Accident-prone Locations Research Articles

Sistem Informasi Geografis Pemetaan Titik Daerah Rawan Kecelakaan di Sumatera Barat Berbasis Web

Geographic Information System (GIS) is a system designed to work with spatially coordinated data or geographical coordinates. Based on West Sumatra Police Traffic data, there are 48 points of accident-prone areas in West Sumatra. This leads to the need for an accident-prone SIG in West Sumatera that is web-based to describe the map of vulnerable area locations along with the required information. Disaster information system appli-cations using this web using JavaScript and PHP programming language, and using MySql database and Google Maps Fire. The method used in this research is the method used by using. (UML) with stages consisting of system, system analysis, system design, and system implementation. The end result of this research is the creation of web-based GIS application that can present accident-prone location information in West Sumatra online and can be accessed easily.

Quantifying drivers’ visual perception to analyze accident-prone locations on two-lane mountain highways

Owing to constrained topography and road geometry, mountainous highways are subjected to frequent traffic accidents, and these crashes have relatively high mortality rates. In middle and high mountains, most roads are two-lane highways. Most two-lane mountain highways are located in rural areas in China, where traffic volume is relatively small; namely, traffic accidents are mainly related to the design of roads, rather than the impact of traffic flow. Previous studies primarily focused on the relationship between actual road geometry and traffic safety. However, some scholars put forward that there was a significant discrepancy between actual and visual perceived information. Drivers greatly depend on what they perceived by their vision to determine driving behavior. Thus, in this paper drivers’ visual lane model was established to quantify drivers’ visual perception. To further explore drivers’ perception of horizontal and vertical alignments, the visual lane model was projected onto horizontal and vertical planes in drivers’ vision respectively. The length and curvature of the visual curve were extracted as shape parameters of drivers’ visual lane models. Real vehicle driving tests were conducted on typical two-lane mountain highway sections of G318 in Tibet, China. Then the differences of visual perception at black spots and accident-free locations were analyzed and compared. In horizontal and vertical projections of visual lane model, there were 9 shape parameters have significant differences between accident-prone and accident-free locations. A probabilistic neural network (PNN) was formed to identify accident-prone locations on two-lane mountain highways. This study will lay a foundation for the improvement of traffic safety on mountain highways based on the quantification of drivers’ visual perception, during the phase of both road design and reconstruction, and can also make a contribution to the automatic driving technique.

Choosing safe and suitable bicycle routes to integrate cycling and public transport systems

Bicycle utilization has become an important transportation mode in various cities during the past decade. It has brought a rapid growth as an important transport mode in many countries by providing several benefits for traffic, health and cost. However, the growing in bicycle utilization demand causes many problems such as safety, effectiveness etc. For this reason, other effective parameters on urban transport should be considered when planning new safe bicycle paths in the spatial platform. In this study, it was aimed to examine all these negative and positive effective parameters on safety and choice while integrating cycling with a public transport (bus link) system. To determine effective parameters on route choice, a Questionnaire Survey (QS) was carried out on 460 participants who are cycling for a long time in Isparta City/Turkey. It was obtained from the QS that Accident Prone Areas (APA) are the most important factor on cycling and public transport integration. The other parameters are determined as Bus Lane (BL), Road Side Car Park (RSCP), Bicycle Parks (BP), Road Grade (RG), Signalization (S), Traffic Capacity (TC), Connected Bike Lane (CBL) and Separated Bike Lane (SBL) according to their importance. In order to determine safer and serviceable bicycle routes selection problem, accident-prone locations (using Accident Reports and Geographic Information Systems-GIS), bus lines and survey data has been used in GIS. Also, a multi-criteria decision making approach Analytic Hierarchy Process (AHP) method is implemented to analyze survey data and decide effective parameters on safer and serviceable bicycle routes. It was found that safer and serviceable bicycle routes can be determined by the utilization of QS, GIS and AHP methods with together. Also, it was concluded that Accident Prone Areas, Bus Lanes and Road Side Car Parks are the most important factors while integrating cycling with a public transport system.

