Traffic Safety Improvement Research Articles

Traffic Safety Problems and Improvement Measures through Child Traffic Accident Case in Apartment Complex

Traffic Safety Problems and Improvement Measures through Child Traffic Accident Case in Apartment Complex

Transport Management for Sustainable Urban Development at Tambun Market Area

Tambun market is a commercial area located at T-Junction, an area with mixed land uses. Land use in this area consists of settlement, commuter line stations, offices, factories, and a historic building (Gedung Juang) which is a tourist destination. Trade activities, loading goods, unloading goods, public transport passengers, on-street parking, and traffic conflict between vehicles and pedestrian in this area influence the road traffic. It causes the bottleneck up to congestion at Tambun Market area. The level-crossing at the commuter line station become one of the causes of congestion. To solve from 2014 to 2017 that problem, the government of Bekasi districts built the underpasses to overcome congestion and improve road safety. In fact, the construction of underpasses does not solve the problems. The purpose of this research is to propose transportation management in the Tambun market area. The problem is analyzed based on the principles of traffic engineering. There are include some points such as accessibility planning, movement of people, goods and vehicles, pedestrian facilities, and parking areas. The analysis is using the principle of connectivity, accessibility, comfort, and safety. The results are the improvement of pedestrian facilities, design of public parking area, loading and unloading management, improvement road performance for Volume and Capacity Ratio (v/c ratio), traffic flow and accessibility planning, where it could be improvement of connectivity, accessibility, and traffic safety.

Vehicle Trajectory Prediction Based on Motion Model and Maneuver Model Fusion with Interactive Multiple Models

<div class="section abstract"><div class="htmlview paragraph">Safety is the cornerstone for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). To assess the safety of a traffic situation, it is essential to predict motion states of traffic participants in the future with mathematic models. Accurate vehicle trajectory prediction is an important prerequisite for reasonable traffic situation risk assessment and appropriate decision making. Vehicle trajectory prediction methods can be generally divided into motion model based methods and maneuver model based methods. Vehicle trajectory prediction based on motion models can be accurate and reliable only in the short term. While vehicle trajectory prediction based on maneuver models present more satisfactory performance in the long term, these maneuver models rely on machine learning methods. Abundant data should be collected to train the maneuver recognition model, which increases complexity and lowers real-time performance. In this paper, a vehicle trajectory prediction method based on motion model and maneuver model fusion with Interactive Multiple Model (IMM) is proposed. Firstly, Constant Turn Rate and Acceleration (CTRA) motion model and Unscented Kalman Filter (UKF) are used to predict vehicle trajectory with uncertainty in the future. Then, vehicle trajectory prediction based on simplified maneuver recognition model is conducted, using temporal and spatial relationship between vehicle historical trajectory and lane lines. After that, vehicle trajectory prediction by integrating motion model and maneuver model with IMM is conducted. Finally, the proposed method is compared with CTRA motion model based vehicle trajectory prediction and lane keeping model (LKM) based vehicle trajectory prediction in two simulation test scenarios. The simulation results indicates that the IMM-based method achieves both excellent prediction accuracy and appropriate prediction uncertainty in the whole prediction horizon. This research can be used to support decision making for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems and leads to improvement of traffic safety.</div></div>

Smart Transport in road transport infrastructure

Introduction of new technologies in cities through effective interaction between people and modern IT management tools has introduced the concept of «smart city» able to independently manage its own resources, energy, space and information to improve life quality. One of the main components of a «smart city» is «smart transport» using latest communication technologies for efficient movement, monitoring of location, interaction between vehicles and other traffic elements, general traffic safety improvement resulting in a decrease in the accident number (tendency towards zero mortality). One of the ways to expand this field of UAV application is their use for primary photofixation of the accident site, which will both increase the objectivity of the investigation and help to reduce time required for the investigation. Solving problems related to traffic safety in a «smart city» using IT technologies and UAVs — smart transport components results in fast collection of information, including that from hard-to-reach zones, the ability to take pictures from small heights and near objects to obtain high-resolution images. These are a possibility of application in emergency zones without unnecessary risk to life and health of personnel; improvement of the objectivity of primary information from the accident site; the possibility of automatic processing of the primary information with its transformation into the necessary types of formalized documents.

