes an alternative conceptual approach applied to the managerial dimension of the road safety system. It develops the institutional-strategy-environment (ISE) model for elaborating road safety public strategy. This conceptual elaboration is based both on a proven theoretical corpus and on numerous field observations, and expert activities carried out in various countries. The ISE dimensions are essential because they are seldom discussed, even though they are the underpinning of more and more public road safety policies. In this respect, this contribution proposes an alternative approach by considering these three interdependent dimensions. The institutional dimension rests upon a layered approach, while strategy is considered with an adapted SWOT matrix. The environmental dimension is assessed with a Pestel model. The application of such an approach to the road safety field and to test its explanatory power is particularly challenging but opens new avenues for implementing road safety public policy.

Road safety performance index: A tool for crash prediction

Road safety performance index: A tool for crash prediction

Safety Performance of Edge-Lane Roads

Safety Performance of Edge-Lane Roads

Evaluasi Kinerja Penyedia Jasa pada Pekerjaan Preservasi Jalan Nasional dengan Metode Ahp: Ruas Jalan Simpang Kawat – Aek Kanopan -Rantau Prapat - BTS. Provinsi Riau

Road preservation activities have an important role in maintaining road conditions so that they continue to function in serving traffic so that they can extend the service life of the road and ensure traffic safety. One of the road preservation activities that affect the success of the work is the capacity and capability of the service provider. The Service Provider as one of the procurement actors is also an important element that must be considered and will determine and influence the success of project governance. This study attempts to evaluate the performance of service providers on road preservation work based on road performance indicators using the Analytic Hierarchy Process (AHP) method. Service provider performance criteria will be identified as Road Performance Indicators (IKJ), Past Performance (KT), Safety Performance (KK), Financial Stability (SF), and Technical Resource Performance (KSDT). The weight values ​​from the results of the AHP method analysis for each criterion are: IKJ (0.289), KT (0.122), KK (0.203), SF (0.135), and KSDT (0.251). The performance of the preservation work service provider gets a performance score of 2,868 with a good predicate (2 to 3).

Evaluasi Kinerja Penyedia Jasa pada Pekerjaan Preservasi Jalan Nasional dengan Metode Ahp: Ruas Jalan Simpang Kawat – Aek Kanopan -Rantau Prapat - BTS. Provinsi Riau

Road preservation activities have an important role in maintaining road conditions so that they continue to function in serving traffic so that they can extend the service life of the road and ensure traffic safety. One of the road preservation activities that affect the success of the work is the capacity and capability of the service provider. The Service Provider as one of the procurement actors is also an important element that must be considered and will determine and influence the success of project governance. This study attempts to evaluate the performance of service providers on road preservation work based on road performance indicators using the Analytic Hierarchy Process (AHP) method. Service provider performance criteria will be identified as Road Performance Indicators (IKJ), Past Performance (KT), Safety Performance (KK), Financial Stability (SF), and Technical Resource Performance (KSDT). The weight values ​​from the results of the AHP method analysis for each criterion are: IKJ (0.289), KT (0.122), KK (0.203), SF (0.135), and KSDT (0.251). The performance of the preservation work service provider gets a performance score of 2,868 with a good predicate (2 to 3).

Road safety performance rating through PSI-PRIDIT: A planning tool for designing policies and identifying best practices for EAS countries

Road traffic injuries are a leading cause of socio-economic loss in East Asia Summit (EAS) countries. A regular performance rating to monitor progress and calibrate interventions is crucial for road safety improvement, helping to save human lives and reducing economic losses. To this end, we propose a new and easy-to-adapt multi-criteria decision-making method for systematically rating the road safety performance of the EAS countries, the preference selection index with principal component analysis of RIDIT scores (PSI-PRIDIT) (scores of a distribution relative to an identified distribution integral transformation). Using the results from other classical methods as a reference, we rank the EAS countries robustly and place them into three groups over the last decade (2009–2019), based on the composite road safety development index. Our findings should be useful for EAS countries to detect underlying problems and identify best practices, as well as provide government officials, policymakers, and practitioners with meaningful guidelines on the adoption of successful road safety measures. Overall, the proposed rating framework should help to strengthen the institutional capacity for road safety management and addresses road safety issues.

