Data Mining and Knowledge Engineering

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Comparision Study on Traffic Risk Assessment and Road Safety Analysis Using Spatial Data Mining Techniques


Affiliations
1 Department of Computer Science and Engineering, JNTU, Hyderabad, India
2 Department of Computer Science and Engineering, JNTU College of Engineering, Hyderabad, India
     

   Subscribe/Renew Journal


Road safety and Road accidents are the essential area of study and research in present days. Different study research has been going on but accident graph is not reducing. The foremost among purpose of this research is to this research is to improve the road safety, identify traffic crash analysis and forecasting the accident prone zone with the help of spatial data mining algorithms and techniques. Road accidents causing damage and massive losses for our community and plenty of government assets drain away every year. Spatial data mining became a convenient undertake, highly demanding, and the flourishing research field. A massive amount of spatial and temporal large data have gather in different areas like, Medical Imaging, Traffic Control, Environmental Studies, Remote sensing, Crime Hotspots, Warming of Ocean, Agriculture land grading, Analysis forest extent changes, Navigation, Remote sensing and Image database exploration. This paper emphasis on the distinguishing of SDM and spatial objects, the feature of road accidents and their relation with the geographical environment. This research analysis allows identifying the road difficulties in order to put forward safety measures. Identify the regions with a frequently happening and estimate acuteness of risk then analyze and explain this risk based on geographical location. With the help of spatial autocorrelation, spatial clustering, spatial classification, to discover hidden patterns and predict, controlling and reduce future accidents.


Keywords

Spatial Data Mining, Road Safety, Decision Tree Algorithm, Clustering, Association Rule Mining, SDM.
User
Subscription Login to verify subscription
Notifications
Font Size

  • . K.Venkateswara Rao, A.Govardhan, K.V.Chalapati Rao,"Spatiotemporal data mining issues tasks and applications", IJCSES2012.

  • . Arvind Kumar, "Spatial data mining: Recent trends and techniques. Computer and Computational Sciences (ICCCS), 2015 International Conference on Amity School of Engineering and Technology, Amity University, Noida, India.

  • . Jiawei Han, Micheline Kamber, Jian Pei, "Data Mining concepts and Techniques".

  • . Karolien Geurts, Isabelle Thomas, Geert Wets, “Understanding spatial concentrations of a road accidents using frequent item sets. Accident Analysis and Prevention" 37 (2005) 787-799.

  • . J. M. Manasa, “Spatial Decision Tree for Accident Data Analysis", School of Information Technology, Indian Institute of Technology Kharagpur, West Bengal, India.

  • . Sachin Kumar, Durga Toshniwal, " Analysing Road Accident Data Using Association Rule Mining", Indian Institute of Technology Roorkee, Uttarakhand, India, 2015 IEEE.

  • . Frantisek Babic, Karin Zuskacova , " Descriptive and Predictive Mining on Road Accidents Data", 2016 IEEE.

  • . Bimal Ghimire, Shrutilipi Bhattacharjee, Soumya K. Ghosh, " Analysis of Spatial Autocorrelation for Traffic Accident Data Based on Spatial Decision Tree ",Nepal , IIT Kharagpur, India, 2013 IEEE.

  • . Sachin Kumar, Durga Toshniwal, “A data mining approach to characterize road accident locations",Kumar and Toshniwal Journal of Big Data, a Springer open Journal 2015.

  • . Hailiang Jin, Baoliang Miao, “The Research Progress of Spatial Data Mining Technique", Key Laboratory of Mine Spatial Information Technologies of State Bureau of Surveying and Mapping Henan Polytechnic University Jiaozuo, China.

  • . Dipali B. Gaikwad , Yogesh W. Wanjari , Karbhari . V. Kale, " Accident Analysis System by Integration of Spatial Data Mining with GIS Web Services",IJCA2014

  • . Farzaneh Moradkhani, Somayya Ebrahimkhani, Bahram Sadeghi Begham, " Road Accident Data Analysis: A Data Mining Approach ", Article in Indian Journal of Scientific Research May 2014.

  • . Karolien Geurts ,Isabelle Thomas,Geert Wets ,"Understanding spatial concentrations of road accidents using frequent item sets",2005 Elsevier Ltd, Accident Analysis and Prevention 37 (2005) 787–799.

  • . Manjula Aakunuri, Dr.G.Narasimha, Sudhakar Katherapaka, "Spatial Data Mining: A Recent Survey and New Discussions", International Journal of Computer Science and Information Technologies, 2011.