Assessment of sustainability of improving road safety process in the volga federal district

The article examines the issues of assessing the sustainability of traffic safety improvement process in the cities of the Volga Federal District of the Russian Federation. In 2015…2018 a sharp decrease in the overall level of road traffic accidents in the Russian Federation was recorded. However, in different regions and cities of the country this positive process runs extremely heterogeneously, with various speeds and different levels of qualitative changes in the field of road safety. The T-Wilcoxon criterion is an instrument, used in analyzing accident rate statistics, which can help in the argumentation of the opinion on the stability of this process or, vice versa, on the chaotic state and weak expression. On the example of accident rate statistics in subjects of the Volga Federal District of Russia, the article proves that improvement of road safety can be characterized as sustainable.

Modelling heterogeneous traffic dynamics by considering the influence of V2V safety messages

In recent years, more attention has been paid to the study of modelling heterogeneous traffic flow dynamics consisting of regular vehicles (RVs) and connected vehicles (CVs). However, the establishment of current mixed traffic flow models mostly lacks the support of field experimental data analysis results. In this study, a new heterogeneous car-following model is presented on the basis of data mining results to study the impact of vehicle-to-vehicle (V2V) safety messages on traffic flow stability. The analytically critical stability criterion of this proposed heterogeneous model is obtained by linear stability analysis. Then, the effect of the market penetration rate (MPR) of CVs on heterogeneous traffic stability is investigated. Finally, simulation experiments are carried out under periodic boundary conditions to test the critical stability condition. Both the theoretical derivation and simulation experiment results show that the traffic flow stability can decline despite improvements in traffic safety in the case of considering only the safety information of the nearest-neighbour leading vehicle. However, both the safety and stability of traffic flow can be enhanced by considering the V2V messages of more leading vehicles. Moreover, the heterogeneous traffic flow stability is improved with an increase in the MPR of CVs.

Using Vehicle-to-Vehicle Communication to Improve Traffic Safety in Sand-dust Environment.

Sand-dust environment affects drivers’ perceptions of surrounding traffic conditions, resulting in unsafe operations. From an ergonomics perspective, such adverse effects could be alleviated by environment control as well as the assistance of machines. Vehicle-to-vehicle (V2V) communication appears to be an important component of machines in future traffic systems, which could support the driving task. In order to explore what influences V2V communication would generate on traffic systems, this paper proposes a car-following model accounting for V2V communication in a sand-dust environment. The results indicate that V2V communication helps to reduce the fluctuations of acceleration, headway, and velocity, when a small perturbation is added to the traffic flow in sand-dust environment. If a vehicle in the traffic flow stops suddenly, the number of crumped vehicles decreases with V2V communication taken into account. Furthermore, the residual velocities of the crumped vehicles decrease, which means the severity of collision is suppressed. It is concluded that V2V communication can play an active role in the improvement of traffic safety in a sand-dust environment.

The Effect of Distracted Driving Laws on Automobile Liability Insurance Claims

We examine whether laws designed to reduce distracted driving have consequences for the automobile liability insurance market. Our research is motivated by prior studies that suggest distracted driving laws lead to improvements in traffic safety. Following these studies, we propose that distracted driving laws should also lead to reductions in the frequency and cost of injury liability insurance claims. Consistent with this expectation, we provide evidence that cellphone bans lead to approximately 3,400 fewer injury liability insurance claims, on average, in any given state enacting a ban. Our analysis also suggests that cellphone bans lead to reductions in injury liability loss costs, and we estimate the total statewide savings to the insurance industry, on average, is approximately $32 million per year in any given state enacting a ban. Additional analysis that further considers other types of distracted driving laws confirms that laws designed to limit distracted driving lead to substantial reductions in the frequency and cost of injury liability insurance claims. Finally, we also present analysis that suggests the reduction in claims frequency and injury liability loss costs attributable to distracted driving laws leads to statewide automobile liability insurance premium savings of approximately 4.7%.

