Abstract
Road Traffic Accidents (RTAs) are currently the leading causes of traffic congestion, human death, health problems, environmental pollution, and economic losses. Investigation of the characteristics and patterns of RTAs is one of the high-priority issues in traffic safety analysis. This paper presents our work on mining RTAs using association rule based methods. A case study is conducted using UK traffic accident data from 2005 to 2017. We performed Apriori algorithm on the data set and then explored the rules with high lift and high support respectively. The results show that RTAs have strong correlation with environmental characteristics, speed limit, and location. With the network visualization, we can explain in details the association rules and obtain more understandable insights into the results. The promising outcomes will undoubtedly reduce traffic accident effectively and assist traffic safety department for decision making.
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
Similar content being viewed by others
References
World Health Organization: Global status report on road safety 2018. World Health Organization (2018)
Road Safety Facts. https://www.asirt.org/safe-travel/road-safety-facts/. Accessed 25 Oct 2018
US Department of Health and Human Services, CDC. https://www.cdc.gov/injury/wisqars. Accessed 14 Jan 2018
Bhandari, A., Gupta, A., Das, D.: Improvised apriori algorithm using frequent pattern tree for real time applications in data mining. Procedia Comput. Sci. 46, 644–651 (2015)
Weng, J., Zhu, J.Z., et al.: Investigation of work zone crash casualty patterns using association rules. Accid. Anal. Prev. 92, 43–52 (2016)
Montella, A.: Identifying crash contributory factors at urban roundabouts and using association rules to explore their relationships to different crash types. Accid. Anal. Prev. 43(4), 1451–1463 (2011)
Subasish, D., Sun, X.: Investigating the pattern of traffic crashes under rainy weather by association rules in data mining. In: Transportation Research Board 93rd Annual Meeting, No. 14-1540. Transportation Research Board, Washington DC (2014)
Gao, Z., Pan, R., et al.: Research on automated modeling algorithm using association rules for traffic accidents. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, pp. 127–132 (2018)
Priya, S., Agalya, R.: Association rule mining approach to analyze road accident data. In: 2018 International Conference on Current Trends Towards Converging Technologies (ICCTCT), Coimbatore, pp. 1–5 (2018)
Xu, C., Bao, J., et al.: Association rule analysis of factors contributing to extraordinarily severe traffic crashes in China. J. Saf. Res. 67, 65–75 (2018)
Das, S., Dutta, A., et al.: Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures. Int. J. Urban Sci. 23(1), 30–48 (2019)
Gariazzo, C., Stafoggia, M., et al.: Association between mobile phone traffic volume and road crash fatalities: a population-based case-crossover study. Accid. Anal. Prev. 115, 25–33 (2018)
Deng, X., Zeng, D., Shen, H.: Causation analysis model: based on AHP and hybrid Apriori-Genetic algorithm. J. Intell. Fuzzy Syst. 35(1), 767–778 (2018)
Feng, M., Zheng, J., et al.: Big data analytics and mining for effective visualization and trends forecasting of crime data. IEEE Access 7(1), 106111–106123 (2019)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993)
UK Road Safety Dataset. https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data
Feng, M., Zheng, J., Han, Y., Ren, J., Liu, Q.: Big data analytics and mining for crime data analysis, visualization and prediction. In: Ren, J., Hussain, A., Zheng, J., Liu, C.-L., Luo, B., Zhao, H., Zhao, X. (eds.) BICS 2018. LNCS (LNAI), vol. 10989, pp. 605–614. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00563-4_59
Yan, Y., Ren, J., et al.: Cognitive fusion of thermal and visible imagery for effective detection and tracking of pedestrians in videos. Cogn. Comput. 10, 94–104 (2017)
Yan, Y., Ren, J., et al.: Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement. Pattern Recogn. 79, 65–78 (2018)
Cao, F., Yang, Z., Ren, J., et al.: Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 57, 5580–5594 (2019)
Sun, H., Ren, J., et al.: Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images. Remote Sens. (MDPI) 11(5), 536 (2019)
Zhang, A., Sun, G., Ren, J., et al.: A dynamic neighborhood learning-based gravitational search algorithm. IEEE Trans. Cybern. 48, 436–447 (2017)
Acknowledgement
This work has been supported by HGJ, HJSW and Research & Development plan of Shaanxi Province (Program No. 2017ZDXM-GY-094, 2015KTZDGY04-01).
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
Feng, M., Zheng, J., Ren, J., Xi, Y. (2020). Association Rule Mining for Road Traffic Accident Analysis: A Case Study from UK. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-39431-8_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39430-1
Online ISBN: 978-3-030-39431-8
eBook Packages: Computer ScienceComputer Science (R0)