Traffic Safety Evaluation Research Articles

A short-term vehicle speed prediction approach considering dynamic traffic scene

Vehicle speed serves as a crucial indicator in traffic flow efficiency and safety evaluation. Previous researches typically utilize the time-series data of target vehicle speed or the relative speed and distance between the target and leading vehicles as speed prediction model input, which are suitable for stable and single-vehicle scenes. However, the surrounding traffic scenes dynamically changes during the vehicle’s driving process, the driver needs to allocate attention to the key scene vehicles in the surrounding traffic scenes and adjust the current speed based on these vehicles’ behavior. Therefore, this paper proposes a short-time vehicle speed prediction model considering the dynamic traffic scenes. A dynamic grid method is adopted to divide the target vehicle’s surrounding areas into left front, front, and right front regions. And key scene vehicles are identified based on each region’s state. Then the relative distance, speed, and acceleration data between the target vehicle and key scene vehicles are selected as model inputs. Vehicle trajectory data from the South-North No. 1 Expressway in Hefei and Ranger optimized Temporal Convolutional Network (TCN) are utilized to train the speed prediction model. Experiment results indicated that under 1 s, 2 s, and 3 s prediction lengths, the model’s average Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were 0.430 and 0.271 respectively. Compared to the speed prediction model considering the static traffic scenes, the average RMSE and MAE for the three prediction lengths have been reduced by 49.15 % and 56.02 % respectively.

Traffic Safety Evaluation of Downstream Intersections on Urban Expressways Based on Analytical Hierarchy Process–Matter-Element Method

This study aimed to explore the traffic safety evaluation model for downstream intersections of urban expressway exits and make up for the shortcomings in safety research on downstream intersections of urban expressway exits. We constructed a comprehensive traffic safety evaluation index system, established a traffic safety evaluation model, and divided precise safety evaluation levels using the AHP–Matter-Element analysis method, establishing a traffic safety evaluation index system consisting of eleven indicators. The effectiveness of this method was validated through an assessment of traffic safety at the intersection of Dongsheng Street and Free Road in Changchun City. A theoretical basis for improving traffic safety at downstream intersections of urban expressways and a reference for subsequent related research were provided.

Traffic safety comprehensive evaluation of urban tunnel visual guiding system based on extension matter-element model: a case study in tunnel curves

Objective The visual guiding system, as a tunnel traffic safety improvement method by using visual guiding facilities to actively guide driving safely, has been widely used in countries with many tunnels, in recent years. This paper aims to quantitatively study the comprehensive evaluation of traffic safety of the visual guiding system in tunnels, which has certain engineering application value and can provide support for the quantitative evaluation and optimal design of tunnel traffic safety. Methods Based on the analysis of the relevant factors of urban tunnel traffic safety, a multi-factor comprehensive evaluation system with 5 upper-level indicators and 12 basic-level indicators was proposed. Considering the independent and incompatible indicators, a comprehensive evaluation method of traffic safety of the visual guiding system in urban tunnels was constructed by using the extension matter-element model. Taking the scene of 4 types of tunnel curves, such as no facilities, horizontal stripe, chevron alignment sign, and LED arch, as examples, the comprehensive evaluation of various schemes were carried out by using simulation tests. Results The traffic safety comprehensive evaluation system of visual guiding system in urban tunnels can be analyzed from five aspects: perception reaction, guidance ability, driver factor, driving task, and facility appearance. The results demonstrated significant the comprehensive evaluation result of the target level of scene 1 was L4, scene 2 was L3, scene 3 was L2, and scene 4 was L1. That is, the final results of the comprehensive evaluation of the four scenes were poor, medium, good, and very good, respectively. Conclusions In the scheme of visual guiding system for urban tunnel curves, the effectiveness of the three types of designs, from high to low, was the LED arch, chevron alignment sign, and horizontal stripe, and the safety of the scene without facilities was the lowest. Hence, setting the LED arch in the urban tunnel curve has a good effect in the aspects of guidance ability, sight distance, and sight zone, and is conducive to the driver’s perception reaction and driving task.

