Accident Black Spots Research Articles

Accident Blackspots Study and Traffic Accident Costs Analysis of Highway

Accident Blackspots Study and Traffic Accident Costs Analysis of Highway

A SMART GPS-BASED IOT SYSTEM FOR BLACKSPOT NOTIFICATION

Abstract. An accident blackspot is a length of the road marked as having high road accidents potential. Various highways have become a nightmare to passengers, drivers and pedestrians in the recent past. One of the common causes of road accidents is a driver being unfamiliar with a road. Despite authorities using various methods to raise awareness of blackspots including the use speed guns detectors the rate of the accidents on the roads is still in an alarming rate. More often, signposts on the road can be vandalized or even be hit by a reckless driver. There is also an assumption that every driver reads and understands signposts. We propose an IoT system embedded on a vehicle and having ability to sense a blackspot from a given distance. After sensing the blackspot, the system then provides an audio directive that notifies a driver to be more careful and avoid overtaking. The proposed system was developed using GPS, microcontrollers, and GSM mobile technology. The system was tested using coordinates extracted from road sections marked as blackspots. The proposed system sends notifications to end users using GSM system chip.

Identification of Factors Affecting Road Traffic Injuries Incidence and Severity in Southern Thailand Based on Accident Investigation Reports

Thailand has the second-highest rates of road traffic mortality globally. Detailed information on the combination of human, vehicle, and environmental risks giving rise to each incident is important for addressing risk factors holistically. This paper presents the result of forensic road traffic investigation reports in Thailand and determines risk factor patterns for road traffic injuries. Detailed forensic reports were extracted for 25 serious traffic accident events. The Haddon matrix was used to analyze risk factors in three phases stratified by four agents. The 25 events analyzed involved 407 victims and 47 vehicles. A total of 65.8% of victims were injured, including 14.5% who died. The majority (66.1%) of deaths occurred at the scene. Human-error-related factors included speeding and drowsiness. Passenger risks included not using the seat belt, sitting in the cargo area and the cab of pickups. Overloaded vehicles, unsafe car modifications, no occupant safety equipment and having unfixed seats were vehicular risks. Environmental risks included fixed objects on the roadside, no traffic lights, no guard rails, no traffic signs, and road accident black spots. At present, traffic accidents cause much avoidable severe injury and death. The outcome of this paper identifies a number of preventable risk factors for traffic injury, and importantly examines them in conjunction. Road traffic safety measures need to consider how human, vehicle, and environmental risks intersect to influence injury likelihood and severity. The Haddon matrix is useful in identifying these pre- and post-accident risk factors. Furthermore, the sustainable preventions of road traffic injury need to address these risks together with active law enforcement.

Urban Road Accident Black Spot Identification and Classification Approach: A Novel Grey Verhuls–Empirical Bayesian Combination Method

The identification and classification of accident black spots on urban roads is a key element of road safety research. To solve the problems caused by the randomness of accident occurrences and the unclear classification of accident black spots by the traditional model, we propose a method that can quickly identify and classify accident black spots on urban roads: a combined grey Verhuls–Empirical Bayesian method. The grey Verhuls model is used to obtain the predicted/expected numbers of accidents at accident hazard locations, and the empirical Bayesian approach is used to derive two accident black spot discriminators, a safety improvement space and a safety index (SI), and to classify the black spots into two, three, four and five levels according to the range of the SI. Finally, we validate this combined method on examples. High-quality and high-accuracy data are obtained from the accident collection records of the Ningbo Jiangbei District from March to December 2020, accounting for 90.55% of the actual police incidents during this period. The results show that the combined grey Verhuls–Empirical Bayesian method can identify accident black spots quickly and accurately due to the consideration of accident information from the same types of accident locations. The accident black point classification results show that the five-level rating of accident black points is most reasonable. Our study provides a new idea for accident black spot identification and a feasible method for accident black spot risk level classification.

