Keywords Accident Black Spot, Digitizing, GIS, KDE.

Transcription

1 Volume 6, Issue 1, January 2016 ISSN: X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: Mapping and Analysis of Accident Black Spot in Aurangabad City using Geographic Information System Umesh M. Raut *, Dhananjay B. Nalawade, Karbhari. V. Kale Department of CS & IT, Dr.BAMU University, India Abstract Road accidents contributing to major deaths due to increase in vehicular density. It have been recognized as one of those adverse elements which contribute to the suffocation of economic growth in the developing countries, so it is an urgent task to reduce these accidents by performing analyses and taking appropriate countermeasures. Identification of problematic locations is one of the most important aspects in accident studies. The GIS based application combines the information collection capabilities with the visualization. Accident analysis studies to focus at the identification of high rate accident locations and safety deficient areas. Keywords Accident Black Spot, Digitizing, GIS, KDE. I. INTRODUCTION Transportation contributes to the educational, economical, industrial and social development of any country. Road transportation is the only mode that gives maximum service to everyone. Due to the increase in population, vehicular density is also increasing which leads to the increase in road accident. Accident data, collected for many years, serve as the ground base for designing system to reduce the number of road accidents. The fundamental objective is to determine the factors that contribute to accidents at those spots. It is important to take actions that will reduce crash frequency or severity. Determining such factors like road user characteristics and road way features is usually done through a macroscopic study of a large accident database. [1] Implementing road accident countermeasures effectively, it is important to identify accident black spot locations and analyzing accident patterns so that the most appropriate measures may be taken for each location. Conventionally, accident patterns and location have been plotted on paper maps. However, in recent years, software applications incorporating Geographic Information Systems (GIS) have been being developed in many fields. In addition to permitting linkage between various types of maps and data, a GIS is able to manipulate and visually display numerous types of data for easy comprehension. GIS has been identified as an excellent system for managing and storing these types of data and also as a potential tool to improve accident analysis process. It provides an efficient system of linking a large number of databases and provides a spatial referencing system for reporting output at the different levels of aggregation. [2] 1.1 Black Spot Hotspots, Black spots, or high crash locations are sites on a section of a roads that have an accident frequency significantly higher than expected at some threshold level of significance. This study consists locating Hospitals, Police stations, Heavy and Light vehicles accident spot and analysis through KDE Method. In the present study, GIS analysis is performed using ARCGIS package. II. STUDY AREA - AURANGABAD CITY Aurangabad city is a Capital of Marathwada Region of Maharashtra. The co-ordinates for Aurangabad are N and E in Decimal Degree. Aurangabad district comprises of 9 talukas and 1344 villages, covers an area of 10,100 sq. km.with the population of lakhs as per 2011census. The city is famous for its rich cultural heritage. The famous world tourist s places are Ajanta Caves, Bibi-ka-Makbara, Daulatabad Fort, Ellora Caves and Panchakki. It is located in centre of Maharashtra. Aurangabad is one of the fastest developing city in Asia. It is well connected by roads with various major cities of Maharashtra and other states. [3][4] [5] [23]. 2.1 Problem Definition As the Aurangabad City is one of the fastest growing city in Maharashtra, the accident rate is also increased in the past few years. The traditional methods are not enough to cope up with the growing population and the accident. The term Black spots identification required to determine the highest rate accident locations and the cause of these accidents. It issued to develop a model to prioritize hazardous locations by comparison of real time data with records available in the police station. Getting the knowledge of concentration of accident within an area, law enforcement agencies can enact timely and effective judgment of assigning police resources and way to hospital. In addition, Accident Black Spots mapped by using GIS technology can admirably communicate accident patterns and accident prevention policies to public and decision makers. [6] 2016, IJARCSSE All Rights Reserved Page 511

