Accident Black Spots Research Articles

Geopathic Stress: A Possible Cause for Pavement Distresses and Road Accidents

India loses 3% of its GDP due to road accidents. Significance of Geopathic stress as a causative factor of road accidents has been studied by few researchers; however its effect on Pavement distresses and hence road accident is yet unexplored. The aim of this research is to determine the correlation between average number of accidents, Pavement Condition Index (PCI) values and Geopathic stress. Accident data was collected from Pune traffic department for 3 years period from 2015-16 to 2017-18. Based on the number of accidents during this period accident blackspots were found. On each black spot pavement distresses survey was carried out and its condition was analyzed by Indian Road Congress (IRC) 82:2015 code method. At these accident blackspots detection of geopathic stress was done by using 2 copper L-rods, lecher antenna. Intensity was measured in terms of electrical and magnetic field. Electrical field reading was measured using Esmog-spion and magnetic field reading was measured by magnetometer. Data was analyzed using Karl Pearson’s correlation coefficient and a linear regression model is developed for average number of road accidents (Ā) with Pavement Condition Index (PCI). Utility of the equation is for forecasting the number of fatal accidents at similar black spots based on their pavement distress condition. A further attempt is to investigate the effect of electric and magnetic characteristics of geopathic stress on road accidents.

Geographical Locations of Accident-Prone Areas in the Northern Senatorial District of Ondo State in Nigeria

The occurrence of accident irrespective of the cause seems to be concentrated on some particular points or areas on our roads. Such areas have witnessed accidents that have claimed lives and as a matter of fact, have left many families to be widowed,while the country has been drained of active manpower. It is in the realization of these facts that this paper aims at identifying accident prone areas in the Northern Senatorial District of Ondo State in Nigeria with a view to reducing road accident and at suchidentify accident vulnerable areas. A reconnaissance visit was made to study the area and thereafter, three hundred and ten (310) copies of questionnaires were purposively administered to ten (10) areas each: that is 31 copies of questionnaires in each sampled zone. The study identifies specific areas prone to road traffic accidents which account for various geographical characteristics of identified accident black spots. The study identifies Okia-Oka as the most prone accident area in order of magnitude from year 2010 to 2019. The black spots were identified and demarcated on a map to serve as a road guide for road users. The study recommends mounting of programmed traffic light for the management of traffic, particularly at notorious accident black spots in the study areas such as Akungba–Oka Junction, Benin-Ifon Junction and Owo–Ikare Junction. The erection of billboard at accident prone areas indicating speed limit of 30km per hour among others is also suggested.

Prioritization of Black Spots in Hilly Road Segment using Road Engineering Data

Road accidents are one of the causes of disability, injury and death. As per the latest road accident data released by the Ministry of Road Transport & Highways (MoRTH), the total number of accidents increased by 2.5 percent from 4,89,400 in 2014 to 5,01,423 in 2015. The analysis reveals that about 1,374 accidents and 400 deaths take place every day. Every single year, it has been estimated that over three lakh persons die and 10-15 million persons are injured in road accidents throughout the world. According to the analyses, statistics of global accident indicate that in developing countries, the rate of fatality per licensed vehicle is very high as compared to that of industrialized countries. A road stretch of about 500 metres in length in which either ten fatalities or five road accidents (involving grievous injuries/fatalities) took place during last three calendar years, on National Highways is considered as a road accident black spot according to MoRTH, Government of India. In the present study the identified black spots of Haridwar and Dehradun city were included comprising of a total of 81 black spots out of which there were 49 black spots which were identified in Dehradun followed by 32 black spots in Haridwar. The present study was an attempt to carry out the prioritization of these identified blackspots with respect to the factors that were considered to evaluate accident prone locations on the road. The identified black spots were then prioritized using the classification scheme (ranking from low to high).The study reveals that the advantage of using this approach for prioritizing accident black spots on roads is that it requires very less additional data other than the road network maps.