A macroscopic safety indicator for road segment: application of entropy theory

Road traffic safety is confronted with unique challenges worldwide due to the varied road network, high population density, and the rapid increase in vehicle numbers. This necessitates an effective evaluation of traffic safety at the road segment level to improve traffic safety management. Existing safety indicators, such as crash frequency (which lacks real-time availability) and Surrogate Safety Measures (focused on micro-level interactions), fall short in quantifying the overall safety status of road segments. To overcome these limitations, we introduce the concept of entropy, typically used to measure disorder in complex systems, into road traffic safety assessment. Our research presents a novel macroscopic safety indicator named ‘velocity entropy,’ along with its calculation method, which involves discretization and the Parzen window technique. The efficacy of velocity entropy is validated using the UTE dataset, a high-fidelity trajectory dataset from China, showing a more significant correlation with traffic conflicts than other macroscopic traffic properties. Notably, velocity entropy can track the temporal and spatial evolution of traffic safety, providing insights into the dynamic development of traffic risks. Overall, velocity entropy offers an innovative, practical, and theoretically meaningful tool for assessing and predicting road safety.

A New Surrogate Safety Measure Considering Temporal–Spatial Proximity and Severity of Potential Collisions

Accurate identification and analysis of traffic conflicts through surrogate safety measures (SSMs) are crucial for safety evaluation in road systems. Existing SSMs for conflict identification and analysis mostly consider the temporal–spatial proximity of conflicts without taking into account the severity of potential collisions. This makes SSMs unsuitable for traffic safety evaluation in complex road environments. In order to address the shortcomings above, this study first introduces a new SSM called the Potential Conflict Risk Index (PCRI). To validate the effectiveness of PCRI, the inD dataset is adopted for conflict identification comparison between time-to-collision (TTC) and PCRI. Using PCRI, this study conducts a conflict analysis in the freeway merging areas based on the data from the Outer Ring Expressway Dataset (ORED), accounting for differences between cars and trucks. The comparative results between TTC and PCRI show that PCRI can provide a more comprehensive identification of conflicts and a more accurate identification of the moment with the highest conflict risk. The results of conflict analysis suggest that conflicts occur more frequently in situations involving trucks, and these conflicts commonly occur in closer proximity to the on-ramp at freeway merging areas. The findings from this study can improve the accuracy of conflict identification under different conflict patterns, enhancing the specificity of traffic safety measures and ultimately ensuring the safety of road systems.

A traffic dynamic operation risk assessment method using driving behaviors and traffic flow Data: An empirical analysis

Presently, traffic safety analysis is mainly based on accident-based indicators, but they are incidental and small probability events, making safety analysis based on accident-related data difficult to achieve a comprehensive application in network size, and to realize risk assessment before accidents occur. To this end, this study innovatively develops a metric named Traffic Dynamic Operation Risk (TDOR) based on aggressive driving behaviors (ADBs) and traffic flow data for traffic safety evaluation. And Non-Negative Matrix Factorization (NMF) is adopted to explore the quantitative risk under the coupling of multiple risk factors. The results show that: 1) segments with high TDOR are always concentrated in a certain area and in a spatial distribution of Gaussian, and the peaks of these distributions are almost the assesses of transportation hubs or complex intersections; 2) traffic volume and coefficient of speed variation are the significant risk factors at ordinary sections and branches, while traffic volume and speed difference between upstream are the largest contributions at intersections; 3) roads with more accidents are associated with a higher TDOR, but the relationship between the two is not completely linearly correlated. The experimental results show that it is reasonable and feasible to use ADBs and traffic flow parameters to quantify traffic safety risks. Compared with other matrix factorization methods, the importance of variables based on NMF is more interpretable and more consistent with the change pattern of risk variables. The method proposed in this paper can change the previous traffic risk assessment pattern based on accident-related indicators and provide a basis for pre-accident risk prevention.