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.

An Evaluation of Road Safety Performance for Selected Road Stretches in Kolhapur City

Abstract: The major cause for global deaths and injuries are mainly due to road crashes. It is worst in developing countries due to rapid and unplanned urbanization. It was estimated in 2010 that about 1, 60,000 persons have died due to road crashes in India. Since, road safety is influenced by many factors it involves complex studies to arrive at the reasons for accidents. As a result the accident study itself remains under estimated in many of the countries specially in developing countries. In recent years several indicators are developed in order to study the factors influencing the accident and based on which the improvement measures can be adopted to reduce accident rate. In the present study two stretches are selected to validate the Road safety Index (RSI) equation and to suggest the improvements to the selected road stretches. Also an analysis is made to check if there is any improvement in the RSI value.

Safe System for Intersections Method Scoring: Comparing Intersection Alternatives Based on Exposure, Kinetic Energy Management, and Movement Complexity

Transportation agencies across the U.S. are increasingly looking to the Safe System approach as a path toward achieving Vision Zero and other similar road safety performance goals. Vision Zero and the Safe System approach seek to eliminate traffic fatalities and serious injuries. The Federal Highway Administration (FHWA) funded an effort to create a Safe System for Intersections (SSI) method to provide a technical basis by which road planners and designers could apply Safe System principles on intersection projects, detailed in the report A Safe System-Based Framework and Analytical Methodology for Assessing Intersections. Applying the SSI method results in a set of SSI scores for each intersection design under consideration. The SSI scores characterize the extent to which an intersection alternative aligns with Safe System principles. This paper builds on the content presented in the FHWA report and describes how the key SSI method components of user exposure, conflict point severity, and intersection movement complexity are combined to produce the SSI scores. This additional background and detail on the development of the SSI scores will support future applications of the method and the identification of future research needs and method improvements.

Influence of Intersection Density on Risk Perception of Drivers in Rural Roadways: A Driving Simulator Study

With the aim of maintaining a decent level of accessibility, the presence of intersections, often in high numbers, is one of the typical features of rural roads. However, evidence from literature shows that increasing intersection density increases the risk of accidents. Accident analysis literature regarding intersection density primarily consists of accident prediction models which are a useful tool for measuring safety performance of roads, but the literature is lacking in terms of evaluation of driver behavior using direct measurements of driver performance. This study focuses on the influence of intersection density on the risk perception of drivers through experiments carried out with a driving simulator. A virtual driving environment of a rural roadway was constructed. The road consisted of segments featuring extra-urban and village driving environments with varying intersection density level. Participants were recruited to drive through this virtual driving environment. Various driver performance measures such as vehicle speed and brake and gas pedal usage were collected from the experiment and then processed for further analysis. Results indicate an increase in driver’s perceived risk when the intersection density increased, according with the findings from the accident prediction modeling literature. However, at the same time, this driving simulator study revealed some interesting insights about oscillating perceived risk among drivers in the case of mid-level intersection separation distances. Beyond the accident research domain, findings from this study can also be useful for engineers and transportation agencies associated with access management to make more informed decisions.

A Predictive Approach to Identify Geometrically Hazardous Road Segments and Evaluate the Relative Safety Effects of Design Alternatives

This study has two goals: First, to fill a gap in the use of the predictive approach for evaluating road safety performance in Ethiopia, the most recent analytical methods of the HSM predictive approach in IHSDM software were used to evaluate the safety and operational effects of the existing roadway geometric design. Second, to assure safety and a sustainable transportation system, the relative safety effects of design changes made to hazardous road segments were quantified. Based on the Crash Prediction Module (CPM) evaluation of IHSDM software, the study identified fifteen hazardous road segments on the existing rural two-lane roads of Addis Ababa to Chacha and Addis Ababa to Dillela. The design changes made to the hazardous road segments have resulted in a remarkable reduction in crash rate, especially on the first top five hazardous segments, where incredible improvements have been observed. The total safety benefits acquired by applying engineering mitigations to the fifteen identified hazardous segments are described as follows: 17.18% reduction in total crash frequency (crashes/year), 58.94% reduction in crash rate (crashes/km/year), and 58.86% reduction in travel crash rate (crashes/million veh-km). In general, the study’s findings underlined the effectiveness of performance-based road safety evaluation and design in providing safe, efficient, and economically-feasible roadway infrastructure. The IHSDM reports and graphical outputs assist decision-making in the roadway design process by providing a quantitative evaluation of the safety impact of various design features and identifying roadway segments with safety concerns. Additionally, IHSDM is a tool capable of saving time for Road Safety Audit (RSA) teams. The paper also outlined the need for a computerized crash database recording system in Ethiopia to develop jurisdiction-specific Safety Performance Functions (SPFs).

