Road Users Research Articles

Worst case analysis, a video analysis approach to injury potential in traffic

This paper presents a method for evaluating the potential for injury in any traffic scenario involving road user interactions based on trajectory data from a few hours of data of typical traffic conditions. The basic idea was to evaluate to what extent interactions at a location are limited in speed, direction, and whether the interactions happen at a safe speed. Specifically, the method works by applying an existing injury model to the highest observed values at a specific point in space. The injury model is used to estimate a worst-case scenario at that point in space. By generating a heat map, it is possible to analyse a location and evaluate the injury potential at a location. The method does not provide a complete safety analysis with exposure or risk but instead focuses specifically on the potential consequences. The method is suitable for preliminary safety analyses that focus on determining areas of particular interest for further study.

Analysis of Surface Texture of Asphalt Mixture with Sand Circle Method

A road is usually defined as an infrastructure connecting a zone to other zones. As service and safety are essential for vehicle users, any conditions related to the pavement, including surface texture, are considered. The importance of this matter is that many researchers or practitioners are concerned with the quality of asphalt mixtures, whether structural or functional, which affect the safety and security of road users, mainly related to the texture of the pavement surface. This research aims to determine the texture depth (MTD, Mean Texture Depth). The research method used is the circle sand method, which is tested using the Sand Patch tool. The asphalt mixture type uses AC-WC with a composition of 39% coarse aggregate, 54.5% fine aggregate, and 6.5% filler. Optimum Asphalt Content of 6.28% and Density of 2.28 were obtained, and texture depth of 0.549 mm was more significant than 0.45 mm (Bina Marga 2010 revision 3). The conclusion is based on the design composition of the aggregate mix in the AC-WC asphalt mixture, which has a safe texture.

The epidemiology of road traffic injuries in the republic of Serbia: a study based on hospital data, 2015-2019

IntroductionRoad traffic injuries (RTI) are the leading cause of death and severe disability among individuals under the age of 40, posing a significant public health challenge globally. This manuscript highlights key aspects of the epidemiology of injuries in road traffic crashes (RTC) in Serbia, based on hospitalization report data.ObjectivesThe main aim of this study was to analyze the epidemiological characteristics of road traffic injuries (RTI) based on hospital data over a five-year period in Serbia.MethodsThe data for this study were obtained from the Hospitalization Report, which is part of the hospitalization database maintained by the Institute for Public Health of Serbia “Dr Milan Jovanović Batut,” covering the period from January 2015 to December 2019. The research included data from the Hospitalization Reports of 66 healthcare institutions across Serbia.ResultsDuring the study period, a total of 15,028 patients with road traffic injuries were admitted to healthcare institutions in Serbia. During the five-year period, the crude RTI incidence rate increased every year, from 39.0/100, 000 in 2015 to 43.7/100,000 in 2019. Older adult people aged 65 and over were particularly vulnerable as bicyclists and pedestrians (31.3, 27.7%, respectively). The Vojvodina region experienced a higher incidence of injuries among bicyclists compared and car accidents were most frequent in Central Serbia than in the other regions of Serbia. Craniocerebral injuries were the most common type of road traffic injury, accounting for 37.8% of cases. Significant differences in the types of injuries were observed based on age (χ2 = 649.859; p < 0.001) and gender (χ2 = 31.442; p < 0.001).ConclusionUnderstanding the epidemiological profile of road users involved in accidents is essential for monitoring and controlling specific risk factors. Our results highlight the need for enhanced traffic safety measures at the local level.

Identifying risk factors for wheelchair damage, part failure, and adverse consequences to the user

