Accident Hotspots Research Articles

Will vehicle data be shared to address the how, where, and who of traffic accidents?

Vehicles are increasingly equipped with sensors that measure the state of the vehicle and surrounding road users. Although most of these sensor data currently remain local to the vehicle, the data could be shared with the aim to improve road safety. We postulate that there is a range of scenarios regarding data sharing, with two extremes: In scenario 1, the acquired shared data will be analysed regarding the how, where, and who of road traffic errors, violations, and accidents; actions can then be taken to improve automated driving systems, manage accident hotspots, and provide personalised feedback, rewards, or penalties to road users. In scenario 2, the recorded data will not be shared, because of privacy concerns. We conclude that there exists a tension between a position of utilitarian use of data and a position of privacy.

Road Accident Mapping along Halsema Highway

This paper showcases the profile of the road accident rate that happened along the Halsema Highway and the determination of road accident hotspots through QGIS mapping. This research used the qualitative research design and descriptive method using documentary analysis to determine the road accident rate profiles. On-site observation was conducted to describe the physical set-up of the accident prone areas; while interviews with police officers, commuters and drivers were conducted to supplement the findings of the study. It was found that most road accidents happen during the 2 nd quarter of the year. More accidents happen during weekends between 12:00 at noon and 4:00 in the afternoon. It was also revealed that vehicle-to-vehicle is the most common type of collision. Looking at the accident map, Usok Area, Amlosong, Balili, SitioPalinga-aw, Namatec, Mount Data Cliff and Mount Data Sawmill are identified as accident prone areas. The findings indicate that road accidents choose no place, weather or person. It happens at any time regardless of vehicle type. https://doi.org/10.26803/ijhss.11.1.3

Evaluation of various GIS-based methods for the analysis of road traffic accident hotspot

In order to establish objective criteria for road traffic accident (RTA) hotspots, this paper examines the application of three different hotspot analysis methods to both identify and rank the RTA hotspots. The three methods selected are the network Kernel Density Estimation (KDE+) method, the Getis-Ord GI* method, and a recently proposed risk-based method that accounts for RTA frequency, severity and socioeconomic costs - STAA method. The study road, Jalan Tutong, is a major dual-carriageway connecting major residential and commercial areas from the west of Brunei-Muara district and beyond to the capital, Bandar Seri Begawan. The RTA data consists of cases reported to the police during a 5-year period from 2012 to 2016. The RTA data were digitised and prepared, before being imported into ESRI ArcGIS 10.2 software for analysis using each of these methods. The outcomes, particularly the location, extent and priority of the RTA hotspots, are subsequently compared to results from road safety audits, in order to determine the relative merits and drawbacks of each method. The findings from the comparative study would be useful to recommend the most suitable method to identify and rank the RTA hotspots for the study road.

The Analysis of Spatial Pattern and Hotspots of Aviation Accident and Ranking the Potential Risk Airports Based on GIS Platform

Aviation accident analysis is an important task to ensure aviation safety. The existing researches mainly focus on the analysis of aviation accident time characteristics and accident causes and less analysis of the spatial characteristics of aviation accidents. The spatial characteristics analysis of aviation accidents can identify hot spots of aviation accidents, improve the accuracy of aviation accident emergency management, and provide decision support for airport route planning. This study established the severity index of aviation accident based on aviation accident data, using GIS spatial analysis methods to study the spatial distribution characteristics of aviation accidents. The hot spots were identified in the aviation accidents. Finally, airports around the accident hot spots were ranked to obtain the airports with high potential aviation risks based on RI, taking Florida as an example. It was found that in the Florida aviation accident, general aviation accidents accounted for the majority, but the aviation accident severity index for air route flight was far greater than general aviation accidents. From the spatial distribution point of view, accidents with high severity index were distributed around large international airports. The Density Center for Aviation Accidents was located in Tampa, Miami, and some airports link areas in Florida. In terms of the Moran’s I index, the distribution of aviation accidents tended to aggregate in the region as a whole. However, aviation accident severity index was randomly distributed for each year separately. At the level of significance of 0.01, there were a total of 75 accident hotspots in the Florida region, mainly in the north and southwest. Airports with high RI in the Florida area were mainly concentrated in the Miami area and the Tampa Bay area, and Orlando Airport was ranked outside the top 10.