2 Fig 2.1 Aurangabad City- The Study Area. III. LITERATURE REVIEW Pulugurtha et al. (2007) studied the spatial patterns of pedestrian accidents with the help of Simple and Kernel Methods in order to identify high pedestrian accident zones. [7] In Turkey, Erdogan et al. (2008) studied accidents hot spots and detected safety deficient areas on the highways in the city of Afyon, Turkey. KDE and repeatability analysis were conducted to identify accident black spots. As a result of both analyses, almost same locations were founded as black spots where most of them located at cross roads, access roads, junction points to the towns and villages. They also showed that accident density increase in summer and winter especially in December and August. Also weekends have higher frequencies of accidents. Moreover, they found that Heavy accidents mostly occurred in midnight. [8] Xie and Yan (2008) used KDE in terms of lixel size (linear pixel, same as raster cell but it is in a network), bandwidth and density visualization while identifying accident hot spots. [9] Pulugurtha and Vanapalli (2008) studied also hazardous bus stop identification using KDE to create crash hot spot maps. Different from the former study, traffic volumes, bus stop coverage, transit ridership data and street centreline coverage were used in ranking hazardous bus stops.[10] Parasannakumar et al. (2011) studied clustering analysis with respect to accident type and its occurrence time, using Moran s I Index to perform spatial autocorrelation. Clustering analyses were performed with KDE and Getis-Ord GI* statistics. As a result of the analyses, accident cold and hotspot were determined. [11] Moreira et al. (2012) studied three different methods to identify hazardous road location (HRL) in the City of Vila Real, Portugese: KDE, Point Density and NNH clustering algorithm, As a result of the study, crossing speed limit was found as an indicator factor of single vehicle crashes (run-off crash). [12] Most recently, Kaygısız et al. (2012) studied the spatiotemporal analyses of road accidents occurred on the roadways in Turkey. Both KDE and Network KDE methods were used to identify hot spots, which were detected mostly in the summer time. In the study area, most commonly seen accident types were rear-end, colliding with stationary object and run-off. [13] Different spatial analysis methods have been used in recent years for pedestrian safety studies. But KDE (Kernel Density Estimation) is the most used method for finding the Black spot and is effectively visualize and helps analysing the accident events spread across the Geography. ArcGIS software is used by most of the Authors. 3.1 Causes of Road Accidents: The various causes of Road accidents are: Road Users - Excessive speed and rash driving, violation of traffic rules, failure to perceive traffic situation or sign or signal in adequate time, carelessness, fatigue, alcohol,sleep etc. Vehicle - Defects such as failure of brakes, steering system, tyre burst,lighting system. Road Condition - Skidding road surface, pot holes, ruts. Road design - Defective geometric design like inadequate sight distance, inadequate width of shoulders, improper curve design, improper traffic control devices and improper lighting,. Pedestrian: Carelessness, illiteracy, crossing at wrong places moving on carriageway, Jaywalkers. Passengers: Projecting their body outside vehicle, by talking to drivers, alighting and boarding vehicle from wrong side travelling on footboards, catching a running bus etc. Environmental factors -unfavorable weather conditions like mist, snow, smoke and heavy rainfall which restrict normal visibility and and makes driving unsafe. Other causes -Improper location of advertisement boards, gate of level crossing not closed when required etc. IV. METHODOLOGY The detailed methodology as shown in Fig 4.1 for carrying out the results is explained as follows. 2016, IJARCSSE All Rights Reserved Page 512

3 Fig 4.1 Methodology of Proposed Work A. Data Collection 1) Base Map Aurangabad city ( N and E) is considered as the study area [24]. The Toposheet of Aurangabad (M.S, India) has been used. Creator: Survey Of India Year: Scale: 1:50,000 Sheet: E43DM (47 M/5), Aurangabad, Maharashtra Projection: UTM, 43N Datum: WGS 84 Other: Magnetic Variation from True North about 3 West in 2005 (Decreasing by about 1 4 annually). [14] Figure 4.2 Toposheet of Aurangabad City of the year , IJARCSSE All Rights Reserved Page 513

4 For Accident spot mapping hypothetical data which is based on the coordinates of the Toposheet and assumption is used for experimental purpose. The Heavy vehicles and Light vehicles accident spots are considered with respect to police stations boundary for result and analysis purpose. [15] a) b) c) Figure 4.3 a) Police Service Information b) Health Care Information c) Collected Accident Data Figure 4.4 The Geodatabase file showing different attributes of Heavy Vehicles Accident incidence. B. Georeferencing of the Toposheet In the process of Georeferencing, registration of the digitized Toposheet into the Geographic Coordinate System (GCS) takes place. The GCS for Aurangabad is WGS_84. Aurangabad comes under the Universal Transverse Mercator (UTM) zone 43 N. [16] Scanned maps usually do not contain information as to where the area represented on the map fits on the surface of the earth. To establish the relationship between an image co ordinate system and a map(x, y) coordinate system need to align or georeference the raster data. This is very crucial stage to obtain the high accuracy or precision in measurements. It can be also known as rectifying map. Geo-referencing of base map is critical stage, because geo-referencing allows us to register the base map with respect to the earth s surface. Geo-referencing can be done by selecting the four control points on the corners of the scanned map and placing that point by giving appropriate latitude and longitude values and it is very important to perform this step with minimum RMS error in order to gain maximum accuracy. The RMS (Root Mean Square) error can be calculated by using following equation, Where x act and y act are the x and y value of the actual location, and x act and y act are the x and y values of the estimated locations. [17] RMS error determines the goodness of control points which measures the deviation between actual and estimated locations of control points. 2016, IJARCSSE All Rights Reserved Page 514