Communication between road users and the influence of increased car automation

For a smooth, safe and comfortable cooperation of all actors in traffic good communication is essential. In view of the progressing automation in traffic the impacts on communication have to be considered. This paper puts a special focus on car automation's impact on the communication between road users including interaction between highly automated vehicles and vulnerable road users (VRUs). The main research objective was to develop relevant assumptions concerning changing conditions of communication. To reach this objective, interviews with experts were carried out. The results show that various developments between the two poles - (1) implementation controlled by certain societal strategies or (2) implementation that just takes its course - are considered possible. Enhanced automation could lead to the decrease in the use of interpersonal communication while the use of digital communication gains the upper hand. Such a development, on the one hand is seen as a chance for the improvement of traffic safety and efficiency. On the other hand, interviewed experts also identify risks such as misunderstandings between VRUs and automated cars, with fatal outcomes, or reductions in traffic flow. This is expected to be the case especially during the transition phase where vehicles with different degrees of automation are on the road and where many road users outside vehicles move about, e.g. in densely inhabited areas, where ~70% of the citizens in industrial countries live nowadays.

Endogenous relationship of accident occurrence with speed, traffic heterogeneity and driving environment on inter-urban roads in Indonesia

Speed performances and characteristics of traffic have mostly been considered as homogeneous across vehicles. In countries where the roads are dominated by mixed types of vehicles, the heterogeneity needs to be considered. This study is aimed at modeling how traffic heterogeneity as captured in speed, speed deviation, and traffic volume determines the fatality rates and accident rates. Traffic volume, road geometry (bendiness, hilliness, bend density and hill density) and road surface condition (represented by IRI) become the independent variables in a simultaneous regression using structural equation model (SEM). SEM is adopted to represent the hierarchical causal effects between the independent variables and dependent variables. The data cover inter-urban roads in eight provinces in Indonesia from 2012-2016 and 2019. Speed is not significant in predicting accident rate, and speed deviation is not significant in predicting fatality rate. An increase in speed deviation lowers the accident rates; an increase in speed increases fatality rates. Road geometry and traffic volume negatively impact the speed deviations of all vehicle categories, indicating that when there is more traffic on the road, the speeds of all vehicle categories become more homogenous. Bend density, bendiness, hill density and hilliness negatively affect both the speed and the speed deviations of the vehicles of all categories The findings of the study can contribute to traffic policing and traffic safety improvement schemes for heterogeneous traffic.

"디자인에 기초한 어린이보호구역 교통안전 환경개선 연구 ; 도로 공간 시각 위계와 어린이 보행행동 특성을 중심으로"

"디자인에 기초한 어린이보호구역 교통안전 환경개선 연구 ; 도로 공간 시각 위계와 어린이 보행행동 특성을 중심으로"

Comparative Assessment Between Statistical and Soft Computing Methods for Accident Severity Classification

Globally traffic scenario has experienced a vast change over the years as volumes of traffic on urban roads have increased by leaps and bounds. This increase has also marked an increase in the number of road accidents, both fatal and non-fatal. Accident prediction has a significant role in the improvement in traffic safety, and urban traffic management. Different statistical methods and various soft computing techniques are being used for developing accident prediction models. In this study, ensemble ranking method, comprising three filter-based ranking methods such as Information Gain, Gain Ratio, and Symmetric Uncertainty, has been employed to select significant features for prediction of accident severity. Through analysis, eight significant features have been selected for accident severity prediction. These are Annual Average Daily Traffic, Road Width, Percentage of Left-Turn Vehicles, Driver’s Age, Percentage of Right-Turn Vehicles, Types of Vehicles Involved, Spot Speed, and Cause of Accidents. Multiple logistic regression (MLGR) and artificial neural network (ANN) have been applied for accident severity prediction in urban roads. This analysis concludes that ANN model gives more accuracy and minimum errors compared with the MLGR method at the time of severity classification of a vehicular accident.