No tools or technologies exist to inform data-driven inspection schedules for wheelchairs. To develop such a schedule, this study identifies risk factors linked with manual wheelchair damage, part failures, and consequences and evaluates preferences for a new wheelchair servicing technology. A mixed methods study was performed with manual wheelchair users at The Ohio State University Martha Morehouse Clinic. Demographic data, wheelchair information, failure counts, and consequences suffered by the user were collected using surveys. Wheelchair usage was collected for a month using a sensor. A servicing smartphone app that connects with the sensor was demonstrated as a new servicing technology, and participant preferences were recorded. Thirty participants completed the survey testing procedures. Twenty-three collected usage data and eighteen collected it for over a week. At least 215 wheelchair part failures with an average of 13.4 ± 14.8 self-reported part failures and 4.7 ± 4.8 high-risk failures occurred in 12 months before the first study visit. Two weeks of collected data from 18 participants showed that normalised road shocks, age, and weight were associated with the condition of wheels and frames, as well as self-reported caster failures. Participants responded with a favourable preference for the new wheelchair servicing technology, with more than half of them interested in buying and using it. Risk factors like road shocks and user’s age and weight are associated with part damage towards failures and self-reported failures that risk injury. These factors can be modelled to develop and test the efficacy of wheelchair inspection schedules.

Euro NCAP Safety Tests: Trials Road Cars go through before they Receive a Star Rating

Road safety is an issue of major importance in European Union policies, with the main objectives of reducing/eliminating road accidents, reducing/eliminating road casualties and fatalities, and increasing the level of safety and comfort for all road users. The scientific paper presents a concrete research carried out by the authors in order to implement some technical concepts, so that those interested in this field can become acquainted with a certain way of presenting the problems of the safety tests carried out by the European organization for road safety assessment Euro NCAP, established in 1996, based in Louvain, Belgium, the first results of the tests carried out on road vehicles were published in February 1997. In this way, readers can learn about how European organizations interpret the number of stars in road vehicle safety tests, the scales of results for awarding safety stars, the evidence of Euro Ncap tests and levels of protection for adult and child road users, the protection of vulnerable road users (motorcyclists, cyclists, pedestrians) and the conditions for testing the effectiveness of safety systems fitted by manufacturers on road vehicles. It is then presented, the Euro NCAP assisted driving of road vehicles, quadricycle ratings of road vehicles, Euro NCAP tests for trucks and a case study on Euro NCAP tests for the Romanian off-road car, Dacia Duster 3rd generation. The conclusions of the scientific research are presented at the end of the paper.

ASSESSING THE FEASIBILITY OF MALANG RAYA MOSQUES IN THE RELIGIOUS MODERATION PROGRAM

<p>This article aims to assess the feasibility of Malang Raya mosques in the Religious Moderation Program through the perspective of Durkheim, Parsons, and Merton’s functionalist sociological theories. Employing a qualitative approach, the research utilizes a case study methodology across multiple sites, specifically Al-Ghozali Mosque (Islamic education institution area), Al-Mukhlisin Mosque (residential area), and Al-Barokah Mosque (housing area). The findings align with four indicators of religious moderation: First, all three mosques fulfill the National Commitment standards based on empirical data related to organic solidarity that fosters a functional society, as evidenced by the manifest and latent functions exhibited by the mosques in the context of national commitment (Mu’ahadah Wataniyyah) and adherence to laws (Dusturiyyah). Second, the mosques meet the tolerance standards through their affiliations with mass organizations, namely Nahdlatul Ulama and Muhammadiyyah, which have historically demonstrated tolerance (Tasamuh), as well as the principle of justice (‘Adliyyah) upheld through financial transparency and accountability. Third, the mosques satisfy the anti-violence standards through programs promoting care and compassion (Rahamutiyyah) for women, children, the elderly, orphans, and the impoverished, alongside the active involvement of women (Mubadalah) in mosque activities, mainly as educators and staff at Al-Qur’an Learning Centre (Taman Pendidikan Al-Qur’an or TPQ) affiliated with the mosque. Fourth, the mosques adhere to the standards for acceptance of tradition through activities that reflect local traditional values (‘Urfiyyah), such as the mosque's architectural style; humanity (Insaniyyah), exemplified by economic assistance for disaster victims; and public welfare (Maslahah), demonstrated by their concern for the rights of road users.</p>

Quantitative Estimation and Analysis of Spatiotemporal Delay Effects in Expressway Traffic Accidents

Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. The innovation lies in utilizing real-world vehicle trajectory data, combined with a Traffic Performance Index (TPI), to quantitatively assess delay impacts. By applying spatial clustering and hotspot detection techniques, we investigate the distribution patterns of delays and further employ a Spatial Error Model (SEM) to examine the relationships between accident characteristics and associated delay effects. Using expressway accident data and vehicle trajectory records from Hunan Province, the results demonstrate that the TPI-based approach effectively captures the duration, extent, and severity of traffic delays. Moreover, significant correlations are identified between delay impacts and specific accident characteristics, such as accident type, road type, road environment, pre-accident vehicle speed, and secondary accidents. This approach provides traffic management authorities with actionable insights into the overall roadway impact, facilitating targeted emergency response strategies and informing road usage policies tailored to the characteristics of accident impacts, thus helping to mitigate future risks.

