Traffic Safety Research Articles

A seafarers’ cognitive error mining model based on IDA and TRACEr and mitigation measures for ship collision accidents

Human factor is the main cause of maritime accidents. However, the in-depth study on the mechanism of cognitive errors in seafarers’ human error needs to be supplemented. This study empirically analyzes 116 collision cargo ships along the Yangtze River trunk line. To explore the relationship between seafarers’ task errors and the cognitive process of accidents, a seafarers’ cognitive error mining model is proposed, which integrates the Information, Decision, and Action (IDA) cognitive model into the Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr). According to the approach of mining from the outside to the inside, firstly, the error mode in the cognitive stage of seafarer tasks are identified. Then the fine-grained main manifestation of the task errors is mined by K-means, and Apriori algorithm is employed to mine the hidden features between the external and internal error modes. The results show that there are four main problems of Yangtze River ship accidents, and seafarers have four to seven corresponding causal chains from psychological error mechanism to performance shaping factors for each major issue in the cognitive stage. On the basis of the results, this study summarizes the guiding principles of accident prevention and seafarer management measures to provide decision support for water traffic safety.

Evaluation of Flexible Pavement Friction Coefficients: A Case Study of East-West Highway Near Mahendranagar City, Nepal

Friction is a crucial factor in highway engineering, as it affects the traction between a vehicle's wheels and the road surface. This traction significantly influences the movement, speed, and efficiency of vehicles on highways. The coefficient of friction depends on various factors, such as pavement materials, traffic volume, road age, temperature, and weather conditions. The Skid Resistance Test Method is adopted for the study, which involves using a Portable Skid Resistance Tester to measure the British Pendulum Number (BPN) and then calculating the coefficient of friction by dividing the BPN by 10. An evaluation of a 5 km stretch of the East-West highway near Mahendranagar Bazaar revealed a longitudinal coefficient of friction of 0.354 and a lateral coefficient of friction of 0.184 under both dry and wet conditions. These findings were relevant for design speeds ranging from 60 to 80 km/h and considered an Annual Average Daily Traffic (AADT) of 10,321 Passenger Car Units (PCU) during the year 2023/2024. The assessment also indicated a variance of 0.19% and a poor International Roughness Index (IRI) value of 2.05. These aggregates had specific gravities ranging from 2.6 to 2.67 and were sized at 13.2 mm. The application rate for the aggregates ranged from 12 to 15 kg per square meter. It is important to note that a higher coefficient of friction leads to reduced vehicle efficiency. Thus, it is crucial to carefully consider the coefficient of friction values and detailed specifications to ensure traffic safety and road integrity.

Gaussian Process Regression as a precrash velocity determination method–subcompact vehicle class

The following paper presents an innovative approach to determining vehicle precrash velocity when hitting an immovable obstacle facing forward. Precrash velocity is necessary in order to perform a crash reconstruction. It is needed for the time-space analysis of the events, as well as to assesscrash mitigation and to evaluate drivers’ technique and tactics. For this task, the authors are using Gaussian Process Regression (GPR). Such an approach offers a number of advantages over the currently used methods that prove to be outdated when considering modern vehicles. The mathematical model was trained on a database shared by the National Highway Traffic Safety Administration.. This database covers a large number of crash tests of different kind, however authors focus on frontal collisions of the subcompact car class. Due to low accuracy of linear methods used up till now, Authors developed an innovative approach to determine the EES parameter utilizing Gaussian process regression. The newly developed method is an effective and accurate way to determine the vehicle’s velocity and shows promising results, as is demonstrated in this paper.

An example of how to improve safety at selected pedestrian crossings

Road safety continues to be a critical issue, especially for vulnerable road users such as pedestrians walking across the road at a place designated for the purpose. The most effective method of safety improvement at pedestrian crossings is to install traffic lights to control traffic. Such a solution cannot be used anywhere, however, due to the risk of the lack of flow capacity, especially in the area of nearby junctions. Therefore, persons responsible for traffic safety look for alternatives to improve the situation at pedestrian crossings. To meet the expectations of those persons, this article proposes mechatronic systems which are worth installing at selected pedestrian crossings to improve safety and reduce the number of traffic incidents involving vulnerable people. Three pedestrian crossings, considered dangerous by the authors, and located in the town of Nowy Sącz, were taken for analysis. Among the safety features, the use of an active sign informing of a pedestrian crossing, reflective elements and LED lights embedded in asphalt, as well as active pedestrian crossing illumination, are suggested. The proposed solutions are distinguished by low operating costs, as energy needed to power those solutions is obtained from photovoltaic panels.