Discussion of active safety countermeasures of motorway work zone

A series of comparison analyses of work zone configurations, traffic organizations and work management among China and oversea countries are carried out. Taking into consideration of human, vehicles, roads, environment as well as management factors, the causes of traffic accidents in work zone areas are deeply discussed. Based on road safety in work zone areas, a series of active traffic safety improvement countermeasures for both workers and drivers in work zone areas have been introduced from the respects from traditional safety devices and traffic control measures to information and intelligent measures, which will provide inspires for the improvement of work safety and operation safety in work zone areas.

A Web-Interface based Smart Tracking of Overloaded Vehicle

Due to the development of road infrastructure and business trade, transportation of goods is increasing day-by-day. Each vehicle has its own configuration, size, power and capacity. However, heavy vehicles are often used to carry loads beyond their capacity. Therefore overloading is one of the major problems which affect truckers as well as other motorists and pedestrians going near-by. In order to provide a convenient solution to this problem, a web-based tracking system is proposed. A heavy duty vehicle which is overloaded and moving at high speed may cause the vehicle to loose balance while making turns along the roads. This problem may also cause the tire to burst, which in turn makes it difficult to control the vehicle. Communication module is interfaced with a microcontroller which takes data from global positioning system receiver to locate the current position of the overloaded vehicle. This unit is majorly controlled by an electronic control unit. A sensing element is placed at the axle point of the vehicle to concentrate on the total weight of the vehicle. A software study is done to extract the global positioning system data from GPRMC format. The proposed system is simulated and tested for semi-real data. The received data is transmitted to the control room user for further improvement in traffic regulation and road safety.

Interpretable machine learning approach in estimating traffic volume on low-volume roadways

Abstract Many state and local agencies are currently facing challenges concerning the collection and estimation of traffic volumes, particularly regarding the collection of annual average daily traffic (AADT) on low-volume roads. To overcome these challenges, there is a need to develop new affordable methods to collect data and estimate traffic volume on low-volume roadways. In this study, the research team developed an innovative interpretable machine learning framework and applied it to low-volume roads in Vermont to estimate traffic volumes. This study used several databases (e.g., U.S. Census, the American community survey) to prepare the final dataset for the model development. The findings show that population density and work area characteristic (WAC) density are the best predictors in estimating AADT. The model outcomes show that the machine learning models yield better estimates than the conventional parametric statistical methods. By improving the accuracy of AADT estimations, this study contributed to traffic monitoring and safety improvement, and it can help reduce costs of data collection. This study developed the top five decision rules for three types of low-volume roadways. Stakeholders can use the findings of this study to meet the new requirements pertaining to availability of AADT estimates for low-volume roads. Additionally, the best fit estimates and the developed rules from the current study could enhance the predictive power of the SPF development for the low-volume roadways in Vermont and therefore improve the decision-making process.

The challenges impeding traffic safety improvements in the Kurdistan Region of Iraq

Due to the stability and economic development in the Kurdistan Region of Iraq (KRI) relative to its neighbors after 2005, the density of the population and number of vehicles have increased dramatically. This situation produced certain undesirable consequences socially, administratively, legislatively, technically, and economically. An increase in the number of traffic crashes was one of the serious challenges that local authorities faced. The objective of this study is to address the challenges of traffic safety improvement in the region. Based upon existing traffic-related situations in the KRI, some fundamental and priority recommendations are proposed such as improving traffic regulations and guidelines, creating a traffic crash database, and starting traffic safety training and education. It is crucial to address the challenges of impeding traffic safety improvement in the KRI.