A big data analytics method for the evaluation of maritime traffic safety using automatic identification system data

The complex traffic situations are among the factors influencing maritime safety. They can be quantitatively estimated through the analysis of traffic data. This paper explores the impact of complex traffic situations on maritime safety, focusing on inland waterway traffic. It presents a big data analytics method, utilizing data from the Automatic Identification System (AIS) and historical maritime accident records. The methodology involves AIS data preprocessing and spatial autocorrelation models, including Moran's index, to extract and evaluate the dynamic characteristics of maritime traffic. The analysis of traffic characteristic includes a thorough investigation into the spatial-temporal distribution of ship average speed and trajectory density. The paper then introduces an effective traffic characteristic analysis model that evaluates the relationship between maritime traffic patterns and accidents. The study, specifically targeting the Nanjing section of the Yangtze River, reveals variations in ship trajectory density and average speed over time. It identifies several hotspots with a significant local correlation between these factors. Moreover, a substantial correlation is found between the locations of maritime accidents and areas with increased ship trajectory density and average speed. These results may provide insights for traffic safety management and highlight strategies for preventing maritime accidents.

Modeling of mixed traffic flow and safety evaluation in the upstream area of the bus station

The mixed traffic flow of various types of vehicles at bus station is severe, which has an impact on the overall safety of traffic. Especially upstream of bus station, since buses need to change lane in order to stop at the bus station, it interacts with social vehicles. Thus, in this paper, a two-lane CA model considering the mixed traffic flow composing of buses, cars and nonmotorized vehicles upstream of a bus station is proposed. Through several field surveys, the movement characteristics of the vehicles are obtained and the basic parameters are calibrated. For comparison, five cases about bus stops are studied. Several indexes (i.e. parallel time, blind area dwell time, time to collision, potential collision energy) are analyzed to assess the safety upstream of the bus station. Four safety indices are used to examine, based on simulation findings, the impact of bus station on vehicle safety and the significance of establishing exclusive nonmotorized vehicle lanes and exclusive bus lanes. Finally, several safety-related recommendations for drivers and management are provided. These findings offer a theoretical basis for driver management and optimal design of bus stations.

Comprehensive Safety Index for Road Safety Management System

A safety-index-based road safety management system (RSMS) is a tool to help identify locations where safety intervention is needed. To date, various safety indices have been developed and utilized, but it is rare to consider the plan–do–check–act structure in an RSMS when studying the decision-making methodology. In this study, 36 indicators and a system of evaluation indicators were selected based on the major classifications of performance, effect, and improvement. Performance was categorized by safety system components and effect was reflected in the safety status, such as the number of injuries. The indicators were validated, and a classification methodology for safety groups was proposed through cluster analysis. It was found that there was no correlation between the indicators and the population, budget, or road area by administrative district. It was also found that no particular indicators had a significant impact on the overall result in the major category or the overall index. It was determined that the developed indicators were suitable for administrative district-specific safety monitoring. It is expected that these indicators will be continuously utilized and enhanced in the national evaluation of road traffic safety.

Surrogate safety evaluation of curve sections on an interurban highway under heterogeneous traffic conditions in India