Identifying Expressway Accident Black Spots Based on the Secondary Division of Road Units

For the purpose of reducing the harm of expressway traffic accidents and improving the accuracy of traffic accident black spots identification, this paper proposes a method for black spots identification of expressway accidents based on road unit secondary division and empirical Bayes method. Based on the modelling ideas of expressway accident prediction models in HSM (Highway Safety Manual), an expressway accident prediction model is established as a prior distribution and combined with empirical Bayes method safety estimation to obtain a Bayes posterior estimate. The posterior estimated value is substituted into the quality control method to obtain the black spots identification threshold. Finally, combining the Xi'an-Baoji expressway related data and using the method proposed in this paper, a case study of Xibao Expressway is carried out, and sections 9, 19, and 25 of Xibao Expressway are identified as black spots. The results show that the method of secondary segmentation based on dynamic clustering can objectively describe the concentration and dispersion of accident spots on the expressway, and the proposed black point recognition method based on empirical Bayes method can accurately identify accident black spots. The research results of this paper can provide a basis for decision-making of expressway management departments, take targeted safety improvement measures.

ASI Based Identification and Prioritization of Accident Black Spot

Abstract: The number of motor vehicles on road increased spectacularly which causing major social and transportation problems such as death and economic losses. Eradication of accident blackspots is a crucial part of the road safety approach. So, this issue informed the quest to study and identify the accidental black spot in the context of NH 6. The stretch of 35 km on NH 6 near Pune was considered to identify the black spot. Accident blackspot locations were distinguished through the accident severity index (ASI) method by employing two years of accident data. These identified black spots were prioritized based on their final severity index score. The two most vulnerable black spots out of nine were selected for further analysis and countermeasure process. Conclusively, the accident severity-based framework can effectively correlate the nature of the accident with the type of casualty to provide detailed and accurate black spot location. Keywords: Black Spots, Accident Severity Index (ASI), Black Spot Prioritization, Traffic Volume, Highway, Countermeasures.

Application of geographical information system (GIS) in reducing accident blackspots and in planning of a safer urban road network: A review

“Geographic Information System” (GIS) signifies a novel model for designing and organizing the information as well as information system respectively, whose essential aspect is basically utilizing concepts of location as a basis for information system's structuring. As per the spatially distributed nature of the data which is associated to transport and the demand for several types of network-level analyses, statistical analysis, and spatial analysis and handling, the application of GIS is relevant for transportation. At the GIS platforms, the transportation network database is generally broadened through its linear referencing system by integrating several sets of its attributes and spatial data. In addition, GIS will facilitate integration with the transport network database of all other socio-economic data for the diverse planning functions. GIS also provides help to plan analyze a green environment. This paper reviews the need of analyzing the road accidents in urban areas through GIS based systems so, that we can reduce the number of accidents taking place and reducing the deaths occurring on roads due to these accidents which will be a great contribution towards the people of a country and its integrity. This paper aims to develop a base for developing a GIS based framework for analyzing the road accidents. It is also attempted through this paper to provide greater aspects for planning a greener and safer road environment.

Identification and Improvement of Accident Black Spots on SH-1 and SH-38 Dist. Khargone (Madhya Pradesh) - A Case Study