  • . Jeffrey Erman , " Traffic Classification Using Clustering Algorithms", SIGcomm2006 workshop.

  • . V. Prasannakumar, H. Vijith, R. Charutha, N. Geetha, " Spatio-Temporal Clustering of Road Accidents: GIS Based Analysis and Assessment", Elsevier2011.

  • . Pratyush Kar, Sameer Saran. ISRO Dehradun, Computer Science BITS Pilani, " Spatial DM: An Open Source Data Mining Plugin for QGIS", Journal of Geomatics, 2015.

  • . He YueShun, Xu Wei, "A Study of Spatial Data Mining Architecture and Technology", 2009 IEEE.

  • . Shashi Shekhar , Michael R. Evans, James M. Kang , " Spatial Data Mining ",Department of Computer Science, University of Minnesota.

  • . Diansheng Guo, Member, IEEE, Xi Zhu, “Origin-Destination Flow Data Smoothing and Mapping. IEEE transactions on visualization and computer graphics", vol. 20, no. 12, December 2014.

  • . Nejdet do gru, Abdl ulhamit subasi, “Comparison of clustering techniques for traffic accident detection ", Turkish Journal of Electrical Engineering & Computer Sciences, Herzegovina (2015).

  • . Wang Jinlin , Chen Xi1, Zhou Kefa1, Wang Wei, Zhang Dan, " Application of Spatial Data Mining in Accident Analysis System", 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geo science and Remote Sensing.

  • . Dr. R. Geetha Ramani, S. Shanthi, “Classifier Prediction Evaluation in Modeling Road Traffic Accident Data ", 2012 IEEE International Conference on Computational Intelligence and Computing Research.

  • . He YueShun, Xu Wei, “A Study of Spatial Data Mining Architecture and Technology", 2009 IEEE.

  • . Emerson D, Nayak R, Weligamage , "Identifying differences in safe roads and crash prone roads using clustering data mining ",proceedings of the sixth annual world congress on engineering asset management. ohio.2011.

  • . Shashi Shekhar, Michael R. Evans, James M. Kang, Pradeep Mohan , " Identifying patterns in spatial information a survey of methods .Department of Computer Science, University of Minnesota, Minneapolis, Minnesota, USA, June2011.

  • . Anderson, T. K, “Kernel density estimation and K-means clustering to profile road accident hotspots, Accident Analysis & Prevention", 2009.

  • . Agnar Renolen, "Modelling Spatiotemporal Information: The Spatiotemporal Object Model. May 23, 1997.

  • . Imad Abugessaisa, “knowledge discovery in road accidents database- integration of visual and automatic data mining methods”, International Journal of Public Information Systems, 2008.

  • . Rupam Deb, Alan Wee-Chung Liew, “Incorrect attribute value detection for traffic accident data.” 2015 IEEE.


Abstract Views: 536

PDF Views: 1




  • A Comparision Study on Traffic Risk Assessment and Road Safety Analysis Using Spatial Data Mining Techniques

Abstract Views: 536  |  PDF Views: 1

Authors

Dara Anitha Kumari
Department of Computer Science and Engineering, JNTU, Hyderabad, India
Dr. A. Govardhan
Department of Computer Science and Engineering, JNTU College of Engineering, Hyderabad, India

Abstract


Road safety and Road accidents are the essential area of study and research in present days. Different study research has been going on but accident graph is not reducing. The foremost among purpose of this research is to this research is to improve the road safety, identify traffic crash analysis and forecasting the accident prone zone with the help of spatial data mining algorithms and techniques. Road accidents causing damage and massive losses for our community and plenty of government assets drain away every year. Spatial data mining became a convenient undertake, highly demanding, and the flourishing research field. A massive amount of spatial and temporal large data have gather in different areas like, Medical Imaging, Traffic Control, Environmental Studies, Remote sensing, Crime Hotspots, Warming of Ocean, Agriculture land grading, Analysis forest extent changes, Navigation, Remote sensing and Image database exploration. This paper emphasis on the distinguishing of SDM and spatial objects, the feature of road accidents and their relation with the geographical environment. This research analysis allows identifying the road difficulties in order to put forward safety measures. Identify the regions with a frequently happening and estimate acuteness of risk then analyze and explain this risk based on geographical location. With the help of spatial autocorrelation, spatial clustering, spatial classification, to discover hidden patterns and predict, controlling and reduce future accidents.


Keywords


Spatial Data Mining, Road Safety, Decision Tree Algorithm, Clustering, Association Rule Mining, SDM.

References