<p class="H1">Road traffic safety is a global concern. Conventionally, road safety evaluation is carried out by analyzing historical crash data, which is a reactive approach to safety analysis. The safety analyst has to wait for sufficient time to accumulate crash data before taking up any safety analysis as a sample of substantial crashes is required for analysis. But in safety analysis with a proactive approach no crash data is required, which is replaced with surrogate “conflicts”, which can be obtained from the new techniques of traffic conflict technique and surrogate safety parameters. Other approaches can be applied in traffic safety evaluation in anticipation of the crash occurrence. The advent of traffic conflict techniques, i.e., use of traffic conflicts in place of crashes and microsimulation methods like modeling of the traffic flow and pattern in traffic streams on a road network, started to apply as a method of analyzing microscopic simulation models and traffic conflict techniques to determine the safety issues in traffic systems and correlate them to the probable incidences of collisions. In this regard, surrogate safety parameters have been used to determine the level of safety on the typical curve sections of an interurban highway namely, Faridabad-Gurgaon four-lane divided highway in Haryana, India. This is accomplished by the use of vehicle trajectory data extracted through microscopic simulation in Verkher in Staedten Simulation (VISSIM) and analysis in Surrogate Safety Assessment Model (SSAM) for the above-referred corridor. Further, efforts have been made to present the intensity of traffic conflicts happening at the curved sections. The surrogate safety measures time to collision (TTC), deceleration rate (DR), and change in velocity (<em>ΔV</em>), as well as conflicting vehicle speed (Max <em>S</em>), are obtained by analysis from the SSAM model for all the curve sections and are validated using the reported crash data on the curve sections of the candidate corridor for 3 years. With the help of statistical elaboration, the critical threshold for TTC with the heterogeneous traffic movement is found 1.6 s, meaning any conflict occurring less than this time would invariably lead to a fatal crash. Similarly, the critical deceleration rate is observed as 0.569 m/s<sup>2</sup>, implying that any conflict with more than this value may lead to a fatal crash. Further, the Delta<em>V</em> values deduced for the study corridor on interurban curve sections catering to heterogeneous traffic movement is 4.1 m/s. Again, any conflict more than this value can turn into a potential crash.</p>

Traffic Performance of Successive Freeway Merge Segments

At freeway interchanges with high entering flows, implementing two successive merges can improve traffic flow and capacity by distributing the merging maneuvers. The German Guidelines for the Design of Motorways define different types of successive (“double”) merges. According to the quality of service assessment procedure for freeway merge segments given in the German Highway Capacity Manual, successive merge segments can be evaluated separately. However, depending on the distance between the merges, the capacity of the second (downstream) merge might be affected by the entering traffic at the first (upstream) merge, which influences the lane-flow distribution. For a more precise evaluation of traffic flow performance and traffic safety depending on the geometric design characteristics, vehicle interactions, lane changes, the resulting capacity of successive merge segments, accident rates, and crash types were analyzed in the study. The research was based on loop detector data as well as trajectory data obtained from video measurements, which provide an insight into the interactions between mainline and entering vehicles. For the analysis of traffic safety, crash data were evaluated over a period of 3 years. Furthermore, successive merge segments with different geometric and traffic characteristics were modeled with the microscopic traffic simulation tool BABSIM to analyze the influence on traffic flow and capacity. As a result, capacity models and recommendations for the geometric design of successive freeway merge segments are provided.

Development of an Urban Traffic Safety Evaluation System Based on the Matter Element Method

The urban road traffic safety situation significantly influences the city’s development pace and residents’ lives. Therefore, focusing on urban roads as the research subject leads to two types of hazards as secondary indicators. The first type of hazard, being the energy carrier, determines the severity of accident consequences, while the second type determines the probability of accidents occurring. By selecting 12 corresponding three-level indicators for hazard assessment and establishing a matter element model, an example analysis was conducted. MATLAB was employed along with AHP-entropy weight method to determine weight values and evaluate levels using the matter element model. Improvement suggestions are also proposed.”

Accuracy comparison of mobile mapping system for road inventory

Mobile Mapping Systems (MMSs) stand out as the preferred solution for achieving highly precise 3D environmental models, particularly in urban planning, highway mapping, asset inventory, corridor mapping, traffic safety evaluation, autonomous vehicle, digital twin, and emergency response mapping where traditional aerial or satellite surveys often fall short in providing precise data. Understanding the intricate factors that impact the accuracy of mobile mapping is pivotal to harnessing the full potential of this advanced measurement technique. This study analyzes the spatial accuracy of geographical data produced by the mobile mapping method, considering factors such as the speed of the mobile mapping tool, measurement time difference, camera shooting distance of the produced data, and differences in picture shooting distances. The acquired results were examined for their applicability in the production of inventory along the highway route, revealing their practical usability through analysis and findings. This investigation delves into the proficiency and precision benchmarks of mobile mapping systems, specifically in the context of creating road inventory and supporting decision-making for road systems. The study discusses the usability and accuracy criteria of mobile mapping systems for creating transportation inventory and decision support systems.