Epidemiological Study of Road Traffic Accidents and Detection of Accident Hot Spot in Golestan Province, Northern Iran

Epidemiological Study of Road Traffic Accidents and Detection of Accident Hot Spot in Golestan Province, Northern Iran

Analysis and Assessment of Lithuanian Road Accidents by AHP Method

Lithuanian road accidents were evaluated based on the geographic information systems and multi-criteria method of Analytical Hierarchy Process This paper presents the methodology for selecting and ranking high accident concentration sections on the roads of national significance. Methodology involves the following process phases: 1) preparation of spatial data of the road accidents; 2) estimation of road sections with a high accident rate; 3) calculation of spatial statistics for estimation of accident points and hot spots; 4) selecting indicators for multi-criteria assessment; 5) calculation by Analytical Hierarchy Process method and ranking the selected high accident concentration sections. Assessment of spatial clustering of accidents and hot spots was carried out following geo-information technologies and using Getis-Ord Gi statistics and point density functions. This geospatial criterion was integrated into multicriteria assessment for ranking the high accident concentration sections by using the Analytical Hierarchy Process method. Presented method is useful for various agencies in order to improve their planning and management strategies for better traffic conditions as well as to reduce the number of accidents. The result of the research presents selection methodology of dangerous accident section and ranking of the tenth the most dangerous sections involving geographic information systems and Analytical Hierarchy Process method.

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.

Assessment of roe deer (Capreolus capreolus L.) – vehicle accident hotspots with respect to the location of ‘trees outside forest' along roadsides

Animal-vehicle collisions (AVCs) pose a serious threat to human and animal welfare, and result in increasing costs for society. Mitigation efforts have been in the focus of research for decades but have revealed only few generalities on where and why AVCs occur. Uncertainty therefore remains on how to make decisions regarding nature conservation, wildlife and transportation management. In our study, we used GPS data on almost 1000 AVCs between October 2014 and October 2016 involving roe deer (Capreolus capreolus L.) in the administrative district of Göttingen, Germany, to identify accident hotspots based on Kernel Density analysis. We then used information from a mapping campaign of trees outside forests (TOF), including hedges, bushes, groves, isolated trees and other non-forest vegetation to investigate whether TOF abundance is larger near accident hotspots when compared to areas showing no hotspots (assumed as reference area). We found that near hotspots, TOF are significantly more abundant than in the remaining reference area. We conclude that future transportation management should consider TOF management as a possible indicator for AVCs.

Application of Fractal theory for crash rate prediction: Insights from random parameters and latent class tobit models

The repercussions from congestion and accidents on major highways can have significant negative impacts on the economy and environment. It is a primary objective of transport authorities to minimize the likelihood of these phenomena taking place, to improve safety and overall network performance. In this study, we use the Hurst Exponent metric from Fractal Theory, as a congestion indicator for crash-rate modeling. We analyze one month of traffic speed data at several monitor sites along the M4 motorway in Sydney, Australia and assess congestion patterns with the Hurst Exponent of speed (Hspeed). Random Parameters and Latent Class Tobit models were estimated, to examine the effect of congestion on historical crash rates, while accounting for unobserved heterogeneity. Using a latent class modeling approach, the motorway sections were probabilistically classified into two segments, based on the presence of entry and exit ramps. This will allow transportation agencies to implement appropriate safety/traffic countermeasures when addressing accident hotspots or inadequately managed sections of motorway.

Assessing accident hotspots by using volunteered geographic information

Assessing accident hotspots by using volunteered geographic information

Preventing traffic accidents with in-vehicle decision support systems - The impact of accident hotspot warnings on driver behaviour

Despite continuous investment in road and vehicle safety, as well as improvements in technology standards, the total amount of road traffic accidents has been increasing over the last decades. Consequently, identifying ways of effectively reducing the frequency and severity of traffic accidents is of utmost importance. In light of the depicted challenge, latest studies provide promising evidence that in-vehicle decision support systems (DSSs) can have significant positive effects on driving behaviour and collision avoidance. Going beyond existing research, we developed a comprehensive in-vehicle DSS, which provides accident hotspot warnings to drivers based on location analytics applied to a national historical accident dataset, composed of over 266,000 accidents. As such, we depict the design and field evaluation of an in-vehicle DSS, bridging the gap between real world location analytics and in-vehicle warnings. The system was tested in a country-wide field test of 57 professional drivers, with over 170,000km driven during a four-week period, where vehicle data were gathered via a connected car prototype system. Ultimately, we demonstrate that in-vehicle warnings of accident hotspots have a significant improvement on driver behaviour over time. In addition, we provide first evidence that an individual's personality plays a key role in the effectiveness of in-vehicle DSSs. However, in contrast to existing lab experiments with very promising results, we were unable to find an immediate effect on driver behaviour. Hence, we see a strong need for further field experiments with high resolution car data to confirm that in-vehicle DSSs can deliver in diverse field situations.