Fatalitas dan analisis apasial lokasi rawan kcelakaan lalu lintas di kabupaten Gunungkidul

Fatality and spatial analysis of road traffic accident in Gunung KidulPurposeThis study is aimed to conduct further analysis of road accidents fatality and accident-potential area using geographic information system.MethodsThis was an analytic observational study using cross-sectional study design. Samples are all traffic accident recorded in police departement accident registry from January 1st to December 31st 2015. Data will be analyzed using poisson regression with robust variance and accident location will be analyzed spatially using ArcGIS 10.4 software.ResultsThe result revealed that 06.00-11.59 a.m (PR 0.31, 95% CI 0.144-0.687) or 12.00-17.59 p.m (PR 0.40; 95% CI 0.184-0.865), uphill roadway geometric (PR 2.16, 95% CI 1.144-4.094) or winding roadway (PR 1.80, 95% CI 1.013-3.213) and single accident type (PR 3.59; 95% CI 1.953-6.592) were significant factors affecting road traffic fatalities. Accident-prone locations to traffic accidents in Gunungkidul are clustered on several streets, such as Yogyakarta-Wonosari Street, Karangmojo-Semin Road, Wonosari Semanu Street and Wonosari Baron Street..ConclusionsFatal traffic accident in Gunungkidul are influenced by environmental conditions and accident types. Multisectoral coordination was needed to improve intervention to population at risk and stakeholders need to make efforts to modify the environment related to road geometric conditions to minimize the occurrence of accidents.

Black Spot and Accidental Attributes Identification on State Highways and Ordinary District Roads Using Data Mining Techniques

Road traffic accident is a causative aspect and a particular instance of traumatic event that constitute major harm to life and property. Therefore vaticinating the cause of occurrence of accident related information on roads is necessary. Statistical and empirical analysis on State Highways (SH’s) and Ordinary District Roads (ODR’s) accidental datasets has been performed. The need of the study is to scrutinize the traffic related dataset through Exploratory Visualization Techniques, K-means and KNN Algorithms using RStudio. This paper presents results by comparing the above said three mining techniques and predicts the cause of accident, accident prone location, analyze the time of accident, examine the reason of accident and anatomize the accused vehicle.

Non-stationary concept of accident prediction

This paper aims to develop a model to improve road safety based on the spatial and temporal features of accident occurrence. The spatial distribution characteristic of an accident follows a probability density function (PDF) that can be estimated with parametric functions. Estimating the PDF of accidents can help road specialists recognise accident-prone locations and improve road safety through countermeasures. In this study, the PDF of accidents is estimated using Gaussian mixture models. Gaussian models possess local and density information about accident distributions. For the prediction step, the parameters of the Gaussian models are predicted by means of a recursive least-squares approach. The major feature of this study is that the proposed model can accurately predict physical points where accidents are more likely to take place in the upcoming year. Moreover, because of the non-stationary feature of accidents, the proposed model is online, implying that it would update itself annually. The range of errors obtained from the results of cross-validation with existing data provides a very good indication of the accuracy of the proposed model.

Geometric and Road Environmental Effects against Total Number of Traffic Accidents in Kendari

From the large number of traffic accidents that occurred, the carrying of Kendari as the biggest contributor to accidents in the Southeast. The number of accidents in Kendari row since 2011 was recorded at 18 accidents due to the influence of geometric road, in 2012 registered at 13 accident and in 2013 amounted to 6 accidents, with accident data because of the influence Geometric recorded for 3 consecutive years the biggest contributor to accidents because of the influence of geometric is Abeli districts. This study aimed to determine the road which common point of accident-prone (Black spot) in Kecamatan Abeli as accident-prone areas in Kendari, analyze the influence of geometric and road environment against accidents on roads in Kecamatan Abeli, provide alternative treatment based on the causes of accidents on the location of the accident-prone points (blackspot) to reduce the rate of traffic accidents. From the results of a study of 6 curve the accident-prone locations, that the curve I, II, and VI is the “Black Spot” influenced by the amount and condition of traffic accidents, while at the curve II, a traffic accident that occurred also be caused by unsafe geometric where the type of geometric should be changed from Spiral-Spiral type to Spiral-Circle-Spiral type. This indicates geometric effect on the number of accidents.

A GIS Approach to Evaluate Infrastructure Variables Influencing the Occurrence of Traffic Accidents in Urban Roads

Several studies worldwide have been developed that seek to explain the occurrence of traffic accidents from
 different perspectives. The analyses have addressed legal perspectives, technical attributes of vehicles and
 infrastructure as well as the psychological, behavioral and socio-economic components of the road system
 users. Recently, some analysis techniques based on the use of Geographic Information Systems (GIS) have
 been used, which allow the generation of spatial distribution maps, models and risk estimates from a spatial
 perspective. Sometimes analyses of traffic accidents are performed using quantitative statistical techniques,
 which place significant importance on the evolution of accidents. Studies such as those in references have
 shown that conventional statistical models are sometimes inadequate to model the frequency of traffic
 accidents, as they may provide erroneous inferences. GIS approach has been used to explore different spatial
 and temporal visualization technologies to reveal accident patterns and significant factors relating to vehicle
 crashes, or as a management system for accident analysis and the determination of hot spots. This paper
 examines the relationship between urban road accidents and variables related to road infrastructure,
 environment and traffic volumes. Some accident-prone sections in the city of Kocaeli are specifically identified
 by GIS tools. Urban road accidents in Kocaeli are a serious problem and it is believed that accidents can be
 related to infrastructure characteristics. The study aimed to establish the relationship between urban road
 accidents and the road infrastructure variables and revealed some possible accident prone locations for the
 period of 2013 and 2015 in Kocaeli city.