Application of ontology/conceptual modelling in the context of risk identification at level crossings

This conference paper discusses a new analytical approach to the evaluation of traffic safety at the railway crossings. The new method is based on the use of ontological principles. The ontological tools present a comprehensive decision-making tool, will can be applied to any potential hazardous railway crossing on a road or rail network. The ontology can be considered as a knowledge base that not only stores the data itself, but also defines the relationships between these data, and can therefore be described as a clearly suitable tool for describing risks at level crossings.

Bayesian-Based Traffic Safety Evaluation Study for Driverless Infiltration

Although driverless technology belongs to the frontier of science and technology, there is no sufficient actual data. From the lack of a comprehensive systematic evaluation method of traffic safety under driverless penetration, considering the impact of the three core systems of driverless perception, decision-making, control, and the complex road factors on the safety of driving, we review the main risk causal factors through the analysis of the accident causal model STAMP and put forward the fusion of the Leaky Noisy-OR Gate and Bayesian network model. The Bayesian network professional analysis tool GeNIe 2.0 was used to simulate, analyze, and evaluate the driverless traffic risk Bayesian network model, which accurately assessed the traffic safety risk under driverless penetration and diagnosed and identified the sensitive risk factors. The results of this study concluded that, in order to effectively deal with the future traffic safety risks of driverless vehicles, vehicle enterprises, research institutions, software and hardware suppliers in the field of driverless driving should strengthen the research and development and manufacturing of key components such as perception, and enhance the depth of research and development of AI decision-making software, which provides a new way of thinking about the management of the safety risk of driverless traffic and a theoretical basis for the development and implementation of risk control measures.

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

The present study proposed a real-time traffic safety evaluation framework using macroscopic flow variables. To this end, open-access extended vehicle trajectories were employed. Rear-end traffic conflicts and macroscopic traffic flow variables were derived from the trajectory data and were integrated for real-time safety evaluation. The Proportion of Stopping distance ( PSD) accounts for all types of interactions (both safe and unsafe) in the traffic stream; therefore, the same was adopted to analyze the rear-end traffic conflicts. A macroscopic indicator termed “time spent in conflict ( TSC)” was derived to evaluate the rear-end traffic conflicts. Machine learning models, namely, Random Forest (RF), Support Vector Machines (SVM), and eXtreme Gradient Boosting (XGB), were employed to predict TSCs using macroscopic traffic flow variables. The results revealed that the TSC computed based on PSD exhibits a reliable and explainable relationship with the macroscopic traffic flow variables. TSC computed based on PSD revealed that intermediately congested traffic flow conditions are critical in traffic safety and can be attributed to complex traffic phenomena such as traffic hysteresis, traffic oscillations, and increased speed variance. Moreover, a stable relation between traffic safety and traffic flow was suggested for varying threshold values. Among different machine learning models, the RF model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. TSC quantifies the safety status of a given traffic flow condition, where a higher value of TSC for a particular traffic flow condition indicates that vehicles prevail in the conflicting scenario for a longer time and, therefore, reflect higher operational risk. The developed machine learning model can be employed to predict TSC (operational risk) in real time using the macroscopic traffic flow variables and, therefore, facilitate traffic safety monitoring.