Comparative Analysis of Deep Neural Networks and Graph Convolutional Networks for Road Surface Condition Prediction

Road maintenance is essential for supporting road safety and user comfort. Developing predictive models for road surface conditions enables highway agencies to optimize maintenance planning and strategies. The international roughness index (IRI) is widely used as a standard for evaluating road surface quality. This study compares the performance of deep neural networks (DNNs) and graph convolutional networks (GCNs) in predicting IRI values. A unique aspect of this research is the inclusion of additional predictor features, such as the type and timing of recent roadwork, hypothesized to affect IRI values. Findings indicate that, overall, the DNN model performs similarly to the GCN model across the entire highway network. Given the predominantly linear structure of national highways and their limited connectivity, the dataset exhibits a low beta index, ranging from 0.5 to 0.75. Additionally, gaps in IRI data collection and discontinuities in certain highway segments present challenges for modeling spatial dependencies. The performance of DNN and GCN models was assessed across the network, with results indicating that DNN outperforms GCN when highway networks are sparsely connected. This research underscores the suitability of DNN for low-connectivity networks like highways, while also highlighting the potential of GCNs in more densely connected settings.

Comparing road traffic injuries by types of road users among children and adolescents in South Korea, 2011–2021

IntroductionRoad traffic injuries (RTIs) are the leading cause of mortality among children and adolescents. This study aimed to compare clinical characteristics and identify factors associated with severe RTIs based on...

Информационно-аналитическая система детектирования движения объектов на пешеходном переходе

To manage pedestrian and vehicle flows at intersections, systems are implemented that use adaptive traffic light models, adjusting time intervals based on the volume of pedestrians and vehicles. These systems include video cameras that monitor road user movements, enhancing real-time traffic control. This paper introduces an information and analytical system for managing transport and pedestrian flows using the YOLO neural model, which enables object recognition. The system performs several operations: converting the original image to gray scale, applying Gaussian blurring, detecting object boundaries through the Canny filter and fuzzy logic for edge detection, and contour processing, where each identified contour is assigned a unique number. The neural network then compares detected contours to the training sample data, determining whether the object is a person or a vehicle. The paper presents experimental results for these algorithms in object recognition. Modified software and images of intersections with pedestrian crossings captured from street video cameras in Kursk were used in the experiments. The recognition accuracy rate from the experiments was 72.4%.

Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making.

Analysis of Children's Road Crashes in Hungary

In the EU, more than 6,000 children died in road accidents between 2011 and 2020. Children are particularly vulnerable road users, and they need to be protected. This underlines the importance of the Safe System approach. The Safe System approach is a holistic view of road safety, which integrates the different elements of the traffic system and takes human vulnerability and fallibility into account. Children are still in the phase of developing the cognitive and physical skills necessary to travel safely in traffic. Because of their small size, children are less visible than other road users and less experienced; they can easily become innocent victims in collisions. Despite significant improvements in vehicle safety in recent years, almost half of all child road deaths occur while traveling in cars. Limited data is available on the correct use of child seats in cars across the EU, but studies have shown that misuse remains a significant problem. Several measures have been taken in recent years to make it safer for children to travel on the roads, but many more interventions are needed to further improve their safety. Our research aimed to examine the characteristics of child accidents in Hungary and to highlight the main road safety problems affecting children in Hungary.