Systemic approach to auditing road traffic accident black spots

The systemic approach described in the article is designed for the development of an effective approach and purposeful measures to ensure road traffic safety. The need for a systemic approach to auditing road traffic accident black spots is justified. A conceptual model of the systemic approach to auditing road traffic accident black spots is developed. This model includes interrelations of subsystems, factors of accident-prone situations, causes of the emergence of road traffic accident black spots in the system “road user – vehicle – road – external environment”. A hierarchy of interrelations and flows in the system ensuring road traffic safety is suggested. The authors suggest considering the “Road User – Vehicle – Road – External Environment” system that includes such subsystems as the Driver (D), Pedestrian (P), Child Pedestrian (PC), Vehicle Passenger (PassV), Child Passenger (PassVC). They also suggest using the traditional term “Car subsystem” as the Vehicle (V). To account for the entire content of the “external environment” (EE) subsystem, the following structure is suggested: road infrastructure (RI) reflecting the conditions and parameters of traffic management as well as existing weather and climatic conditions. The systemic approach to auditing road traffic accident black spots reveals the potential of improving this mechanism.

ПОТЕНЦИАЛ СОВЕРШЕНСТВОВАНИЯ МЕХАНИЗМА АУДИТА МЕСТ КОНЦЕНТРАЦИИ ДТП

Introduction: This study proves that the procedure of inspecting road traffic accident black spots (RTA BS) needs improvement. This improvement is to involve the tools and insights associated with the targeted program approach, as well as a road infrastructure indicator system, and information technology tools. The creation of a road infrastructure indicator system and its comprehensive application, coupled with analytical methods and accident prediction system methods, enables the assessment of measures aimed at reducing the number of RTAs. Accounting for information technology tools and systems (such as the digital traffic safety inquiries desk) is also necessary if traffic safety is to be organized and maintained in a systemic way. Purpose of the study: The study is aimed at finding a new approach to improving the procedure of inspecting RTA black spots. Methods: In the course of the study, we use systemic analysis, analytical methods, traffic safety evaluation based on defining the safety and accident coefficients and revealing RTA black spots, probability theory methods, research results processing, and IT computational methods. Results: We provide a rationale for a comprehensive approach to inspecting RTA black spots within the “traffic participant – vehicle – road – external environment” system. We also demonstrate how a group of parameters can be used for studying the systemic indicators of road infrastructure, in the context of the parameters’ characteristics, as well as the conditions of their use. We determine the capabilities of analytical methods, as well as accident prediction methods, in the context of finding an approach to improving the procedure of inspecting RTA black spots. We propose applying a comprehensive approach to the improvement of the RTA BS inspection procedure.

EFFECTIVE APPROACH TO DETECTING ROADS FOR PRIORITY PLANNING FOR ROAD SAFETY MEASURES

The basic approaches for identifying dangerous road sections for prioritizing road safety measures are outlined. The effectiveness of the result depends on how well the areas where the road safety measures need to be implemented are identified. Suggestions for identifying dangerous places on the roads according to the statistics of traffic accidents using the methods of probability theory are given. On the basis of the analysis of statistics on roads with different number of adventures, limit values of the admissible number of adventures on sections of roads of different length are established. It is proved that it is necessary to create a comprehensive approach to solving a complex problem – improving road safety. Оne of the important approaches for the definition of dangerous road sections according to the data of road accidents, which is proposed by the authors, is the method of detection of sections (places) of concentration of road accidents (black spots). The purpose of this article is to: introduce an approach in road safety management to reduce the number of road accidents and the severity of their consequences on Ukraine’s highways by first implementing measures to improve road conditions and improve road organization. The effectiveness of the result in reducing the number of traffic accidents depends on the areas so clearly identified that, in the first place, it is necessary to implement measures to improve road safety. That is why this approach was introduced in traffic safety management. The purposeful financing of measures, aimed primarily at eliminating such sites, will help to reduce the number of road accidents and the severity of their consequences. Keywords: road safety, methods of analysis, dangerous road sections, place of concentration of road accidents, black spots, road accident.