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.

Safety Design on Small Radius Curve of Mountain Road

Bend of small radius curve is accident-prone locations. Taking small radius curve as research object, by researching on traveling track and design of widening small radius curve, super elevation, center line as well as security problems of traffic facilities, puts forward measures on safety design on small radius curve of mountain road.

Analysis of Intra-Urban Traffic Accidents Using Spatiotemporal Visualization Techniques

Abstract Road traffic accidents (RTAs) rank in the top ten causes of the global burden of disease and injury, and Iran has one of the highest road traffic mortality rates in the world. This paper presents a spatiotemporal analysis of intra-urban traffic accidents data in metropolitan Shiraz, Iran during the period 2011-2012. It is tried to identify the accident prone zones and sensitive hours using Geographic Information Systems (GIS)-based spatio-temporal visualization techniques. The analysis aimed at the identification of high-rate accident locations and safety deficient area using Kernel Estimation Density (KED) method. The investigation indicates that the majority of occurrences of traffic accidents were on the main roads, which play a meta-region functional role and act as a linkage between main destinations with high trip generation rate. According to the temporal distribution of car crashes, the peak of traffic accidents incident is simultaneous with the traffic congestion peak hours on arterial roads. The accident-prone locations are mostly located in districts with higher speed and traffic volume, therefore, they should be considered as the priority investigation locations to safety promotion programs.

Safety Evaluation of Illuminance Transition at Highway Tunnel Portals on Basis of Visual Load

Highway tunnel entrances and exits are accident-prone locations, especially in the daytime, primarily because of the sharp transitions in lighting in these areas. Therefore, a rational assessment of lighting transitions at tunnel portals is the key to ensuring traffic safety. This study used the EMR-8B eye-tracker system to monitor the pupil changes of eight selected drivers as they drove through 26 typical highway tunnels. The test results showed a power function relationship between drivers’ pupil areas and pupil illuminance at highway tunnel portals. A quantitative relationship between the pupil area and its critical velocity was also established, and the ratio ( k) of the pupil area's changing rate in relation to its critical velocity was used to assess the visual load and to further evaluate the lighting transitions. The results demonstrated that ( a) the relationship between pupil illuminance at the tunnel portal and the driver's pupil area conformed to Stevens’ law from experimental psychology, ( b) the relationship between the driver's pupil area and its critical velocity followed the quadratic function, ( c) the visual load at a tunnel entrance was heavier than that at the tunnel exit, and ( d) the severe transitions in pupil illuminance within 10 m of some existing highway tunnel entrances caused a great visual load, and thus are urgently in need of improvement.

Pedestrian Accident Analysis in Delhi using GIS

Walking is the most sustainable and most used mode of transportation in Indian cities. From 2006-2009, an estimated 8503 fatalities in road traffic crashes occurred in Delhi in which pedestrians contribute almost 51%. The objective of this study was to investigate the potential of utilizing geographic information systems (GIS) in identifying pedestrian accident-prone locations. Crash data were geocoded in ArcGIS over the digitized road map of Delhi. Results highlighted four pedestrian fatal hotspot locations in Delhi. Fatal pedestrian accidents were significantly clustered (p<0.05) over the Ring Road of Delhi at intersections. The analysis showed that the pedestrian fatalities at arterial road intersections involving cars and buses were 27% and 14% of all fatalities involving cars and buses respectively.

Does the effect of traffic calming measures endure over time? – A simulator study on the influence of gates

Accident statistics show that transitions from rural to urban areas are accident prone locations. Inappropriate speed and mental underload have been identified as important causal factors nearby such transitions. A variety of traffic calming measures (TCM) near rural–urban transitions has been tested in field experiments and driving simulator studies. Simulator experiments repeatedly exposing participants to the same treatment are scarce, hence it is unclear to what extent the effects of a TCM endure over time.This is precisely the objective of the current study: to examine what happens with the behavior of drivers when they are exposed multiple times to the same treatment (in this case a gate construction located at a rural–urban transition). Over a period of five successive days, seventeen participants completed a 17km test-drive on a driving simulator with two thoroughfare configurations (gates present or absent) in a within-subject design. Results indicate that gates induced a local speed reduction that sustained over this five-day period. Even though participants were inclined to accelerate again once passed by this gate configuration, they always kept driving at an appropriate speed. We did not find any negative side effects on SD of acceleration/deceleration or SDLP.Overall we conclude that gate constructions have the potential to improve traffic safety in the direct vicinity of rural–urban transitions, even if drivers are repeatedly exposed. Notwithstanding, we advise policy makers to appropriately use this measure. Best is to always carefully consider the broader situational context (such as whether the road serves a traffic- rather than a residential function) of each particular location where the implementation of a gate construction is one of the options.