Factors Affecting Rear-End Collisions in Underground Road Junctions Using VISSIM

Due to urban overcrowding, available land is limited and traffic congestion has increased. Underground roads are being built to mitigate traffic congestion as an alternative. Studies associated with underground roads are needed because these roads are dark and closed and have a high risk of accidents compared to surface roads. In particular, there is limited study on junctions that connect two or more underground roads. In this study, an underground road network including junctions was constructed to analyze the factors behind rear-end collisions at underground road connections. To reflect the driving behavior on underground roads, the scenario analysis was conducted by applying the speed distribution of underground roads in Korea. The results of the analysis showed that variables such as acceleration standard deviation and lateral position standard deviation are crucial for accidents on underground roads. Thus, this study can be used as a basis for traffic management and safety improvement in the operation of underground road junctions in the future.

Determinants influencing alcohol-related two-vehicle crash severity: A multivariate Bayesian hierarchical random parameters correlated outcomes logit model

Alcohol-related driving remains a significant concern due to its profound association with the likelihood of traffic crashes and the severity of resulting injuries, especially between two vehicles. To investigate the determinants influencing the alcohol-related two-vehicle crash severity, a foundational framework employed was a multinomial logit model. Meanwhile, by incorporating random intercept from individual case and vehicle levels to accommodate unobserved heterogeneity, and covariance matrices to underscore correlated outcomes, a multivariate hierarchical random parameters correlated outcomes logit model was proposed. Additionally, to further explore the potential temporal instability of explanatory variables, a random slope from a per-year indicator was introduced into the model. Crash data from the US Statewide Integrated Traffic Records System (SWITRS) database spanning from January 1, 2016, to December 31, 2021, was used. Three crash injury severity categories were examined, encompassing severe injury, minor injury, and no injury, with characteristics related to the driver, vehicle, road, environment, crash, and time serving as explanatory variables. The model results highlighted significant heterogeneity, with each case and vehicle accounting for 56.9% of the total variance for minor injuries and 50.8% for severe injuries. Furthermore, a significant negative correlation was explicitly exhibited between minor injury and severe injury outcomes at the case level. In terms of potential temporal instability, we provided per-year (2016–2019) parameter estimates and identified significant instability for indicators such as non-intersection, broadside and head-on collisions, cloudy weather conditions, and drivers who had been drinking but were not under the influence. Considering the impact of the COVID-19 pandemic, we divided the accident time into pre-COVID and during-COVID periods, modeling parameter estimates for both periods. This analysis revealed significant instability in several factors influenced by the pandemic. Additionally, noteworthy disparities in the estimated results of explanatory variables emerged in comparison to those general two-vehicle crashes or alcohol-related crashes, providing valuable insights. For instance, drivers who had been drinking but were not under the influence were less likely to sustain severe injuries, but the probability of minor injuries increased. These findings underscore the significance of thorough investigations into the determinants of injury severity in alcohol-impaired two-vehicle crash severity, along with the temporal instability of such factors. They hold important implications for effective traffic safety management and the formulation of prohibitive countermeasures.

Enhancing Security and Performance in Vehicular Adhoc Networks: A Machine Learning Approach to Combat Adversarial Attacks

Integrating Machine Learning (ML) techniques into Vehicular Adhoc Networks (VANETs) provides promising features in autonomous driving and ITS applications. In this paper, DSRC data is used to evaluate the effectiveness of different ML models, including Naive Bayes, Random Forest, KNN, and Gradient Boosting, in normal and adversarial scenarios. Since the dataset is relatively imbalanced, the Synthetic Minority Over-sampling Technique (SMOTE) is employed for sampling, and defensive distillation for improving model resilience to adversarial perturbations. From the results, it is clear that models such as Gradient Boosting and Random Forest show high accuracy in both cases, thus showing the potential of using Machine Learning to improve VANET security and reliability when new threats appear. Through this research, the significance of the application of ML in the protection of vehicular communication in order to enhance both traffic safety and flow has been articulated.