Network-Based Point Pattern Analysis of Bicycle Accidents to Improve Cyclist Safety

Examining the spatial distribution of bicycle accidents under different conditions and in different periods is an important issue for increasing cyclist safety. A point pattern analysis methodology of 1,437 bicycle accidents that resulted in injury or death in the city center of Vienna, Austria, between 2012 and 2014 is described. Network-based kernel density estimation was used to examine the hot spots of bicycle accidents, and the network-based nearest-neighbor distance was taken into account to check the significance of the hot spots. Moreover, the global cross nearest-neighbor distance was used to test the effect of urban components on the distribution of bicycle accidents. An understanding of the temporal and conditional differences was obtained by analyzing the accident data in terms of four classifications: all accident data and then the accident data classified according to season, light conditions, and precipitation conditions. It was concluded that the bicycle accident hot spots varied in space according to season, light, and precipitation conditions. Also, these detected hot spots were significant for the pattern of accidents, no matter what classification was used. Besides these points, at the .95 confidence level, bicycle accidents tended to cluster by signalized intersections, bus and tram stations, subway stations, and city bike stations. As a result, a systematic framework was proposed for spatiotemporal analysis of bicycle accidents for the built environment. The framework can serve as a guide to determine effective strategies for cyclist safety in urban areas.

Applying GIS to Identify the Spatial and Temporal Patterns of Road Accidents Using Spatial Statistics (case study: Ilam Province, Iran)

Abstract The number of fatalities and casualties caused by road accidents is mostly affected by the 3 factors of road, human, and vehicle. Nowadays, tackling with the accident prone locations including the definition, identification, and modification prioritization has attracted attention as an approach to enhance and improve the roads network safety level. A road accident analysis method is through the use of the Geographic Information System (GIS) and spatial and temporal patterns in accident prone locations. Since accidents are temporal phenomena, in this paper, GIS-based spatial statistical methods have been used to identify and model accident hot spots; in other words, we have investigated the use of localization patterns and hot spot distribution with the help of temporal information.Hot spot analysis with identification and data generation helps decision makers to take appropriate measures to decrease road accidents. To specify and analyze accidents distribution, the information regarding the accidents in the roads of Ilam Province (Iran 2013), has been investigated. Information included the accident type (fatality, injury).From the hot spot map, it is concluded that in northwest roads, despite less traffic, the number (spatial weight) of fatalities is more.This can be due to such factors as the route geometrical design, lack of appropriate relief, and so on. To identify the temporal patterns and accidents distribution, and also analyze the hot spots, Moran's method of spatial autocorrelation and Getis - OrdGi * statistic have been used.

A Novel Approach for Identification and Ranking of Road Traffic Accident Hotspots

Road Traffic Accidents (RTA) are known to be one of the main causes of fatalities worldwide. One usef ul approach to improve road safety is through the identification of RT A hotspots along a road, so they can be prioritised and treated. This paper introduces an approach based on Geographical Information System (GI S) to identify and prioritise RTA hotspots along a road network using historical RTA data. One particular urban road in Brunei with a historically high rate of RT As, Jalan Gadong, was selected as a case study. Five years of historical RTA data were acquired from the relevant authorities and input into a GIS database. GI S analysis was then used to identify the spatial extension of the RT A hotspots. The RT A hotspots were ranked according to three different schemes: frequency, severity and socio-economic impact of RTAs. A composite ranking scheme was also developed to combine these schemes; this enabled the prioritisation and development of intervention and maintenance programmes of the identified RTA hotspots. A visualisation method of the RTA spatial distribution within each identified RTA hotspot was also developed to determine the most risky road stretches within each hotspot, which is important for treatment prioritisation when limited resources are available.