Using Data Mining Technique to Predict Cause of Accident and Accident Prone Locations on Highways

Road accident is a special case of trau ma that constitutes a major cause of d isability, untimely death and loss of loved ones as well as family b read winners. Therefore, pred icting the likelihood of road accident on high ways with par- ticular emphasis on Lagos - Ibadan express road, Nigeria in order to prevent accident is very important. Various attempts had been made to identify the cause(s) of accidents on highways using different techniques and system and to reduce acci- dent on the roads but the rate of accident keep on increasing. In this study, the various techniques used to analyse the causes of accidents along this route and the effects of accidents were examined. A technique of using data mining tool to predict the likely occurrence of accident on highways, the likely cause of the accident and accident prone locations was proposed using Lagos -Ibadan highway as a case study. WEKA software was used to analyse accident data gathered along this road. The results showed that causes of accidents, specific time/condition that could trigger accident and accident prone areas could be effectively identified.

Study on Driving Deceleration Model on Horizontal Curve Sections of Mountain Highways

Mountain highways are traffic accident-prone locations. Such factors as vehicle speed and trajectory hold a considerable proportion in the accidents. Highway line features is the basis to determine vehicle speed. In this paper, the running speed of vehicles on mountain highways is taken as the main object of study and the observation and survey data on vehicles deceleration behaviors before they enter a curve on the curved sections of mountain highways are taken as the reference object to establish the drivers driving speed control model on the horizontal curve sections of mountain highways, and an example is used to conduct comparative validation on the driving deceleration model. Calculation results show that the deceleration control model established in this paper is feasible and effective, can better simulate vehicles deceleration behaviors when they pass through speed limit signs or the sections with road surface deceleration signs and provides a new theoretical method and guiding basis for the line features design, traffic safety analysis and evaluation and traffic safety improvements of mountain highways.

Modelling for identifying accident-prone spots: Bayesian approach with a Poisson mixture model

In traditional identification of hot spots, often known as the sites with black spots or accident-prone locations, methodologies are developed based on the total number of accidents. These criteria provide no consideration of whether the accidents were caused or could be averted by road improvements. These traditional methods result in misidentification of locations that are not truly hazardous from a road safety authority perspective and consequently may lead to a misapplication of safety improvement funding. We consider a mixture of the zero-inflated Poisson and the Poisson regression models to analyze zero-inflated data sets drawn from traffic accident studies. Based on the membership probabilities, observations are well separated into two clusters. One is the ZIP cluster; the other is the standard Poisson cluster. A simulation study and real data analysis are performed to demonstrate model fitting performances of the proposed model. The Bayes factor and the Bayesian information criterion are used to compare the proposed model with several competing models. Ultimately, this model could detect accident-prone spots.

Analysis of Avoid Entering Dilemma Zone by Acquiring Information on Signal Change Timing

Road intersections are accident-prone locations for traffic. To avoid accidents in the vicinity of intersections, vehicles should avoid entering dilemma zone. In this paper, a model formula was compiled that represents vehicle movement at a signalized intersection to gain an understanding how to ensure safety by making an earlier decision before the signal change to yellow. This formula clearly shows that an earlier decision can result in better safety at signalized intersections by preventing entry to the dilemma zone.

GIS Potential in Management of Pedestrian Accidents in Developing Countries

The main objective of this work was to investigate the potential of utilizing geographic information systems (GISs) in identifying hazardous pedestrian accident-prone locations. The study was performed for roads in Irbid city-Jordan for the years 2002 and 2003. Pedestrian accident data was analyzed by pedestrian characteristics, driver characteristics, accident time and location, environmental conditions, vehicle speed and characteristics, and accident severity. Pedestrian accidents were correlated to operational factors, including traffic volume, vehicle speed, pedestrian volume, street length, and access points. Hazardous locations of vehicle-pedestrian accidents were identified using a GIS query builder. Results showed that there was a high potential for GISs in studying pedestrian accidents.

信号切替情報の獲得によるジレンマ・ゾーンの回避に関する研究

Intersection of the road is one of the accident-prone locations for the traffic. To avoid accidents in the vicinity of intersections, it is considered to be important that vehicle could avoid entering the dilemma zone. In this paper, model formula that represents the vehicle movement at the signalized intersection was made to understand the safety by the earlier decision before signal change to yellow. Utilizing this formula, it could be clearly shown that the earlier decision could bring the safety circumstances at the signalized intersection by avoiding entering the dilemma zone.