5 Fig 4.5 Ground Points for Georeferencing and RMS Error C. Digitizing Digitizing converts paper map features into digital format. The digitizing process creates spatial data. It allows the attribute data to be stored in together. It is the process of encoding the geographic features in digital form as x, y coordinates. It was carried out to create spatial data from existing hard copy maps and documents. [17] In the present work the georeferenced raster image of Aurangabad city is digitized using Arc GIS This type of digitization is called onscreen digitization. Road network of the study area was digitized as line features. Accident spots, police stations, hospitals are digitized as point features. Road Network of Aurangabad city is shown in below fig 4.5. Fig 4.6 Road Network of Aurangabad City D. Mapping Hospitals and Police Station with their Boundary In this phase hospital and police stations are mapped according to their GPS coordinates by Field Survey. Attributes taken for hospitals such as Name, Address, Contact Number. A Hospital and Police Station is Point Feature. For Police station various attributes taken in consideration such as Police Station ID, Address, Contact number, Boundary Name. Police Station boundary is mapped by gathering zone boundary information with respective each police station. The Polygon Feature is used for representing police Station boundary. (a) (b) Fig 4.7 a) Hospitals on Aurangabad Toposheet b) Police Station with their Boundaries on Toposheet. 2016, IJARCSSE All Rights Reserved Page 515

6 E. Mapping different types of Accident Spot on Toposheet After mapping the Hospital, Police stations and their zone boundary, accident data is placed on toposheet of Aurangabad. Accident data is collected by taking GPS location of each spot by field survey. Accident data is categorized as Heavy vehicles Accident spot and Light Vehicles Accident Spot. Accident data is mapped as point feature on toposheet. The Accident spot location is hypothetical. For mapping Accident spot various factors are considered like, road Condition, Population Density, improper curve design, Defective geometric design like inadequate sight distance, inadequate width of shoulders and improper lighting. Fig 4.8 Heavy vehicles and Light Vehicles Accident spot of Aurangabad City V. RESULT AND ANALYSIS In this phase Spatial Analysis method is used to identify black spot of Accident incidence at different places in Aurangabad. Method creates or extracts different new information from spatial data.in spatial analysis method; we have considered Kernel Density Estimation and Point Density Estimation. In this phase Methods are implemented which shows satisfied result for identifying Accident black spot. 1) Kernel Density Estimation Method It is a spatial analyst Method. Kernel Density analysis method was used to calculate the density of accidents with a search radius of 50m. The Kernel Method divides the entire study area to a pre-determined number of cells and applies a circular neighbourhood around each crash. Density Estimation measures cell densities in a raster by using a sample of known points. Kernel Density Estimation associates each known point with a Kernel function. This can be expressed as a bivariate probability density function, a kernel function looks like a bump, centring at a known point and tapering off to 0 over a defined bandwidth or window area.[18][19] [20] KDE shows the smoothing effect and can be applied only on the Points and Polylines. The black spot is detected in dark blue colour for heavy vehicles and dark red for light vehicles. (a) (b) Fig 5.1 The Accident Black Spot identified by KDE Method for a) Heavy Vehicles b) Light Vehicles 2) Point Density Estimation Method Point Density calculates the density of point features around each output raster cell. Conceptually, a neighbourhood is defined around each raster cell center, and the number of points that fall within the neighbourhood is totalled and divided by the area of the neighbourhood.[21][22] 2016, IJARCSSE All Rights Reserved Page 516