A multi-objective method for large multi-lane roundabout design through microscopic traffic simulation and SSAM analysis

Urban traffic congestion poses a significant challenge, impacting both economic efficiency and environmental sustainability. Roundabouts, recognized for their ability to improve traffic flow and reduce collisions, have become a focal point in traffic management strategies in urban areas. Different types and sizes of single and multi-lane roundabouts can be used, including conventional and innovative roundabouts with small, medium or large size diameters. Many optimization models are available for designing conventional roundabouts but not large roundabouts. To partially cover this gap, the present paper introduces a novel approach integrating simulation-based analysis with a goal programming method to optimize large roundabout design. By leveraging advanced traffic simulation tools like AIMSUN Next and Surrogate Safety Assessment Models (SSAMs), this methodology evaluates multiple design scenarios to enhance traffic flow, safety, and environmental performance. A case study of a complex large roundabout in the Italian greenest and sustainable city demonstrates the efficacy of this methodological approach. The findings provide valuable insights for traffic and highway engineers, emphasizing a data-driven, holistic approach to sustainable traffic management and roundabout optimization.

A comparison between bridge-related and non-bridge crashes: severity, vehicle involvement, and motorists’ characteristics

Objectives Bridges, though representing a small portion of transport networks by length, are still prone to traffic crashes. Despite extensive research on bridge-related crashes globally, there’s a scarcity of studies exploring differences between bridge- and non-bridge-related crashes. Thus, this paper attempts to add to the current knowledge by comparing bridge-related crashes and non-bridge crashes using a case study in Victoria, Australia. Methods By utilizing crash data in Victoria between January 2006 and April 2023; the Chi-squared test is conducted to examine differences between crash types. Partial proportion odds models are then employed to establish if bridge-related crashes tend to be more severe. Results In general, the analysis reveals that bridge-related crashes tend to result in more severe injury outcomes compared to non-bridge crashes. Furthermore, there is evidence that bridge-related crashes are more likely to be single-vehicle, to involve a heavy vehicle as well as young/male motorists, and to occur on high-speed roads, regional areas and at nighttime than non-bridge crashes. Conclusions A targeted program aiming at enhancing traffic safety at bridges is recommended due to the higher severity of bridge-related crashes. Considering the presence of bridges on the road network, and particularly in regional areas where many are in poorer conditions, it is essential to consider retrofitting as well as low-cost solutions in order to complement motorist education and awareness programs. Specific groups with higher risks of being involved in bridge-related crashes, such as young male motorists, should be targeted in these programs.

Effects of uncertain anomalous information on traffic flow of automated vehicles with V2V communication

Automated vehicles (AVs) equipped with vehicle-to-vehicle (V2V) communication can operate by sensing real-time status information through onboard sensors and wireless connections. Nevertheless, under the influence of multifarious random factors in real traffic, this critical information that support the normal movement of such vehicles may be anomalous, raising concerns on their mobility and traffic security. Due to the lack of appropriate analytical model, previous studies have not comprehensively uncovered the effects of uncertain anomalous information on traffic flow of AVs with V2V communication. Therefore, this study aims to bridge this critical gap. Firstly, by introducing a probabilistic parameter (i.e., information anomaly probability), we propose a general model that integrates the normal and compromised models, thereby capturing the longitudinal dynamics of AVs featuring V2V communication in the presence of uncertain anomalous information. To enable the detailed theoretical and experimental analyses, we specify it through the cooperative adaptive cruise control model calibrated with real-car data. Subsequently, we define the concept of pseudo string stability and parameterize the stability condition based on the characteristic equation method, so as to demonstrate the relationship between traffic flow stability and the parameters and probability of information anomaly. Finally, we refine the proposed probabilistic model and conduct extensive numerical experiments. The findings show that uncertain anomalous information could result in sudden or even frequent acceleration and deceleration of AVs, causing traffic oscillation, reduced traffic efficiency, and even collision accidents. In particular, the greater the information anomaly probability, the larger the disturbances experienced by traffic flow. Meanwhile, at the same level of anomaly, the combined impacts of various anomalous information could lead to more severe consequences than the singular impact of any individual anomalous information. Furthermore, the duration of anomalous information directly affects the time it takes for traffic flow to return to normal.

