Efficient Analysis of Traffic Accident Using Data Mining techniques
Abstract
Data Mining is the process of extracting patterns from data. Machine Learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn based on data, such as from sensor data or databases. A major focus of machine learning research is automatically learn to recognize complex patterns and make intelligent decisions based on data. Engineers andresearchers in the automobile industry have tried to design and build safer automobiles, but traffic accidents are unavoidable. Patterns involved in dangerous crashes could be detected if we develop a prediction model that automatically classifies the type of injury severity of various trafficaccidents. These behavioral and roadway patterns are useful in the development of traffic safety control policy. We believe that to obtain the greatest possible accident reduction effects with limited budgetary resources, it is important that measures be based on scientific and objective surveys of the causes of accidents and severity of injuries. This paper deals about some classification models to predict the severity of injury that occurred during traffic accidents using two machine-learning approaches. We compared Naïve Bayesian classifier and J48 decision tree Classifier for classifying the type of injury severity of various traffic accidents and the result shows that J48 outperforms Naïve Bayesian.
Keywords
Full Text:
PDFReferences
Gartner Group High Performance Computing Research Note 1/31/95
Ossenbruggen, P.J., Pendharkar, J. and Ivan, J. 2001, “Roadway safety in rural and small urbanized areas”. Accidents Analysis and Prevention, 33 (4), pp. 485–498.
Miaou, S.P. and Harry, L. 1993, “Modeling vehicle accidents and highway geometric design relationships”. Accidents Analysis and Prevention, (6), pp. 689–709.27. Desktop Reference for Crash Reduction Factors Report No. FHWA-SA-07-015, Federal Highway Administration September,2007http://www.ite.org/safety/issuebriefs/Desktop%20Reference%20Complete.pdf
S.B. Kotsiantis, Supervised Machine Learning: A Review of Classification Techniques, Informatica 31(2007) 249-268, 2007
http://www.td.gov.hk/filemanager/en/content_2015/08pubdb.xls
http://www.lri.fr/~pierres/donn%E9es/save/these/weka-3-4/README
http://databases.about.com/od/datamining/g/Classification.htm
http://www.td.gov.hk/en/road_safety/road_traffic_accident_statistics/2008/index.html
Domingos, Pedro & Michael Pazzani (1997) "On the optimality of the simple Bayesian classifier under zero-one loss". Machine Learning, 29:103–137. (also online at CiteSeer: [1])
Rish, Irina. (2001). "An empirical study of the naive Bayes classifier". IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence. (available online: PDF, PostScript)
Abdel-Aty, M., and Abdelwahab, H., Analysis and Prediction of Traffic Fatalities Resulting From Angle Collisions Including the Effect of Vehicles’ Configuration and Compatibility. Accident Analysis and Prevention, 2003.
Bedard, M., Guyatt, G. H., Stones, M. J., & Hireds, J. P., The Independent Contribution of Driver, Crash, and Vehicle Characteristics to Driver Fatalities. Accident analysis and Prevention, Vol. 34, pp. 717-727, 2002.
Evanco, W. M., The Potential Impact of Rural Mayday Systems on Vehicular Crash Fatalities. Accident Analysis and Prevention, Vol. 31, 1999, pp. 455-462.
Kweon, Y. J., & Kockelman, D. M., Overall Injury Risk to Different Drivers: Combining Exposure, Frequency, and Severity Models. Accident Analysis and Prevention, Vol. 35, 2003, pp. 441-450.
Martin, P. G., Crandall, J. R., & Pilkey, W. D., Injury Trends of Passenger Car Drivers In the USA. Accident Analysis and Prevention, Vol. 32, 2000, pp. 541-557.
http://en.wikipedia.org/Traffic_collision
Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
http://en.wikipedia.org/wiki/Genetic_algorithm
Gartner Group High Performance Computing Research Note 1/31/95
Gartner Group Advanced Technologies & Applications Research Note 2/1/95
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.