Traffic safety evaluation of mixed traffic flow considering the maximum platoon size of connected automated vehicles

In this paper, we investigate the influence of the maximum platoon size of connected automated vehicles (CAVs) on the traffic safety of traffic flow mixed with human-driven vehicles (HDVs) and CAVs. First, the spatial distributions of vehicles within the mixed traffic flow are analyzed. On this basis, the car-following characteristics of different vehicles are obtained. Second, the distribution probability of the four car-following modes in mixed traffic flow is derived based on the maximum platoon size of CAVs. Then, the intelligent driver model (IDM), gap-regulating model, cooperative adaptive cruise control (CACC) model, and adaptive cruise control (ACC) model are used to capture the four different car-following modes, respectively. Finally, multiple safety measures, such as the coefficient of variation of the vehicle speed, time exposed time-to-collision (TET), and time-integrated time-to-collision (TIT), are adopted to evaluate the safety of mixed traffic flow. Moreover, the impact of platoon size on the safety of mixed traffic flows is evaluated based on a numerical simulation. The results demonstrate that the magnitude of the impact of the platoon size of CAVs on the safety of mixed traffic flow is related to the penetration rate of CAVs.

Research on urban road traffic safety evaluation based on the combined weight topsis method

In this paper, an index evaluation system on traffic safety is constructed based on accident indicators and hidden danger indicators, combined with the AHP-EMW combined weight method for index weight analysis, which effectively solves the influence of subjective extremum deviation on weight accuracy, and uses the Topsis method to conduct traffic safety evaluation on 10 urban roads, conducts case verification, and proposes suggestions and measures to improve urban road traffic safety according to the evaluation results, providing a certain reference for the research of urban road traffic safety.

IMPROVING THE METHODOLOGY OF EXPERT SAFETY LEVEL ASSESSMENT OF ROAD TRAFFIC

The article is devoted to the improvement of the method of expert safety level assessment of road traffic. A general analysis of road safety assessment methods and techniques was performed. Proposed ways to improve the methodology of expert assessment of the level of road safety based on the use of a combination of statistical and proactive models. The object of research in the article is the process of assessing the level of road traffic safety in a specific area at a certain time. The purpose of the study is to improve the methodology of expert assessment of the level of road safety based on the analysis of foreign and domestic experience. The article provides a general analysis of road safety assessment methods and techniques. It has been established that the indicators of most of the developed methods and techniques for assessing the level of security are based on one of two methodological approaches - the use of statistical and reactive methods and models or proactive and prognostic methods and models. However, it was determined that the analyzed methods and techniques mostly do not establish the influence of external conditions, in particular, variable road factors when assessing the level of road safety. The author's approach is proposed - a method of expert evaluation of road traffic safety, which is based on the use of a combination of statistical and proactive models and takes into account the importance and priority of one or another means of passive anti-accident protection of road traffic. An algorithm for evaluating road traffic safety according to the developed methodology is proposed. The developed method of expert assessment of the level of road safety allows you to make informed management decisions regarding projects and programs on road safety due to the consideration of justified factors of influence and a relative reduction in the time for conducting an examination. It is proposed to combine the use of the developed methodology with the analysis of the established level of road traffic safety using the " bow-tie " or "petal" approach.

Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

Surrogate safety measures (SSMs) are critical tools for evaluating the safety performance of mixed traffic. Crashes are rare events, and historical crash data is scarce for mixed traffic that includes autonomous and/ or connected vehicles. Recent safety review papers focus on traditional human-driven vehicles (TVs) and do not encompass advanced technology vehicles such as Autonomous Vehicles (AVs), Connected Vehicles (CVs), and Connected-Autonomous Vehicles (CAVs). This study examines the development, implementation, and shortcomings of SSMs, and SSM-based models used for mixed traffic safety evaluation. It reviews the current relevant literature and applies a case study analysis using a real-world mixed traffic dataset. The study summarizes the fundamental SSM guiding concepts, as well as their most significant metrics including threshold values employed in the past for SSMs and SSM-based models. Primary benefits and limitations of examined SSMs and SSM-based models are also underlined. This review reveals significant gaps in the literature that might guide future research paths in SSM-based mixed traffic safety assessment. Critical gaps include the absence of robust SSM threshold selection criteria, the suitability of current SSMs in mixed traffic research, microsimulation modeling that lacks proper calibration and validation, and the absence of a framework for selecting or combining multiple SSMs.