Correlates of Exercise Behavior Based on Socio-Ecological Theoretical Model among Chinese Urban Adults: An Empirical Study.

Identifying the correlates of exercise behavior is essential to combating physical inactivity as a public health concern. The purpose of this study is to identify the correlates of physical activity among urban Chinese adults under the social-ecological theoretical model in order to facilitate targeted interventions to promote physical activity. Using the socio-ecological model, we conducted a questionnaire survey among 1459 urban residents in Zhejiang and Shaanxi provinces of China, collecting data on individual demographic factors, sociological factors, environmental perception, and exercise behavior. Binary logistic regression models were employed to analyze the relationships between exercise behavior and socio-ecological factors. Male gender (p < 0.01), advanced age (p < 0.001), higher education level (p < 0.05), living independently from parents (p < 0.05), absence of childcare responsibilities (p < 0.01), residence in a county/prefecture-level city (p < 0.001), favorable neighborhood esthetics (p < 0.001), available greenways/parks (p < 0.001), and family support for exercise participation (p < 0.05) were significantly correlated with an increased likelihood of participating in physical activity. Male gender (p < 0.001), advanced age (p < 0.001), absence of childcare responsibilities (p < 0.05), good neighborhood vegetation (p < 0.01), availability of free neighborhood exercise facilities (p < 0.05), and support from friends for exercise participation (p < 0.01) were significantly correlated with an increased likelihood of engaging in physical activity for more than 150 min per week. BMI, community air quality, traffic safety, public safety, and level of social development were not major correlates. To promote exercise behavior, more attention should be paid to individuals who are female, young, have lower levels of education, bear childcare responsibilities, or reside in provincial capitals in China. Improving the habitat environment and providing convenient and affordable facilities should also be considered. Furthermore, support from family and friends can positively reinforce exercise behavior.

Roundabout Safety Benefits, Design Considerations and Safety Evaluation Techniques

Roundabouts are increasingly replacing conventional signalized intersections due to their potential to enhance traffic safety and operational efficiency. This paper examines and reviews the safety benefits, design considerations, and evaluation methods for roundabouts. Studies consistently show significant reductions in crashes at roundabouts relative to other at-grade intersection control systems, particularly severe injury crashes, with reductions ranging from 37% to 88%. Roundabouts reduce conflict points, lower vehicle speeds, and improve safety for both motorized and vulnerable road users. The methodology for this paper involved a comprehensive review of literature on roundabout design and safety evaluation techniques. Data from global studies were analyzed to identify key design elements such as geometric layout, signage, and pedestrian provisions, which contribute to roundabout safety. Various safety evaluation methods, including crash data analysis, traffic conflict techniques, and surrogate safety measures, were also assessed for their effectiveness in evaluating roundabout performance. Findings indicate that proper geometric design, clear signage, and the use of innovative features like right-turn bypass lanes and roundabout metering systems can enhance safety and traffic flow. Based on these findings, the paper recommends integrating these design elements, prioritizing the safety of vulnerable users, and utilizing advanced safety evaluation techniques, including simulation modelling and intelligent transportation systems (ITS) to further optimize roundabout performance.

Revolutionizing Urban Mobility: Smart Transportation &amp; Vehicle-to-Infrastructure Communication – A Review