ANALYSIS OF ROAD TRAFFIC ACCIDENTS IN ANTALYA PROVINCE (TURKEY) USING GEOGRAPHICAL INFORMATION SYSTEMS

ABSTRACT In Turkey, about 4000 people each year die in traffic accidents and about 200,000 people each year are injured. For this reason, this problem of road traffic in Turkey is found a solution to an urgent. In this study, in ensuring of security of road traffic has been intended to introduce of the importance of Geographic Information Systems (GIS). Antalya Province where is one of Turkey's major metropolises have the problem of heavy traffic. In this study has been made general statistical analysis in a GIS environment of road traffic accidents occurred in 2009 and 2010 years in Antalya Province Center. Also, traffic accidents has been analyzed by correlating environmental factors with parameters situated in a traffic accident data set. In the beginning of this study, by using ArcMap-10 software, the satellite map of Antalya Province has been digitized. A database has been created with Arcmap-10 software using fatal-injury traffic accident datas occurred in Antalya City Center between 2009 and 2010 years. Fatal-injury traffic accidents on Intersection and boulevard-highroad-street have seperated of each other and by using accident datas ( accident day, time interval of accident, occurrence type of accident, accident character, the number of dead, the number of injured ) belonging to accidents occurring in each one, on a satellite map in the ArcMap-10 software of accidents were conducted the statistical evaluations in both visual and graphics. In GIS, the regions where have intensive of intersection accidents have been determined. As a result, at the intersections in Antalya Province Center, it was identified 41 accident hot spots for the year 2009, and was identified 57 accident hot spots for the year 2010. A total of 23 intersections were defined as accident hot spot in both years. Also, on boulevards-highroads-streets on the satellite map in Arc-Map-10 software has been determined 29 accident-prone regions as the years.

A new integrated GIS-based analysis to detect hotspots: A case study of the city of Sherbrooke

This paper proposes a two-step integrated method for identifying traffic accident (TA) hotspots on a roadway network. The first step includes a spatial analysis method called network kernel density estimation (KDE). The second step is a network screening method using the critical crash rate, which it described in the Highway Safety Manual (HSM). The method was examined by using three years of TAs (2011–2013) in Sherbrooke, Canada. The network KDE uses TAs to graphically display sites with a high crash density. Two different crash patterns were used for identifying these locations: (1) a crash pattern that includes three-year aggregated crash data, and (2) a crash pattern that involves three-year merged crash data. The results of the two crash patterns were evaluated based on a prediction accuracy index (PAI). It was found that the results obtained from the merged crash data outperformed the other. On the other hand, crash clustering in a site does not imply a site is hotspot and it is better to tested by other factors. High crash density locations were then tested by the critical crash rate, which helps to create an accurate comparison of sites. The importance of the critical crash rate is that it takes several factors into account such as the amount of exposure, the type of intersection, variance in crash data, etc. We realized that the hotspots determined using the two methods reflect very problematic locations and filter out the locations that do not have a problem. This approach could help transportation authorities and safety specialists to identify and prioritize sites that require more safety attention.

축광노면표시 시인성 및 설치규격개선 효과분석: 고속도로 사고다발구간을 중심으로

야간 혹은 우천시에 노면표시의 시인성이 저하되어 교통안전에 위협을 초래하고 있는 가운데, 본 연구에서는 최근 소개된 차선도색 장비 성능개량에 따른 노면표시 수명 연장(1.5배), 축광차선도료를 배합한 야광 노면표시의 시인성 개선(32.7%), 인간공학적 검토를 통한 고속도로 차선도색 설치규격 개선안(도색길이 6m, 빈길이 12m, 차선폭 13cm: 도색면적 27.8% 절감)을 도입하였을 때 종합적인 소요비용 및 사고절감 편익을 고려한 경제성분석을 통해 도입타당성을 제시하고자 하였다. 기존 문헌을 바탕으로 노면표시 시인성 32.7% 개선에 따라 26.9%의 사고감소 효과가 있는 것으로 나타났으며, 이에 대한 고속도로 교통사고 절감편익은 125억원으로 산정되었다. 야광차선 도입 및 설치규격 개선에 따른 총비용 증가분은 306억원으로 고속도로 일괄 도입시 경제성(비용-편익비 0.41) 확보가 어려운 것으로 나타났다. 야간 교통사고가 잦은 상위 20개 구간을 대상으로 각 구간별 경제성 분석을 수행한 결과, 구간에 따라 비용-편익비가 0.67~4.20으로 전체 20개 구간 중 11개 구간에서 경제성이 확보되어, 특히 지방부 고속도로 사고다발구간에 야광 노면표시 및 설치 규격 개선을 우선적으로 적용시 사고 감소에 따른 경제성이 존재하는 것으로 확인되었다. 연구의 한계와 향후 연구주제가 함께 논문의 말미에 토의되었다. Visibility of lane marking is impaired at night, or in case of rain, and thereby threatens traffic safety. Recently, various studies and technology have been developed to improve lane marking visibility, such as the extension of lane marking life expectancy (up to 1.5 times), improvement of lane marking equipment, improvement of lane marking visibility (32.7%) by applying phosphorescent material mixed paint, and expressway lane design standards alternative (length 6m, gap 12m, width 13cm: 27.8% of painted surface are reduced) adoption with ergonomic analysis. In this study, a set of cost-benefit analyses was performed for the cases where such techniques were applied. Based on the literature review, 26.9% of traffic accidents would be prevented by improving lane marking visibility by 32.7%; accident reduction benefit was calculated as much as 12.5 billion KRW. Meanwhile, total increased cost when introducing phosphorescent material mixed paint and lane design standards alternative is calculated as 30.6 billion KRW. However, economic feasibility could not be secured with 0.41 of cost-benefit ratio when applied to the expressway network as a whole. Additionally, cost-benefit (B/C) analysis was applied to each of the top 20 night accident hot spots and the results of B/C ratios were between 0.67 and 4.20, showing that 11 out of 20 spot sections of expressway can have economic feasibility. This results indicate, with this kind of pavement marking applied to accident hot spots in rural expressway, better visibility and economic feasibility can be guaranteed through traffic accident reduction. Some limitations and future research agenda have also been discussed.