we know that as the increment in population is directly connected to increment in vehicles. And if number of vehicles increase number of accidents is also increased. The reason of accidents are road parameters or human error. We find out from the previews analysis of data that 66% accidents are occur due to human errors and 33% due to the parameters of road. We have 3.3 million km network in India which consist all types of road just like National Highway (NH), State Highway (SH),, Major District Road (MDR) and other District Road (ODR). The Road safety is major important considarable thing in present time because accident cases goes very high in past some years. So the identification of the some sites that called black spots are important for the better road safety policies. So in this thesis we taking a route of a district khargone and find out the black spots, accidents cases, reason of accident cases and after the all calculations we also give the suggestion for the improvement of road. Mandleshwar is a town and Nagar panchayat in the Khargone district of the India state of Madhya Pradesh. It is on the banks of Narmada River 8 kilometres (5.0 mi) east of Maheshwar and 99 kilometres (62 mi) south of Indore. It is a "Pavitra Nagri" as termed by the government of Madhya Pradesh, as it is an ancient town. It is the education centre of Maheshwar block, the location of the district court and district jail of Khargone, and is also the political centre of Maheshwar block In mandleshwar city district Khargone roads has major connectivity and there are not big industries but these road carries large numbers of passengers to industrial area of Indore,and other state Gujarat ,Maharashtra ,Rajasthan through state highways and district road. Mandleshwar is also connected to other state highways just like: Mandleshwar to barwaha( SH 38) 41.4 km Mandleshwar to Mhow (SH 1) 56.2 km Mandleshwar to Dhamnod (SH 38) 21.4 km Mandleshwar to Khargone (SH 1) 47.0 km This paper presents an analysis of accidents on small portion state highway city mandleshwar to connect the other cities.The data for analysis is collected for the period of 2015 to 2019.

Research on Highway Roadside Safety

Frequent and serious traffic accidents have become a focal issue because they hinder the sustainable development of society. In China, roadside accidents account for 40% of fatalities resulting from traffic accidents. Roadside safety has become an important issue of traffic management departments worldwide, and performing research on roadside safety contributes to improving the level of road safety and reducing the number of traffic accidents and fatalities. By systematically sorting a large number of relevant studies, this paper analyzed the current development trends of roadside safety in terms of three aspects (i.e., the year of publication, the country of publication, and the source of publication) and then summarized the research status, existing gaps, and future development directions of roadside safety in terms of three aspects: the frequency of roadside accidents, the severity of roadside accidents, and the practice of roadside safety design. This paper reviewed the different prediction methods and evaluation models for the frequency and severity of roadside accidents. According to the number of times mentioned in the literature, the first five significant risk factors that cause frequent roadside accidents are small-radius curves, heavy traffic, objects adjacent to the lane (such as poles and trees), narrow lanes, and narrow shoulders, and the first five significant risk factors that cause fatal roadside accidents are driver age ≤25 or ≥65, alcohol, speeding, failure to use seat belts, and heavy trucks. Future research on the frequency and severity of roadside accidents should focus on quantitatively analyzing the probability of roadside accidents and occupant injury risk and developing methods for identifying roadside accident blackspots. For roadside safety design, roadside clear zones and safety slopes should be precisely quantified based on a cost-benefit analysis in future studies.

Mapping of Blackspots Accident Area at North South Expressway to Produce Expressway Advisory System

The study was carried out to produce an advisory system based on the blackspot accidents mapping at the North-South Expressway (NSE). Blackspots are location where the road traffic crashes have historically concentrated by type of accident in which the accident caused fatalities, severe injuries, minor injuries and damage. This research aims to identify accident blackspot and plotting accident data into a digital map of the study area to produce advisory systems for highway users. The software used in this study namely Google Earth Pro, ArcMap and ArcGIS online. Results showed that blackspot location are known in the form of mapping where it can be performed online. In addition, the path with high number of accident points weightage location were identified and prioritized. By applying Google Earth Pro and ArcGIS software, the advisory system was created, and these blackspot locations can also be easily accessed by highway users. Therefore, the advisory system helps in facilitate the users for trip planning base on the accident status locations as a precautionary measure and initiative to reduce the rate of road accident in expressway.

Accident Analysis and Blackspot Identification at Chandrapur City

Increased economic activity raised the consumption levels of the people across the India. This created scope for increase in the travel and transportation demanding a greater number of vehicles. The increase in the vehicles since last ten years has put a lot of pressure on the existing roads and ultimately resulting in increased rate of accidents. Blackspots are occurred at urban areas due to increased rate of vehicles, shorter width of roads and defective geometry of intersections. Blackspots are the locations in the city where the accident rate observed to be high. Therefore, there is need identify such locations of the urban area. For the present research, Chandrapur city in Maharashtra, India was selected. Accident data of last four year (2015- 2018) was obtained from police station. Weighted severity index (WSI) method and statistical analysis is used for identifying the accident blackspots. After field observations and interaction with the public, some improvements are suggested for improving the accident blackspot.