The burgeoning popularity of IoT technology has significantly altered transportation, with an extensive variety of repercussions. Intelligent Transportation Systems (ITS) are at the vanguard of this change, which has the potential to fundamentally change urban transportation for both individuals and businesses. ITS has expanded considerably in recent years, owing to global urbanization and industrialization advancements. It is regarded as a game-changing technology that improves road safety, improves traffic flow, and improves the entire driving experience. Despite many research efforts on ITS applications and simulation platforms, formal semantic descriptions for intelligent transportation systems have been noticeably lacking. Rising rates of accidents suggest the necessity for a robust Smart Transport System (STS) that prioritizes improving mobility, and reliability, as well as reducing carbon dioxide and methane emissions from automobiles beyond dispersing gains throughout everywhere. Multiple traffic signal management approaches have been designed to enhance real-time traffic movement at intersections, but none have achieved seamless, congestion-free traffic movements. The creation of an interconnected unified framework that integrates and links vehicles with VANET, IoT, and AI technologies will significantly affect the establishment of an innovative reliable mobility network. Internet of Vehicles (IoV) systems will connect several automobiles to share vital information through an IoT-enabled network. Vehicle-to-infrastructure (V2I) communication is essential in developing intelligent transportation and rail traffic management systems, significantly improving road traffic safety and efficiency. A V2I system integrates transmission and analysis capabilities into vehicles and corresponding infrastructure. Wireless sensors are strategically positioned along highways or driving zones in this system. The significance of safe and effective V2X communication between automobiles and facilities expands substantially as intelligent modes of transport progress.

Investigating the Interrelationships among Factors Associated with Automated Vehicle Crashes Using Additive Bayesian Network

Although automated vehicles (AVs) were considered a promising solution to enhance traffic safety by eliminating human errors, AV crashes still happen in mixed traffic consisting of human-driven vehicles and AVs. Thus, to reduce AV-involved crashes, it is necessary to understand the factors leading to AV crashes. However, traditional regression-based methods may not reveal a structured relationship among leading factors of AV crashes, which hinders the exploration of countermeasures to AV crashes. Based on the 246 AV crash records collected by the National Highway Traffic Safety Administration, this study investigated the factors associated with AV crashes. An additive Bayesian network (ABN) approach was utilized to construct the topological relationship among potential influential factors of AV crashes, followed by post-ABN regression analyses. Results show that, though AV technologies have developed rapidly in the past few years, rear-end crashes are still dominant among AV-involved crashes, potentially because of the discrepancy in the driving behaviors between AV and human-driven vehicles. The crash type of AV-involved crashes is more related to the pre-crash movements of crash partners than it is to the pre-crash movements of AVs, while crash outcomes (e.g., injury severity) are associated with the environmental factors (e.g., operating entities) and crash-procedure-related factors (e.g., crash type). Findings from this study aid in understanding AV crash patterns, which can inform targeted interventions and technology advancements to improve safety outcomes for all road users.

Prune and Quantize Semantic Segmentation Network for Aerial Objects Recognition

Semantic segmentation of aerial and satellite images is crucial for applications in environmental management, urban planning, and traffic safety. While deep learning techniques with convolutional neural networks (CNNs) and attention mechanisms have achieved superior accuracy compared to traditional methods, they often struggle with model complexity and resource constraints. This paper introduces two novel techniques - pruning and quantization - to enhance both the performance and efficiency of semantic segmentation models for remote sensing images (RSIs). Pruning reduces model complexity by eliminating less significant weights, while quantization decreases memory usage by converting weights into a more compact format. We applied these techniques to the DeepLabV3+ model with ResNet18 and ResNet50 backbones and assessed their performance across multiple RSI datasets. Our results show that pruning and quantization effectively balance accuracy and computational efficiency, achieving a mean IoU of 81.24% with a memory footprint of 135.19 MB for pruning, and 81.04% mean IoU with a memory footprint of 33.79 MB for quantization on the ISPRS Vaihingen dataset. These methods offer a viable solution for deploying semantic segmentation models on resource-constrained hardware.

Dynamics and Forecast of Road Traffic Safety Management in Russia until 2030

Dynamics and Forecast of Road Traffic Safety Management in Russia until 2030

Improving Pedestrian Safety with Head-Up Display Warning in a Connected Environment