Relationships Between External Factors and Pedestrian Accident Blackspots – A Case Study of the City of Zagreb

Traffic accidents represent a social, health and political challenge in every country. Urban environments are characterized by intense traffic flows on the network, where different conditions resulting in interactions between motorised and non-motorised transport constantly occur, potentially increasing the risk of accidents. Although road accidents are considered as random events in space and time, a highly detailed analysis may establish correlations between road accidents and external factors (road infrastructure, traffic conditions, weather conditions, land use). This paper considers the impact of external factors on road accidents involving pedestrians in the City of Zagreb, which required an analysis of accident blackspots. The research conducted in this paper puts an emphasis on relations between external factors and accident blackspots involving pedestrians. The results can be used in planning pedestrian infrastructure and improving road safety.

Research on black spot identification of safety in urban traffic accidents based on machine learning method

With the rapid development of economy and urbanization, the number of urban motor vehicles keeps increasing. Urban travel is more convenient, but the traffic safety problems are increasingly prominent. Traffic accident data include not only time and place, but also people, roads, vehicles and the surrounding environment. Traffic accident black spot is the spatial location of traffic accident concentrated distribution. Most of the traditional traffic accident black spot identification only considers time and space factors, ignoring other factors. Based on the traffic accident data of Suzhou Industrial Park, this paper makes a fusion analysis of the multi-source influencing factors involved in traffic accident black spot. According to the structured association characteristics of urban traffic accident big data, a support vector machine method based on maximizing the classification interval is used to train the complex model and optimal learning of accident black spots in the study area. The accuracy of black spot identification is improved. At the same time, aiming at the rapid growth of traffic accident multi-source data, a black point identification algorithm based on deep neural network is proposed. The deep neural network of relevant data category information is established to verify the model's ability to identify accident black spots. A feature-based black spot identification method based on depth neural network is proposed. Furthermore, a dynamic adaptive machine learning architecture is built.

Road safety audit – what we have learnt?

The traffic accident problem in Malaysia is one of the most serious problem facing the nation. In the effort to reduce traffic accidents, ‘Re-Active’ initiatives, such as the ‘Accident Blackspot’ Program, ‘Pro-Active’ measures such as Road Safety Auditing and improved road design practices is implemented. In early 1997, Road Safety Audit (RSA) was started in JKR and a manual was produced as the primary guide for Road Safety Auditing by JKR and its Consultants engaged in this work. Since then, the implementation of RSA has revealed issues on poor RSA management, common repetitive mistakes by designers and the absence of warrant application. This paper discusses these issues and relates them to the study carried out. This study attempts to compare local RSA practices with other countries in finding the solutions to the above problems. It has been shown that having a policy, clear roles & responsibility and proper RSA management is vital in ensuring road safety is adequately catered for. Furthermore, frequent updating of design guidelines and building up of competency of road designers can substantially reduce the issue on quality of road design.

The impact of road alignment characteristics on different types of traffic accidents

Among the factors affecting road traffic safety, road alignment characteristics are often ignored by people because they cause traffic accidents in an indirect way. In view of this problem, the total number of accidents, the total casualties of accidents (deaths, minor injuries, serious injuries), and the total time of road closures were chosen as road safety evaluation indices and ranked by the technique for order preference by similarity to an ideal solution (TOPSIS) method. The road sections whose ranking index value C < 0.8 were identified as accident blackspots. On this basis, the slope direction, absolute value of the elevation difference, Standard Deviation of elevation, curve radius, and transition curve ratio were chosen to describe the characteristics of road alignment. Combined with historical accident data, binary logistic regression models were established to analyze the effects of alignment factors on the frequent occurrence of three accident types (rear-end, hitting the fixture, rollover).

A Novel Identification Model for Road Traffic Accident Black Spots: A Case Study in Ningbo, China