Identification, Analysis and Suggestions for Accident Blackspots

Identification, Analysis and Suggestions for Accident Blackspots

Physiological indices and driving performance of drivers at tunnel entrances and exits: A simulated driving study.

The entrance and exit sections of a tunnel are the accident black-spots in an expressway. For a safe operation of road tunnels, it is necessary to understand a driver’s physiological indices and driving performance when driving through tunnels. In this study, the UC-Win/Road simulation software was used to build 12 tunnel models of different lengths. A simulated driving experiment was carried out in a 6-DoF motion platform. The lateral position of vehicles characterizing the driving performance was measured using the motion platform. Electrocardiogram and eye movement data of 25 recruited drivers were collected simultaneously through the experiment. The spatial changes in a driver’s heart rate (HR) growth rate, RMSSD, pupil diameter growth rate and vehicle lateral deviation within 300 m before and after the tunnel entrance and exit were analyzed to determine the variation rules in the different tunnels. The study identified the length range in the tunnel entrance and exit sections that influences the drivers. A quantitative analysis was further carried out to analyze the relationship between the physiological indices and the driving performance indicator. The results showed that a driver’s heart rate fluctuates significantly 250 m before the tunnel entrance and 50 m before the exit. In this region, the pupil diameter increases gradually, and drivers tend to shift the vehicle to the left. At the tunnel exit, the HR and RMSSD are affected significantly by the tunnel length, and the variation is higher in longer tunnels. In comparison, the tunnel length has no significant effect on the physiological indicators and driving performance of the drivers at the entrance and exit.

Retroreflection and Wettability Controlled Smart Indicator Based on Responsive Bilayer Photonic Crystals for Traffic Warning

AbstractDue to the increasing human mortality caused by natural disasters and traffic accidents, the development of smart traffic warning systems is urgent. The first smart indicator for traffic warning based on bilayer photonic crystals with both selective retroreflection and response reflection optical properties is developed. The wettability‐controlled reflection evaluates the risk of landslides and warns drivers to slow down, while the retroreflection simultaneously displays iridescent vivid structural colors to observers at different viewing directions relative to the illumination direction and informs drivers the approaching of the accident blackspot. The mechanism involves diffractions in the periodic nanostructures and uses the wettability of vertical surfaces to realize the spectral diversity of reflectance. Moreover, colorful flickering structural colors based on the rotational periodic diffraction and angle dependence of the retroreflection are designed for speed limits warning. It demonstrates an energy‐efficient, smart, and tunable multi‐functionalized bilayer photonic crystal that is especially suitable for nighttime traffic warning, advertisement, and periodic warning display applications.

Road Accidental Analysis and Identify the Black Spot

The increase in motor vehicles accompanied with expansion of roads has brought with it the challenge of addressing the adverse influence of road traffic accidents. Road incidents are a nationwide cataclysm with ever increasing trend which incurs a human health and development challenge and highly affect the human capital development of every nation. A mortality rate database (WHO, 2002) assures that India has highest number of accidents. Accident Black Spots are those locations on the road which have higher severity of accident either in terms of numbers or in terms of injuries. In this paper, efforts have been made to identify the location of accident black spots on national highway from singhpora pattan to panthachowk srinagar.The present era accidents are contributing major deaths worldwide due to increase in vehicular traffic.It has been estimated that every year 1.38 lakh lose their limbs and 6 lakh people get injured in road accidents in india.our country incurs a loss of 58000 crore each year due to road accidents. The most used roads to travel from jalandhar in punjab to uri in kashmir is NH-1A.The old name for NH-44 is NH-1A which was replaced after renumbering of all national highways in year 2010. In this report,the analysis includes study of some major accident spots or busy intersections in the NH-1A in between Pantha chowkh Srinagar to Singhpora Pattan.The study includes visiting sites,collecting required data for analysis and derives the results and remedies from what we had got. This report will be containing the material surveyed,pictures of road at different locations ,road details,figures,material for understanding of the unknown and finally the conclusion