TECHNOLOGICAL MEASURES OF FOREFRONT ROAD IDENTIFICATION FOR VEHICLE COMFORT AND SAFETY IMPROVEMENT

This paper presents the technological measures currently being developed at institutes and vehicle research centres dealing with forefront road identification. In this case, road identification corresponds with the surface irregularities and road surface type, which are evaluated by laser scanning and image analysis. Real-time adaptation, adaptation in advance and system external informing are stated as sequential generations of vehicle suspension and active braking systems where road identification is significantly important. Active and semi-active suspensions with their adaptation technologies for comfort and road holding characteristics are analysed. Also, an active braking system such as Anti-lock Braking System (ABS) and Autonomous Emergency Braking (AEB) have been considered as very sensitive to the road friction state. Artificial intelligence methods of deep learning have been presented as a promising image analysis method for classification of 12 different road surface types. Concluding the achieved benefit of road identification for traffic safety improvement is presented with reference to analysed research reports and assumptions made after the initial evaluation.

Creation concept and architecture principle for vehicle motion control competitive systems under road and climatic conditions of the Russian Federation

The urgency of traffic safety improvement is determined by the global significance level of this problem. Current research provides the scientific substantiation of the creation concept and building principles for competitive motion control systems (MCSs) under road and climatic conditions of the Russian Federation. The study was carried out using system analysis methods and modern control theory. The safe vehicle control task is formulated as prevention of collisions with moving and stationary obstacles on the route taking into account the road and climatic conditions of the Russian Federation. To estimate the quality of the dynamic stabilization solution modified quadratic functional is introduced. The results of the study are presented as an analysis of properties of the modified quadratic control quality functional and associated consumer indicators. Based on the analysis of results of the minimization problem for control quality quadratic functional and the multicriteria problem could be solved by software and hardware modification of intelligent systems realized in the minimal configuration of hardware. The novelty of the work is the scientific substantiation of the concept for the competitive vehicle MCSs creation based on virtual information sensors, the use of new mathematical models and control algorithms to solve the problem of collision avoidance.

The experimental system of automatic wheel drift and vehicle rollover prevention

The research topic relevance is determined by the global significance level of traffic safety improvement. The purpose of the work is to create mathematical model and software for the active vehicle safety experimental intelligent system. To solve this problem, methods of indirect vehicle state variables measurements were used. As a mathematical model, we used a system of equations for the wheels rotation speed on a bend. For estimating the parameters of the mass center and the wheels motion, standard wheel speed sensors and algorithms for processing the measurement data are used. The task of angular drift velocity determining based on wheels rotation frequency measurements is ill-posed and additional conditions are introduced to solve it. To predict the wheel drift and car rollover, extrapolate algorithms are used. These phenomena are prevented by the certain brake formation that limit the object speed at the predicted boundaries level. The research novelty is to create algorithms for an experimental system that provides the solution to the wheel drift and rollover prevention problem in a minimal configuration of technical means.

Equity Analysis of Proactively- vs. Reactively-Identified Traffic Safety Issues

Traffic safety issues often impede bicyclist and pedestrian trips, preventing potential users from realizing the benefits of active transport. Traditional active transportation safety analyses, however, take a reactive approach to traffic safety, only accounting for people currently walking or bicycling by analyzing crashes, injuries, and fatalities. This begs the question: which populations are most affected by traffic safety issues neglected by traditional crash analyses? To answer this, we developed a tool to proactively measure perceived traffic safety issues. We focused on child pedestrian and bicycle trips to and from schools in Denver, Colorado by measuring the number of children that would encounter roads perceived as unsafe. We converted these perceptions into barriers in a geographic information system network analysis to estimate trip suppression and used that as a proactive indicator of traffic safety. We finally examined—reactively and proactively—the socio-demographics of those affected via linear regression models and bivariate choropleth mapping. Results of both analyses suggest that negative impacts are borne disproportionately by low-income, low-education, Hispanic, and black neighborhoods. Proactive analyses results identified perceived safety issues in north and northeast Denver neighborhoods neglected by reactive analyses results. Findings suggest the inequitable distribution of traffic safety issues identified in past crash-based literature is graver than conventional reactive analysis would lead one to believe. By incorporating the proactive tool into traditional traffic safety analyses, we hope to better define the places and people that could most benefit from traffic safety improvements, thereby more effectively facilitating the benefits of walking and biking.