With the rapid development of the social economy and accelerating urbanization, the total number of motor vehicles continues to grow at a high rate. Roads in large- and medium-sized cities are becoming increasingly congested, which leads to frequent traffic accidents. To enhance road traffic safety and reduce the traffic accident rate, effectively identifying accident black spots is of great importance. In this study, the data from traffic accidents on the Lianfeng Middle Road, Yinzhou District, Ningbo City were selected for the analytical dataset, and eight impact factors (holiday, day of week, time, rush hour traffic, accident location type, accident type, weather, responsibility and black spot) were set. The improved K-means clustering algorithm was proposed to solve the shortcomings of the traditional algorithm, which is susceptible to outliers and initial clustering centres. Through this algorithm, the traffic accidents in the dataset were divided into two categories: black spots and non-black spots. Then, using the updated dataset, we employed a Bayesian network to construct a black spot identification model, and applied other widely used algorithms (the ID3 decision tree, logistic regression and support vector machine) for comparison. The values of the ROC area, TP rate, FP rate, precision, recall, F-measure and accuracy reached 0.618, 0.668, 0.580, 0.650, 0.668, 0.590 and 0.668, respectively, which showed that the Bayesian network was the best model to effectively identify road accident black spots. Moreover, a bivariate correlation model was applied to verify the correlation between the impact factors and black spots. The results indicated that the accident location type, accident type, time, and responsibility had significant correlations with black spots, which had a value of sig<; 0.05. The conclusions could provide reference evidence for the identification and prevention of traffic accident black spots to significantly contribute to traffic safety.

Identification of Accident Blackspots on Rural Roads Using Grid Clustering and Principal Component Clustering

Identifying road accident blackspots is an effective strategy for reducing accidents. The application of this method in rural areas is different from highway and urban roads as the latter two have complete geographic information. This paper presents (1) a novel segmentation method using grid clustering and K‐MEDOIDS to study the spatial patterns of road accidents in rural roads, (2) a clustering methodology using principal component analysis (PCA) and improved K‐means to create recognition of road accident blackspots based on segmented results, and (3) using accidents causes in police report to analyze recognition results. The proposed methodology will be illustrated by accident data in Chinese rural area in 2017. A grid‐based partition was carried on by using intersection as a basic spatial unit. Appended hazard scores were then added to the segments and using K‐means clustering, a result of similar hotspots was completed. The accuracy of the results is verified by the analysis of the cause extracted by Fuzzy C‐means algorithm (FCM).

Determination of accident black spots by using network screening and K-means clustering methods: a case study on D100 highway in Istanbul

Determination of accident black spots by using network screening and K-means clustering methods: a case study on D100 highway in Istanbul

Applying GIS in Analysing Black Spot Areas in Penang, Malaysia

This study aims to analyze fatal accident rate involving all vehicle types in the North East District of Penang. It covers fatal accident data within the duration of three years from 2011 till 2013. The primary objective is to analyze the spatial pattern and fatal accident black spot areas using Geographic Information System (GIS) application. Average Nearest Neighbor (ANN) tool is used to analyze fatal accident spatial pattern, while Kernel Density Estimation (KDE) method is utilized for fatal accident analysis. The Fatal Accident rates in 2011, 2012 and 2013 were the highest with each accounted up to 90, 88 and 91 cases. The result of ANN shows that the fatal accident pattern for 2011, 2012 and 2013 is clustered with null hypothesis rejected. The KDE analysis result shows that most fatal accident black spot areas happened at main road areas or segments.

Random Regret-Minimization Model for Emergency Resource Preallocation at Freeway Accident Black Spots

The preallocation of emergency resources is a mechanism increasing preparedness for uncertain traffic accidents under different weather conditions. This paper introduces the concept of accident probability of black spots and an improved accident frequency method to identify accident black spots and obtain the accident probability. At the same time, we propose a three-stage random regret-minimization (RRM) model to minimize the regret value of the attribute of overall response time, cost, and demand, which allocates limited emergency resources to more likely to happen accident spots. Due to the computational complexity of our model, a genetic algorithm is developed to solve a large-scale instance of the problem. A case study focuses on three-year rainy accidents’ data in Weifang, Linyi, and Rizhao of China to test the correctness and validity of the application of the model.

IDENTIFICATION OF BLACK SPOTS ON STATE HIGHWAY-75

The issue of road accidents is an increasing problem in developing countries. There are many steps required to achieve road safety measures. Road safety is necessary to reduce accident involving both human and vehicle damage. The safety deficiencies were detected to minimize accidents and to save road users. By studying the various accident parameters and by using accident rate method, accident black spots analysis is done. The data is analysed by developing regression model. Using such analysis blackspots locations were identified for further improvement in road safety and also the causes of accident are studied. For the study Sangli – Kolhapur road (SH-75) is taken having longitude and latitude for sangli (N 16.8530, E 74.5630) and for Kolhapur (N16.7290, E 74.2824).

