Abstract
Road traffic accidents are among the major concerns that are leading for deaths and injuries in the world. Many predictive models use data mining technique to provide semi optimal solutions. Pattern identification and recognition have been used to for road accident predictions based on the critical features extracted depending on the dangerous locations and frequency of occurrences prone for accidents. The aim of this research is to propose a novel predictive model based on the pattern mining predictor which improves the accuracy of accident prediction in frequent accident locations. The proposed system consists of association rules mining technique, which identifies the correlation, frequent pattern and association among the various attributes of the road accident. Clustering technique that discriminates the data based on different patterns and classification technique that classify and predicts the severity of accident. Novel system built leads to an improvement in the accuracy of the accident prediction from 92% to 94%. Furthermore, using selective subset of features decreased the processing time and precision of classification is improved using boosting technique.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amira, A., Vikas, P., Abdelaziz, A.: Applying association rules mining algorithms for traffic accidents in Dubai. Int. J. Soft Comput. Eng. 5(4), 2231–2307 (2015)
Gu, X., Li, T., Wang, Y., Zhang, L., Wang, Y., Yao, J.: Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization. J. Algorithms Comput. Technol. 12(1), 20–29 (2017). https://doi.org/10.1177/1748301817729953
Arwa, A., Mourad, Y.: Hybrid approach for improving efficiency of apriori algorithm on frequent itemset. Int. J. Comput. Sci. Netw. Secur. 18(5), 7–10 (2018)
Janani, G., Devi, N.: Road traffic accidents analysis using data mining techniques. JITA: J. Inf. Technol. Appl. 14(2) (2018). https://doi.org/10.7251/jit1702084j
Tavakoli Kashani, A., Shariat-Mohaymany, A., Ranjbari, A.: A data mining approach to identify key factors of traffic injury severity. PROMET Traffic Transp. 23(1) (2011). https://doi.org/10.7307/ptt.v23i1.144
Mulay, P., Mulatu, S.: What you eat matters road safety: a data mining approach. Indian J. Sci. Technol. 9(15) (2016). https://doi.org/10.17485/ijst/2016/v9i15/92119
Hashmienejad, S., Hasheminejad, S.: Traffic accident severity prediction using a novel multi-objective genetic algorithm. Int. J. Crashworthiness 22(4), 425–440 (2017). https://doi.org/10.1080/13588265.2016.1275431
Jalayer, M., Pour-Rouholamin, M., Zhou, H.: Wrong-way driving crashes: a multiple correspondence approach to identify contributing factors. Traffic Inj. Prev. 19(1), 35–41 (2017). https://doi.org/10.1080/15389588.2017.1347260
Park, S., Kim, S., Ha, Y.: Erratum to: highway traffic accident prediction using VDS big data analysis. J. Supercomput. 72(7), 2832 (2016). https://doi.org/10.1007/s11227-016-1655-5
Krishnaveni, S., Hemalatha, M.: A perspective analysis of traffic accident using data mining techniques. Int. J. Comput. Appl. 23(7), 40–48 (2011). https://doi.org/10.5120/2896-3788
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Joshi, S. et al. (2020). Pattern Mining Predictor System for Road Accidents. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-63119-2_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63118-5
Online ISBN: 978-3-030-63119-2
eBook Packages: Computer ScienceComputer Science (R0)