Identification of Road Accident by using Black Spot Method Between Panthaghati to Dhalli Road

In India road accidents are very serious problem because of large population and high traffic density of vehicles. Most of the road accidents occur mainly due to the negligence of driver and poor infrastructure only a few accidents occur due to the technical error of vehicles. The main purpose of this research paper is prevention of road traffic accidents and improvement of road safety in Shimla. Road safety is very important aspect of today’s life, so it is important that everybody should aware about road safety. To do this study a section of 12km length is chosen between Panthaghati to Dhalli in district Shimla on NH 5 where accidents black spots are identified for the section by analyzing secondary data used to prevent road accidents. In this study primary data is used for observing the road conditions and secondary data is used to find accidents black spot. Black Spot is a point or a place on the road where road accident occurs repeatedly one after another which is known as accident black spot. To identify these black spots we use weighted severity index (WSI) method. It is one the most reliable and effective method for determining the most proven accidents black spots. Shimla is a hilly area and it has narrow roads, blind curve and black spots which increase the chances of road traffic accidents. In past recent years road traffic accidents are increasing in Shimla and this study deals with identification of major issues causing road traffic accidents. This research paper helps to improve the road safety in Shimla because in this study the analysis has been done to identify the major problems responsible for gradually increasing road accidents. This research paper is also used in future research paper as reference purpose and it will also provide an overview to other researchers who want do their research on similar kind of topics.

Accident Hotspot Identification on the Midnapore Kharagpur Development Authority Planning Area

Road accidents are a vital problem in our country for various reasons. According to WHO reports, approximately 1.25 million people died each year, and more than 50 million people injured in road accidents all over the world. Road accident is mostly human-made, and it’s affecting your life negatively. Regarding, many studies or research has been performed to reduce road accident and identify the accident blackspot. This paper represents a methodology to find out the accident-prone zone, estimation of Kernel Density and black Site & black Spot identification of major roads Medinipur and Kharagpur development Authority (MKDA) planning area using of Geographical Information Systems (GIS). For this study, road accident data collected from Paschim Medinipur Kotwali Police station from 2016 to 2019. A kernel density estimation was created to identify black spots & black sites of the study area. Based on the result, suggestions are provided to improve the situation in the future.

Accident Black Spot Detection on Greater Mumbai Region

Traffic accident Black Spot detection on urban settlement using unsupervised learning algorithms and accident analysis using statistical methods on Greater Mumbai Region is carried out in this study. At present the traffic accident analysis in Mumbai is a manual process. Through the clustering analysis on Mumbai accident data from January 2018 to September 2019, we put forward a method to detect accidental blackspots effectively using DBSCAN algorithm.

Probability of Roadside Accidents for Curved Sections on Highways

To predict the probability of roadside accidents for curved sections on highways, we chose eight risk factors that may contribute to the probability of roadside accidents to conduct simulation tests and collected a total of 12,800 data obtained from the PC-crash software. The chi-squared automatic interaction detection (CHAID) decision tree technique was employed to identify significant risk factors and explore the influence of different combinations of significant risk factors on roadside accidents according to the generated decision rules, so as to propose specific improved countermeasures as the reference for the revision of the Design Specification for Highway Alignment (JTG D20-2017) of China. Considering the effects of related interactions among different risk factors on roadside accidents, path analysis was applied to investigate the importance of the significant risk factors. The results showed that the significant risk factors were in decreasing order of importance, vehicle speed, horizontal curve radius, vehicle type, adhesion coefficient, hard shoulder width, and longitudinal slope. The first five important factors were chosen as predictors of the probability of roadside accidents in the Bayesian network analysis to establish the probability prediction model of roadside accidents. Eventually, the thresholds of the various factors for roadside accident blackspot identification were given according to probabilistic prediction results.

Prioritization of Accident Black Spots using GIS

Prioritization of Accident Black Spots using GIS