Driving Safety and Comfort Enhancement in Urban Underground Interchanges via Driving Simulation and Machine Learning

Urban transportation systems, particularly underground interchanges, present significant challenges for sustainable and resilient urban design due to their complex road geometries and dense traffic signage. These challenges are further compounded by the interaction of diverse road users, which heightens the risk of accidents. To enhance both safety and sustainability, this study integrates advanced driving simulation techniques with machine learning models to improve driving safety and comfort in underground interchanges. By utilizing a driving simulator and 3D modeling, real-world conditions were replicated to design key traffic safety features with an emphasis on sustainability and driver well-being. Critical safety parameters, including speed, acceleration, and pedal use, were analyzed alongside comfort metrics such as lateral acceleration and steering torque. The LightGBM machine learning model was used to classify safety and comfort grades with an accuracy of 97.06%. An important ranking identified entrance signage and deceleration zones as having the greatest impact on safety and comfort, while basic road sections were less influential. These findings underscore the importance of considering visual cues, such as markings and wall color, in creating safer and more comfortable underground road systems. This study’s methodology and results offer valuable insights for urban planners and engineers aiming to design transportation systems that are both safe and aligned with sustainable urban mobility objectives.

Reconstructing Road Roughness Profiles Using ANNs and Dynamic Vehicle Accelerations

Road networks are crucial infrastructures that play a significant role in the progress and advancement of societies. However, roads deteriorate over time due to regular use and external environmental factors. This deterioration leads to discomfort for road users as well as the generation of noise and vibrations, which negatively impact nearby structures. Therefore, it is essential to regularly maintain and monitor road networks. The International Roughness Index (IRI) is commonly used to quantify road roughness and serves as a key indicator for assessing road condition. Traditionally, obtaining the IRI involves manual or automated methods that can be time-consuming and expensive. This study explores the potential of using artificial neural networks (ANNs) and dynamic vehicle accelerations from two simulated car models to reconstruct road roughness profiles. These models include a simplified quarter-car (QC) model with two degrees of freedom, valued for its computational efficiency, and a more intricate full-car (FC) model with seven degrees of freedom, which replicates real-life vehicle behavior. This study also examines the ability of ANNs to predict the mechanical properties of the FC model from dynamic vehicle responses to obstacles. We compare the accuracy and computational efficiency of the two models and find that the QC model is almost 10 times faster than the FC model in reconstructing the road roughness profile whilst achieving higher accuracy.

Pengawasan Penyelenggaraan Parkir Tepi Jalan Umum di Kawasan Wisata Jalan Tunjungan pada Dinas Perhubungan Kota Surabaya

The large number of public roadside parking arrangements in the Jalan Tunjungan Surabaya Tourist Area that are not in accordance with existing regulations has caused a lot of anxiety among road users. This research aims to analyze how supervision is carried out by the Surabaya City Transportation Department in the implementation of public roadside parking to find out whether existing supervision makes public roadside parking comply with existing regulations, increases PAD resulting from parking fees, increases parking attendant compliance, as well as sanctions. given for existing violations. This research uses a qualitative descriptive approach with primary and secondary data sources. Data collection methods used were observation, interviews and documentation. Data analysis techniques use data collection, data condensation, data presentation, and drawing conclusions. This research uses the theory of implementation of supervision according to Soekidjo Notoatmodjo which consists of three, namely the object of supervision, the method of supervision, and the process of supervision. The results of this research indicate that supervision of the implementation of public roadside parking in the Jalan Tunjungan Tourist Area is not yet fully optimal because it does not meet the criteria in terms of supervision objects and supervision methods. This is due to the low level of discipline of parking attendants, in addition to the lack of socialization regarding parking management regulations to the public so that complaints are rarely made by the public. However, the criteria for the supervision process show that the process of carrying out supervision by the Surabaya City Transportation Service is in accordance with the established flow and the imposition of sanctions for violations by parking attendants is determined in accordance with applicable regulations. It is hoped that the results of this research can make supervision more optimal.