7 Fig 5.2 The Accident Black spot identified by PDE Method for a)heavy Vehicles b)light Vehicles. VI. CONCLUSION GIS can effectively help in the processing of accident data, and for performing complex spatial analysis. GIS helps tremendously in the visualization of the problem of road accidents. Mapping of Accident data such as Heavy vehicles and Light vehicles accident spot is done and different spatial Analysis techniques are used to find out the accident black spot in Aurangabad City. It will help the peoples as well as the Tourist visiting these places by providing information regarding which area is safe for travelling and concentration of accident at that place. Hospitals are mapped to give emergency service for accident person for their treatments. For Point feature, it is identified that KDE and PDE provide satisfactory result on the toposheet map and provides same result despite of change in the image acquiring process of same area. The KDE enabled an overarching visualisation and manipulation of the accidents based on density and produces smoothing effect layered. The Kernel density gives smoother result while Point density produces more steep edges. VII. FUTURE SCOPE Accident analysis will be more accurate, easier and descriptive if the road accident report are more detail and formatted properly. If the X and Y coordinate of location using GPS are saved in police database then plotting of accident points should be easier and results of analysis will be more reliable. ACKNOWLEDGMENT Authors would like to acknowledge and extend our heartfelt gratitude to UGC who have funded for development of UGC SAP (II) DRS Phase-I F.No.-3-42/2009 to Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. REFERENCES [1] B E Filibus, A Study of the level of enforcement and compliance with the Traffic rules And regulations by road users in Federal Capital Territory (F.C.T) Abuja, M.T.L. thesis, Nigeria Institute of Transportation Technology (N.I.T.T), Zaria, Nigeria, 2010 [2] J A Savena. (May 2011) Environmental and Road Traffic Safety, CRRI, GIS, as an aid to Identify accident patterns. [Online]. Available: [3] Aurangabad, Maharashtra, Sited on Jan [4] [5] Amrapali C. Dabhade, Dr. K. V. Kale, GIS Based Health Care Information System for Aurangabad City, International Journal of Engineering and Innovative Technology (IJEIT) Volume 4, Issue 1, July 2014 [6] Lauren Scott and Nathan Warmerdam, Extend Crime Analysis with ArcGIS Spatial Statistics Tools ( accessed on ). [7] Pulugurtha, S.S., Krishnakumar, K.V., Nambisan, S.S., (2007). New methods to identify and rank high pedestrian crash zones: An Illustration, Accident Analysis and Prevention, 39(4), [8] Erdogan, S., Yilmaz, I., Baybura, T., Gullu, M.,(2008), Geographical Information Systems Aided Traffic Accident Analysis System Case Study City of Afyonkarahisar, Accident Analysis and Prevention, Vol. 40/1, [9] Xie, Z., & Yan, J. (2008). Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 32(5), [10] Pulugurtha, S., and Vanapalli V. (2008). Hazardous bus stops identification: An illustration using GIS. Journal of Public Transportation 11, [11] Prasannakumar, V., Vijith, H., Charutha, R., & Geetha, N. (2011), Spatio-Temporal Clustering of Road Accidents: GIS Based Analysis and Assessment, Procedia - Social and Behavioral Sciences 21, , IJARCSSE All Rights Reserved Page 517

8 [12] Moreira, I., Gonçalves, A., Shrusbsall, S. (2012). Planning on safer urban mobility through spatial analysis: Application to Vila Real, Portugal, AESOP 26th Annual Congress, METU, Ankara. [13] Kaygısız, Ö. Kentsel Arazi Kullanımı Trafik Kazas İlişkisi:Eskişehir Örneği, Phd Thesis, Gazi University, Ankara, [14] Shahebaz M. Ansari and K.V.Kale, Mapping and Analysis of Crime in Aurangabad City Using GIS, Journal of Computer Engineering, 2013, vol. 16, Issue 6 Ver.VII, pp [15] [16] Nagne Ajay D., and Bharti W. Gawali; Transportation network analysis by using Remote Sensing and GIS a Review; IJERA, [17] Kang-Tsung chang, Introduction to Geographic information system (Fourth Edition), Tata McGraw Hill Private Limited. [18] M. Vijaya Kumar and Dr. C. Charasekar; Spatial Statistical Analysis of burglary Crime in Chennai City Promoters Apartments: A Case Study, IJETT, [19] Lahaut, B., Mouchart, M., San Martin, E., Thomas, I., The local spatial autocorrelation and the kernel method for identifying black zones: a comparative approach. Accident Analysis and Prevention 35 (6), [20] Sabel, C., Kernel Density Estimation as a Spatial-Temporal Data Mining Tool:Exploring Road Traffic Accident Trends, GISRUK 2006, University of Nottingham. [21] [22] [23] Dhananjay B. Nalawade, Sumedh D. Kashid, Rajesh K. Dhumal, Ajay D. Nagne and Karbhari V. Kale, analysis of present transport system of Aurangabad city using geographic information system, International Journal of Computer Sciences and Engineering, Volume-3, Issue-6, June [24] Swapnil R Rajput, Mohd Sohel Deshmukh and Karbhari V Kale. Article: Cross-platform Smartphone Emergency Reporting Application in Urban Areas using GIS Location based and Google Web Services. International Journal of Computer Applications130 (12):27-33, November Published by Foundation of Computer Science (FCS), NY, USA. 2016, IJARCSSE All Rights Reserved Page 518