Capturing long-memory properties in road fatality rate series by an autoregressive fractionally integrated moving average model with generalized autoregressive conditional heteroscedasticity: A case study of Florida, the United States, 1975–2018

Introduction: Time series models play an important role in monitoring and understanding the serial dynamics of road crash exposures, risks, outcomes, and safety performance indicators. The time-series methods applied in previous studies on crash time series analysis assume that the serial dependency decays rapidly or even exponentially. However, this assumption is violated in most cases because of the existence of long-memory properties in the crash time series data. Ignoring the long-memory dependency could result in biased understanding of the dynamics of road traffic crashes. Method: To fill this research gap, this study proposes an autoregressive fractionally integrated moving average model with generalized autoregressive conditional heteroscedasticity (ARFIMA-GARCH) to capture and accommodate the long-memory decencies in the road fatality rate time series. To further investigate how the factors influencing the fatality risks play a role in the long-memory dependence, the effects of exogenous variables are examined in this study. The analysis is conducted based on the road crash fatality data in Florida, USA over 44 years. Results’ Conclusions: The case analysis confirmed the existence of long-memory property in the crash fatality time series data by both the joint tests of Augmented Dickey-Fuller and the Phillips–Perron, and the integer order of differencing (≤0.5) in the proposed models. The model results reveal that gasoline price and alcohol consumption per capita is positively associated with road fatality risks, whereas unemployment rate and rural/urban road mileage are negatively related to the road fatality risks. Practical Applications: The significant influential factors are also found to account for the long-memory serial correlations between road traffic fatalities to some extent.

Forecasting Road Traffic Fatalities in Malaysia Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model

In Malaysia, travel activities become more intense during the festive seasons, whereby traffic volume on the roads on average increases about 30%. Consequently, this inevitably increases road traffic fatalities. An integrated enforcement program called the OPS Bersepadu has been carried out since 2011 to ensure high road safety performance. This study was carried out to develop a statistical model for predicting the seasonality of traffic fatalities. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to fit road fatalities data between 1980 and 2000 and forecast traffic fatalities from 2001 to 2019. The results showed that the SARIMA (1, 1, 2) (1, 1, 2)12 model fitted the data fairly well and suggest that the SARIMA model is a possible tool that provides an overview of the seasonal patterns of traffic fatalities in Malaysia. The forecasted traffic fatalities based on the SARIMA model were then compared with the actual traffic fatalities during the festive months to explore the effectiveness of the OPS Bersepadu programme to help enforcement authorities allocate optimal resources that could increase the efficiency of enforcement activities to reduce road traffic fatalities.

Evaluation of Road Safety Performance Based on Self-Reported Behaviour Data Set

Self-reported behavioural data, being often linguistic variables that represent a qualitative measure of respondents’ opinions/attitudes, are vague, uncertain, and fuzzy in nature. A road safety performance index, based on these fuzzy data, should consider this uncertainty. In this study, fuzzy numbers were used to describe self-reported behaviour on Montenegrin roads, which was further integrated into the data envelopment analysis (DEA), a technique for measuring the relative performance of decision-making units (DMUs). The vagueness of the performance scores obtained in this way was treated with grey relational analysis (GRA). GRA was applied to the cross-efficiency (CE) matrix constructed by the DEA to distinguish Montenegrin municipalities’ performance, with the main goal of describing road safety in the observed territories in the environment of uncertain/grey data. It is concluded that the proposed DEA–GRA model, based on fuzzy data, provides a more reasonable and encompassing measure of performance, and with which the overall ranking position of municipalities can be obtained.