Matching vs. Mismatching Signals of External Human-Machine Interface and Vehicle Kinematics: An Examination of Pedestrian Crossing Behavior and Trust, Safety, and Affective Ratings in Interactions with Differently Sized Automated Vehicles

Highly automated vehicles (HAVs) will soon be introduced into mixed urban traffic. Pedestrians might have an idea of HAVs. Nevertheless, they probably have never interacted with them before. Moreover, pedestrians will not be able to communicate with HAVs like they are used to with manual vehicles. External human–machine interfaces (eHMIs) are possible design solutions for HAVs to ensure safe interaction with other road users. Light-based eHMIs positively affected pedestrians’ trust ratings, perceived safety, and willingness to cross. However, previous studies often neglected the effect of vehicle size, although larger-sized HAVs could be potentially perceived as the more significant threat. Additionally, the relationship between vehicle kinematics and eHMIs for differently sized HAVs remains an underexplored research topic. This study investigated the effects of vehicle size (small vs. large), eHMI state (dynamic eHMI vs. static eHMI vs. no eHMI), and vehicle kinematics (yielding vs. non-yielding) on pedestrian crossing behavior and their subjective assessment. In virtual reality, we created a shared space traffic scenario, in which the eHMI and vehicle kinematics matched or did not match. For yielding conditions, the results showed that participants felt more aroused with larger HAVs than with smaller HAVs. Moreover, pedestrians initiated their crossing significantly earlier when both vehicle sizes had a dynamic eHMI compared to a static eHMI vs. no eHMI. Additionally, pedestrians evaluated a dynamic eHMI with higher trust ratings, higher perceived safety, and more positive affective reactions. The results manifested that the use of dynamic eHMIs can effectively enhance pedestrian-vehicle communication with a large and a small HAV. For non-matching conditions, the participants tended to rely on the vehicle kinematics for both vehicle sizes. Overall, the study highlighted the potential of eHMIs for pedestrian interactions with HAVs of varying sizes when they are well-coordinated with the vehicle kinematics, aiming to enhance safety and efficiency in mixed-traffic environments.

Toward Foundation Models for Inclusive Object Detection: Geometry- and Category-Aware Feature Extraction Across Road User Categories

Toward Foundation Models for Inclusive Object Detection: Geometry- and Category-Aware Feature Extraction Across Road User Categories

An AI-driven Approach to Traffic Congestion Prediction in Port Harcourt City Using Naïve Bayes

Four important intersections Location Junction, Rumuokoro Junction, GRA Junction, and Rumuokwuta Junction will be the focus of this study's investigation into the persistent problem of traffic congestion in Port Harcourt, Nigeria. In order to provide insights for improved traffic management, the project also aims to construct a predictive model that forecasts traffic congestion on certain routes using the Naïve Bayes algorithm. The study used both exploratory and descriptive research techniques to gather information about Port Harcourt's traffic congestion. Field surveys were carried out at the four intersections between 7 a.m. and 7 p.m. to collect traffic counts and vehicle footage. The investigation also included data from Rivers State Command, a division of the Federal Road Safety Corps (FRSC). Using this data to find patterns and trends in traffic flow, a Naïve Bayes algorithm was developed to classify and predict the degree of traffic congestion. With a 97% accuracy rate, the algorithm accurately classified 1164 out of 1200 traffic data points, predicting traffic congestion. The predictive algorithm used bar charts to show the traffic conditions; tall, moderate, and tiny bars, respectively, indicated high, medium, and low traffic congestion. The model's efficacy in real-time traffic forecasting was reinforced by the close match between the forecasts and observed traffic patterns. According to the study's findings, the Naïve Bayes algorithm can accurately forecast traffic congestion and provides road users and urban planners with useful information. The model can greatly enhance road efficiency by lowering travel times, relieving congestion, and improving traffic management on Port Harcourt's major arteries by accurately predicting traffic conditions.

The Impact of MRT Line-6 Construction Work on The Environment, Road Users, and Traffic During the Construction Period

The Impact of MRT Line-6 Construction Work on The Environment, Road Users, and Traffic During the Construction Period

Analysis of the Unsafe Road User Behavior in Ternate City Using AHP Method

Unsafe road user behavior is something that can affect the implementation of transportation as data from the police states that 61% of accidents are caused by human factors. This study aims to determine the types and factors that cause violations committed by road users who have passed intersection three (Jalan Hasan Esa-Jalan Ahmad Yani) and (Jalan Pemuda-Jalan A.M Kamaruddin) along with the handling of these violations. The results of the analysis found that the most common type of violation was not using a rear helmet by 88.8% caused by not having a helmet. Meanwhile, based on the analysis with the AHP method, the violation that must be prioritized in handling is against the flow with priority alternative handling, including education, sanctions and training.