Abstract
Traffic accidents are a safety issue that has received widespread attention. Practical mining of the causes of traffic accidents can lay the foundation for early warning of traffic accidents. The emerging user profiling technology can solve this problem very effectively, and the clustering algorithm is the key to realizing user profiling. In this paper, we have designed a portrait system for road safety based on dynamic density clustering algorithms and have developed an architecture and functional requirements for the system. To do this, we use an actual data sample representing road accidents in the United Kingdom (UK) between 2005 and 2014 for data mining and use the dynamic density clustering algorithm to cluster the dataset, discover the data flow trends of safety accidents from 2005 to 2014, discuss the road safety accident-prone scenarios, and combine the system business with realistic road safety management.
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
World Health Organization. Global status report on road safety 2018: summary. No. WHO/NMH/NVI/18.20. World Health Organization (2018)
Mujalli, R.O., de Oña, J.: Injury severity models for motor vehicle accidents: a review. Proc. Inst. Civ. Eng. Transp. 166(5), 255–270 (2013). Thomas Telford Ltd
Nasri, M., Aghabayk, K.: Assessing risk factors associated with urban transit bus involved accident severity: a case study of a Middle East country. Int. J. Crashworthiness 26(4), 413–423 (2021)
Sze, N.-N., Wong, S.C.: Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes. Accid. Anal. Prev. 39(6), 1267–1278 (2007)
Yuan, Q., Chen, H.: Factor comparison of passenger-vehicle to vulnerable road user crashes in Beijing, China. Int. J. Crashworthiness 22(3), 260–270 (2017)
Ding, C., Chen, P., Jiao, J.: Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: a machine learning approach. Accid. Anal. Prev. 112, 116–126 (2018)
Li, Y., et al.: Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network. Accid. Anal. Prev. 111, 354–363 (2018)
Zhang, Z., Ming, Y., Song, G.: Identify road clusters with high-frequency crashes using spatial data mining approach. Appl. Sci. 9(24), 5282 (2019)
Li, Z., et al.: Using support vector machine models for crash injury severity analysis. Accid. Anal. Prev. 45, 478–486 (2012)
Li, Z., et al.: Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models. Accid. Anal. Prev. 120, 139–151 (2018)
Chang, F., et al.: Injury severity analysis of motorcycle crashes: a comparison of latent class clustering and latent segmentation based models with unobserved heterogeneity. Analytic Methods Accid. Res. 32, 100188 (2021)
Taamneh, M., Taamneh, S., Alkheder, S.: Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks. Int. J. Inj. Contr. Saf. Promot. 24(3), 388–395 (2017)
Deng, S., Yu, H., Lu, C.: Research on operation characteristics and safety risk forecast of bus driven by multisource forewarning data. J. Adv. Transp. 2020 (2020)
Tirtha, S.D., Yasmin, S., Eluru, N.: Modeling of incident type and incident duration using data from multiple years. Analytic Methods Accid. Res. 28, 100132 (2020)
Maji, A., Velaga, N.R., Urie, Y.: Hierarchical clustering analysis framework of mutually exclusive crash causation parameters for regional road safety strategies. Int. J. Inj. Contr. Saf. Promot. 25(3), 257–271 (2018)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)
Acknowledgment
This research was supported in part by the NSF of China (Grant No. 62073300, U1911205, 62076225). This paper has been subjected to Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, C., Cui, Y., Hu, C. (2022). Design of Portrait System for Road Safety Based on a Dynamic Density Clustering Algorithm. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1638. Springer, Singapore. https://doi.org/10.1007/978-981-19-6135-9_20
Download citation
DOI: https://doi.org/10.1007/978-981-19-6135-9_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6134-2
Online ISBN: 978-981-19-6135-9
eBook Packages: Computer ScienceComputer Science (R0)