A Novel Explainable Deep Belief Network Framework and Its Application for Feature Importance Analysis

Feature analysis and selection are highly considered topics in deep learning (DL) real-world applications. However, most existing methods are manual and lack of deep insights of training mechanisms. This is because DL is often viewed as a “black box” and the mechanisms providing the output are often hidden from the user and difficult to understand. Some scientists have utilized visualization, sensitivity analysis, and adversary attack machine learning to increase transparency and have demonstrated successful methods in understanding DL, especially related to convolutional neural nets. This paper builds on these methods and focuses on deep belief networks (DBNs), with two training stages: unsupervised learning and supervised learning. First, a novel algorithm named Visual Input-neuron Importance (Vi-II), based on visualization and feature importance criterion are proposed, to calculate changes in the importance of the input features. Second, a criterion named Visual Hidden-layer Importance (Vi-HI) is proposed to dynamically display the contributions of each hidden layer. Third, a novel framework is put forward by combing the two techniques together, to determine the final structure (input and hidden layers) of DBN, for both unsupervised training and supervised training stages. Then, an application based on the analysis of a road safety performance function is demonstrated. The proposed method provides an accurate description of the model’s inner workings, identifies significant features, and eliminates irrelevant features. At last, the revised dataset and optimized model structure are used for car collision prediction; the result demonstrates that the revised model achieves much better performance than comparable methods.

Analysis of Surrogate Safety Performance Parameters for an Interurban Corridor

Traditionally, road safety performance evaluation is an analysis of crash data from the past. However, methods of analysis from crash data have some well-known limitations from an analytical point of view. These limitations include small data samples causing statistical issues in analysis, under reporting of crashes and very little information about behavioral and environmental aspects at crash time. The micro simulation combined with traffic conflict technique enables the transportation engineers to investigate the safety performance of a corridor without using the crash data. Surrogate Safety Assessment Model (SSAM), utilizes simulated vehicle trajectories from the micro simulation software to investigate conflict severity and locations. In this study, safety performance evaluation is carried out of an interurban corridor of 24.3 km stretch from Gurugram to Faridabad in state of Haryana (India) using simulation software VISSIM (Verkehr In Städten – SIMulationsmodell) and SSAM. Simulated vehicular trajectories were generated and analyzed using SSAM to identify potential conflicts. The surrogate safety measures Time to Collision (TTC), Post Encroachment Time (PET) and Max ΔV are obtained by an analysis from SSAM model for all the three homogeneous sections such as midblock, curve section and intersections separately. The approach presented in the paper helps in the identification of inter-urban corridor locations prone to road crashes and hence serves as a proactive alternative as opposed to historical crashes based analyses.

Using cross efficiency method integrating regret theory and WASPAS to evaluate road safety performance of Chinese provinces

Currently, the road safety performance of a national- or province-level is absorbing much attention from practitioners and academics. A variety of models based on Data Envelopment Analysis (DEA) have been presented to appraise road safety performance in previous literature. However, the main weakness of these methods is that the decision-maker’s psychological behavior is neglected. Besides, Cross Efficiency Method (CEM) adopts the weighted average to aggregate the cross efficiencies which may lead to information loss. To address these limitations, a hybrid road safety evaluation model was constructed by integrating CEM, regret theory, and Weighted Aggregated Sum Product Assessment (WASPAS) to appraise the road safety performance of Chinese provinces. In the presented model, regret theory is employed to depict the psychological behavior of the decision-maker. Then entropy weight method is utilized to calculate the weight of self-evaluation and peer-evaluation. To reduce information loss, WASPAS is adopted to fuse the cross efficiencies and derive the ranking of decision-making units. Finally, the presented model is employed to assess the road safety efficiency in the province of mainland China. The results show that the mean of road safety efficiency score for the eastern, central, and western areas is gradually decreasing. Furthermore, the outcomes of a comparative analysis represent that the results obtained by the proposed model outperform the CEM and DEA road safety model.