Predicting the Severity of Motor Vehicle Accident Injuries in Adana-Turkey Using Machine Learning Methods and Detailed Meteorological Data

Cigdem ACI, Cevher OZDEN

Abstract

Traffic accidents are among the most important issues facing every nation in the world as they cause many deaths and injuries as well as economic losses every year. In this study, the traffic accidents that took place in Adana, have been classifiedaccording to injury severity (i.e. fatal or non-fatal) and the factors affecting the accident outcome are investigated. The study included the traffic accident reports kept by Regional Traffic Division and the weather data provided by the Regional Directorate of Meteorology during 2005-2015. Five major machine learning methods (i.e. k-Nearest Neighbor, Naive Bayes, Multilayer Perceptron, Decision Tree, Support Vector Machine) and one statistical method, Logistic Regression, were employed for prediction models and performances of the models as well as the effective parameters were compared. The main objective of the study is to determine how important weather and other phenomena are for the occurrence of traffic accidents. Decision Tree, k-Nearest Neighbor and Multilayer Perceptron based models yielded higher accuracy in classification of accidents compared to other models. Furthermore, in Area Under Curve based analysis of factor importance, it was determined that Mean Cloudiness, Existence of Traffic Control and Ground Surface Temperature had higher positive effects, while Maximum Temperature and Weather (kept by traffic officers) parameters decreased the accuracy of models.

Keywords

injury severity; machine learning; prediction; predictor importance; traffic accident

Full Text:

PDF
Submitted: 2017-12-04 14:21:17
Published: 2018-03-29 15:53:51
Search for citations in Google Scholar
Related articles: Google Scholar

References

WHO, “Global status report on road safety,” 2016. [Online]. Available: http://www.who.int/violence_injury_prevention/road_safety_status/2015/en/. [Accessed: 19-Jun-2017].

WHO, “Road traffic injuries,” 2017. [Online]. Available: http://www.who.int/mediacentre/factsheets/fs358/en/. [Accessed: 19-Jun-2017].

TSI, “Turkish Statistical Institute,” Turkish Statistical Institute, 2017. [Online]. Available: http://www.turkstat.gov.tr/Start.do. [Accessed: 19-Jun-2017].

M. A. Abdel-Aty and A. E. Radwan, “Modeling traffic accident occurrence and involvement” Accid. Anal. Prev., vol. 32, no. 5, pp. 633–42, Sep. 2000.

S. Y. Sohn and H. Shin, “Pattern recognition for road traffic accident severity in Korea,” Ergonomics, vol. 44, no. 1, pp. 107–117, Jan. 2001.

Q. Wu, G. Zhang, X. Zhu, X. C. Liu, and R. Tarefder, “Analysis of driver injury severity in single-vehicle crashes on rural and urban roadways,” Accid. Anal. Prev., vol. 94, pp. 35–45, 2016.

M. Taamneh, S. Alkheder, and S. Taamneh, “Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates,” J. Transp. Saf. Secur., pp. 1–21, Apr. 2016.

E. I. Vlahogianni, M. G. Karlaftis, and F. P. Orfanou, “Modeling the Effects of Weather and Traffic on the Risk of Secondary Incidents,” J. Intell. Transp. Syst., vol. 16, no. 3, pp. 109–117, Jul. 2012.

J. Ona, R. O. Mujalli, and F. J. Calvo, “Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks,” Accid. Anal. Prev., vol. 43, no. 1, pp. 402–411, Jan. 2011.

C. M. Bishop, Pattern recognition and machine learning. Springer, 2006.

T. Mitchell, Machine Learning. McGraw-Hill, 1997.

S. Ajmani, K. Jadhav, and S. A. Kulkarni, “Three-Dimensional QSAR Using the k-Nearest Neighbor Method and Its Interpretation,” J. Chem. Inf. Model., vol. 46, no. 1, pp. 24–31, Jan. 2006.

O. Z. Maimon and L. Rokach, Soft computing for knowledge discovery and data mining. Springer, 2011.

S. Shalev-Shwartz and S. Ben-David, Understanding machine learning : from theory to algorithms. Cambridge: Cambridge University Press, 2014.

M. Chong, A. Abraham, and M. Paprzycki, “Traffic Accident Analysis Using Machine Learning Paradigms,” Informatica, vol. 29, no. 1, pp. 89–98, 2005.

X. Fan, L. Wang, and S. Li, “Predicting chaotic coal prices using a multi-layer perceptron network model,” Resour. Policy, vol. 50, pp. 86–92, Dec. 2016.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. New York, NY: Springer New York, 2009.

M. Taamneh, S. Taamneh, and S. Alkheder, “Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks,” Int. J. Inj. Contr. Saf. Promot., pp. 1–8, Sep. 2016.

D. G. Altman and J. M. Bland, “Statistics Notes: Diagnostic tests 2: predictive values,” BMJ, vol. 309, no. 6947, 1994.

BIML, “Test Statistics,” 2017. [Online]. Available: http://groups.bme.gatech.edu/groups/biml/resources/useful_documents/Test_Statistics.pdf. [Accessed: 19-Jun-2017].

Garson and G. David, “Interpreting neural-network connection weights,” AI Expert, vol. 6, no. 4, pp. 46–51, 1991.

Y.-W. Chang and C.-J. Lin, “Feature Ranking Using Linear SVM,” in JMLR: Workshop and Conference Proceedings 3, 2008, pp. 53–64.

K. Subbian and P. Melville, “Supervised Rank Aggregation for Predicting Influencers in Twitter,” in 2011 IEEE Third Int’l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int’l Conference on Social Computing, 2011, pp. 661–665.

Abstract views:
74

Views:
PDF
77




Copyright (c) 2018 International Journal of Intelligent Systems and Applications in Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
 
© AtScience 2013-2018     -     AtScience is a registered trademark property of AtScience.