In this paper, the potential of using a head-up display (HUD) in the connected environment to improve a vehicle’s running comfort and pedestrian safety is tested, by providing warning information to drivers in advance. To achieve this objective, driving simulation technology is used to construct the connected environment and develop the HUD, and the effectiveness of the system is then tested. Specifically, thirty-four participants were recruited to conduct driving simulation experiments in six scenarios: three warning display types (Baseline/Head-down display/Head-up display) combined with two weather conditions (clear weather/foggy weather). The effects of the three different warning display types on braking risk-avoidance strategy were studied by comparing the drivers’ performance during the perception and decision stage (position of accelerator-pedal release, position of first braking), the risk-avoidance manipulation stage (maximum deceleration, braking distance) and the risk-avoidance result stage (minimum collision distance, position of minimum speed). The influences of weather conditions and driver attributes were also considered. When the HUD warnings were activated, drivers started to decelerate further away from pedestrians, with a more stable and moderate deceleration process and a greater safety margin between the vehicle and the pedestrians. Using HUD warnings in foggy conditions improved drivers’ perception and decision abilities, this study confirmed the great benefits that HUD warnings in the connected environment can bring to traffic safety, especially under risky situations and inclement weathers. The research results provide a reference for the more humanized and rationalized optimization design of these warning systems.

Road accident risk analysis of at- and near-port areas reflecting regional heterogeneous characteristics

ABSTRACT In port cities, road traffic safety management should consider various types of cargo that will shift in the trucks and containers. This applies not only in the case of ‘at port’ areas within a 2-km radius but also ‘near-port’ areas within a 4–8-km radius. This is important for traffic safety because the crash severity can be high as there are many trucks to the ports that can cause large-scale crashes. Therefore, to prepare traffic safety management strategies for port city, it is necessary to identify crash risk factors that affect the crash severity. This study developed a two-stage road crash severity model of ‘at-port’ and ‘near-port’ regions using a support vector machine and a Bayesian multinomial model. Additionally, a severity model suitable for Korean ports was proposed that considered spatial heterogeneity in the Bayesian multilevel multinomial model. Findings derived from this study can be used for policy development to increase road traffic safety in port cities. ‘This article is a revised and expanded version of a paper entitled [Accident Risk Analysis of At and Near-Port Areas Reflecting Regional Heterogeneous Characteristics] presented at [The 11th International Conference on Logistics and Maritime Systems, Korea, 4-7 September 2023]’.

‘There is a lot of traffic at the school gate in the morning which makes me nervous’: exploring children’s current and desired school commutes

ABSTRACT Although children are the primary users of many spaces, their voices are often unheard in transport planning. We therefore do not know much about children’s barriers or wishes regarding mobility. What is known, however, is that children have become less independent in their daily mobility, with parents (or guardians) increasingly driving children instead of allowing them to walk and cycle. The decline in active mobility is worrisome, since active mobility is beneficial to children, but incorporating children’s voices in transport planning can be conducive to increased active mobility of children. The objective of this paper is to explore the wishes and barriers of children regarding school commuting. Data was collected from a survey completed by 1251 pupils and through workshops with four schools in a residential neighbourhood in Brussels, Belgium. We found that there is a significant gap between the current and desired transport modes of pupils, with an important untapped potential for cycling. We also found that traffic safety is a predominant worry of pupils. Additionally, when suggesting the re-design of their school neighbourhood, the most recurring suggestions from pupils included walking and cycling infrastructure, and reduced access to the school neighbourhood for cars.

Car-following model based on artificial potential field with consideration of horizontal curvature in connected vehicles environment

Connected vehicles (CVs) will gradually replace traditional vehicles to become the main components of traffic flow. Studying the car-following behavior characteristics is crucial for improving traffic flow stability and safety in CVs environment. Additionally, the radius of road curvature significantly impacts vehicle driving behavior, making it necessary to consider it for the car-following models of CVs. The artificial potential field (APF) theory can more accurately and comprehensively depict various microscopic driving behaviors, offering a new approach for modeling vehicle microscopic behavior. Firstly, this paper constructs the attractive and repulsive potential fields considering horizontal curve curvature based on a road coordinate transformation model. Secondly, an Artificial Potential Field-Based Car-Following Model Considering Curvature (APFCCM) in connected vehicles environment is proposed. Finally, the model is calibrated and validated using the Hangzhou - Xifu Freeway dataset from the Tongji Road Trajectory Sharing (TJRD TS) platform, and compared with the full velocity difference model(FVDM), the Intelligent Driver Model (IDM) and the Driving Risk Potential Field Model (DRPFM). The results show that the APFCCM performs well in trajectory simulation, model accuracy, and scenario adaptability, and it has the lowest mean absolute error(MAE) and root mean square error(RMSE) in position, speed, and acceleration metrics.