Microscopic analysis of traffic behavior at unsignalized intersections in developing world

Unsignalized intersections in India are uncontrolled, and are characterized by chaotic traffic situation and have become accident hot spots. In this study, we have collected traffic data at three uncontrolled intersections, one each is from city center (Type I), suburb (Type II), and outskirt (Type III). Traffic parameters, such as traffic composition, speed variations, lane distribution, trajectories, conflict points, and pedestrian movements are analyzed. All the vehicle classes prefer inner lane, except auto-rickshaws. The speed on inner lane is higher than outer lane vehicles as latter are affected by the roadside friction. It is also found that the minor approach vehicles have to slow down or stop many times. Trajectories of two-wheelers are found to be much flatter than the trajectories in the standard conflict point diagram. This study has great potential in assessing the performance and safety of unsignalized intersections in developing world.

Risk management framework for safe transportation of petroleum products in Nigeria: Learning from past accidents and good practices

Many accidents involving trucking petroleum products by road in Nigeria have been associated with consequential effects on human safety and the environment. Such accidents have exposed the lack of accident risk management structure in Nigeria. Following an initial analysis of regulatory framework for downstream petroleum activities and a scoping exercise with sampled stakeholders in Nigeria, we conducted 19 semi-structured interviews and collected 2318 accident reports involving truck tankers, which we used for accident risk analysis. Analysis of accident reports shows that 79 per cent of the accidents were caused by human factors such as dangerous driving and speed violation. Accidents were categorised based on reported fatalities. Most of the accidents with deaths had 1–5 fatalities, with approximate cumulative frequency of 0.89. Accident hotspots across Nigerian states were identified. Thematic interview analysis reveals a number of contributory factors to regulatory deficit including limited regulatory resources, poor accident reporting and investigating culture, corruption and rent seeking culture. A risk management framework was therefore proposed. The framework is designed to be utilised by both regulators and operators in Nigeria and adheres to principles of commitment to change, collaboration, organisation and communication, enhancing knowledge of hazards and risks, and continues improvement.

Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway

Analyzing the pattern of traffic accidents on road segments can highlight the hazardous locations where the accidents occur frequently and help to determine problematic parts of the roads. The objective of this paper is to utilize accident hotspots to analyze the effect of different measures on the behavioral factors in driving. Every change in the road and its environment affects the choices of the driver and therefore the safety of the road itself. A spatio-temporal analysis of hotspots therefore can highlight the road segments where measures had positive or negative effects on the behavioral factors in driving. In this paper 2175 accidents resulted in injury or death on the South Anatolian Motorway in Turkey for the years between 2006 and 2009 are considered. The network-based kernel density estimation is used as the hotspot detection method and the K-function and the nearest neighbor distance methods are taken into account to check the significance of the hotspots. A chi-square test is performed to find out whether temporal changes on hotspots are significant or not. A comparison of characteristics related driver attributes like age, experience, etc. for accidents in hotspots vs. accidents outside of hotspots is performed to see if the temporal change of hotspots is caused by structural changes on the road. For a better understanding of the effects on the driver characteristics, the accidents are analyzed in five groups based on three different grouping schemes. In the first grouping approach, all accident data are considered. Then the accident data is grouped according to direction of the traffic flow. Lastly, the accident data is classified in terms of the vehicle type. The resultant spatial and temporal changes in the accident patterns are evaluated and changes on the road structure related to behavioral factors in driving are suggested.