The impact of traffic sign deficit on road traffic accidents in Nigeria

ABSTRACTThis study assesses the impact of traffic sign deficit on road traffic accidents in Nigeria. The participants were 720 commercial vehicle drivers. While simple random sampling was used to select 6 out of 137 federal highways, stratified random sampling was used to select six categories of commercial vehicle drivers. The study used qual-dominant mixed methods approach comprising key informant interviews; group interviews; field observation; policy appraisal and secondary literature on traffic signs. Result shows that the failure of government to provide and maintain traffic signs in order to guide road users through the numerous accident black spots on the highways is the major cause of road accidents in Nigeria. The study argues that provision and maintenance of traffic signs present opportunity to promoting safety on the highways and achieving the sustainable development goals.

Panorama laser lane system based IoT

Background/Objectives: Lane marking to recognize road condition helps driver safely drive. Device in this study is developed as a complement because existing lane marking is able to difficult to identify lane according to its environment.Methods/Statistical analysis: Lane marking has been built by spraying tiny particles of glass on a paint for the purpose of retro-reflection against headlamps in automobile at night. But paint with tiny particles of glass is eliminated by automobiles and function of retro-reflection tends to lost over time. That makes lanes invisible well on rainy days at night. Panorama laser lane system based on mobile network with remote control is designed to irradiate laser beam sequentially toward the road on rainy or snowy day at night for safety guidance.Findings: The system provides visible lane markings on rainy days or at night because laser beam lights the lane on the road to help avoid traffic accident.Improvements/Applications: Smart road to provide safety drive will be built by the installation of system at 20 meters in the road particularly dangerous section of road or accident black spot.

Hot spot analysis based on network spatial weights to determine spatial statistics of traffic accidents in Rize, Turkey

The increased numbers of vehicles using roads in the world today are cause of traffic-related problems, and in this respect, road traffic accidents are an important topic relating to public health. Especially on the road connecting two border provinces, traffic accidents are increasing substantially in parallel with the quantity of transport facilities. By determining areas where traffic accidents result in deaths or injuries, accident prevention strategies can be developed. This study applies the spatial statistics techniques using Geographical Information Systems (GIS) to determine the intensity of traffic accidents (hot-spot regions) over 45 km of main routes in Rize Province, Turkey. Traffic accidents recorded in data spanning 5 years are combined with a geographical dataset for evaluation using hot spot statistical analysis. Unlike other studies, this study used hot spot analysis based on network spatial weights (an innovative review in the methods of determining traffic accident hot spots: “novel application of GIScience”) to identify black spots for traffic safety. To perform the analysis using Hot Spot Analysis: Getis-Ord Gi*, a generated network dataset and the spatial weights of the road data are used to generate network spatial weights. Then, Kernel Density method is used to define traffic accident black spots. Finally, these two methods are compared each other with visually.

Adapting the FlexiArch for widening a complex arch bridge

The 1840's Teewell Hill arch bridge, in the suburbs of Bristol, UK, was no longer adequate for increasing local traffic levels and needed to be widened. Several widening options were considered and it was concluded that the innovative ‘FlexiArch’ would best accommodate the complex geometry of the existing structure while minimising social and economic impacts. In order to elegantly accommodate the raked spandrel walls of the existing bridge Macrete and WSP|Parsons Brinckerhoff worked collaboratively to produce a custom-designed, high-quality, precast concrete FlexiArch, which matched the contours of the existing bridge. As the FlexiArch system has no corrodible reinforcement, it is highly sustainable and will result in reduced maintenance, as for the existing bridge. The elimination of centring and speed of construction (hours not months) minimised disruption to road traffic and to cyclists on the cycle network below the bridge – a key project criterion required by the client. Thus, in addition to addressing an accident black spot, the FlexiArch solution provided South Gloucestershire Council (the client) with an aesthetically pleasing and fully functional solution at a competitive cost.