APPLICATION OF CLUSTER ANALYSIS IN THE BEHAVIOUR OF TRAFFIC PARTICIPANTS RELATING TO THE USE OF SAFETY SYSTEMS AND MOBILE PHONES
Abstract
This paper presents a cluster analysis related to the behavior of traffic participants in relation to the use of safety systems and mobile phones. The data on traffic behavior were downloaded from an open data portal in Serbia. Three types of cluster analysis have been applied: hierarchical clustering, Bayesian Information Criterion (BIC) clustering and model clustering. The obtained results point to the various possibilities of using these three clustering methods in the field of traffic and suggest further research.
Keywords
Full Text:
PDFReferences
The Law on Road Traffic Safety ((“The Official Gazette of the Republic of Serbia”, No. 41/2009, 53/2010, 101/2011, 32/2013 – Constitutional Court decision, 55/2014, 96/2015 – other law, 9/2016 – Constitutional Court decision, 24/2018, 41/2018, 41/2018 – other law, 87/2018 and 23/2019)
Strategy on waterborne transport development of the Republic of Serbia, 2015 – 2025, available at: http://aler.rs/files/STRATEGIJA_razvoja_vodnog_saobracaja_Republike_Srbije_od_2015_do_2020_godine_Sl_gl_Rs_br_3_2015.pdf, Last access March 23rd 2020.
Open data portal, https://data.gov.rs/sr/, Last access March 23rd 2020.
Z. Zhong, E. Lee, M. Nejad and J. Lee, “Influence of CAV clustering strategies on mixed traffic flow characteristics: An analysis of vehicle trajectory data”, Transportation Research Part C: Emerging Technologies, vol. 115, June 2020, 102611
R. Jia, A. Khadka and I. Kim, “Traffic crash analysis with point-of-interest spatial clustering”, Accident Analysis & Prevention, vol 121, pp. 223-230. December 2018.
J. Ona, G. Lopez, R. Mujalli and F. Calvo, “Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks”, Accident Analysis & Prevention, vol. 51, pp. 1–10. March 2013.
A. Sfyridis and P. Agnolucci, “Annual average daily traffic estimation in England and Wales: An application of clustering and regression modeling”, Journal of Transport Geography, vol. 83, 102658, February 2020.
K. Ng, W. Hung and W. Wong, “An algorithm for assessing the risk of traffic accident”, Journal of Safety Research, vol. 33, pp. 387–410. October 2002.
N. Gregersen and H. Berg, “Lifestyle and accidents among young drivers”, Accident Analysis & Prevention, vol. 26, pp. 297-303. June 1994.
B. Depaire, G.Wets, K. Vanhoof, “Traffic accident segmentation by means of latent class clustering”, Accident Analysis & Prevention, vol. 40, pp. 1257–1266. July 2008.
D. Kehagias, M. Grivas, and G. Pantziou, “Using a Hybrid platform for Cluster, NoW and GRID computing”, Facta Univ. Ser.: Elec. Energ., vol. 18, No. 2, pp. 205-218. August 2005.
R Studio, retrieved from: https://rstudio.com/.
A.K. Jain, M.N. Murty, P.J. Flynn, “Data clustering: A review”, ACM Comput. Surveys, vol. 31, no. 3, pp. 264–323, 1999.
W. Yang, H. Long, L. Ma, H. Sun, “Research on Clustering Method Based on Weighted Distance Density and K-Means”, Procedia Computer Science, vol. 166, pp. 507–511, 2020.
D. Hofmeyr, “Degrees of freedom and model selection for k-means clustering”, Computational Statistics & Data Analysis, vol. 149, 2020.
O.A. Abbas, “Comparisons between data clustering algorithms”, The International Arab journal of information technology, vol. 5, no. 3, pp. 320–325. July 2008.
W. Wei, Liang. J, X. Guo, P. Song, Y. Sun, Hierarchical division clustering framework for categorical data, Neurocomputing, vol. 341, pp. 118–134, May 2019.
G. Schwarz, “Estimation the Dimension of a Model", The Annals of Statistics, vol.6, pp. 461-464, 1978.
B. Zhou, J. Hansen, “Unsupervised audio stream segmentation and clustering via the Bayesian information criterion”, In Proceedings of the 6th International conference on spoken language processing, Bejing, China, 2000.
G. McLachlan, D. Peel, “Robust Cluster Analysis via Mix- tures of Multivariate t-Distributions,” Lecture Notes in Computer Science, vol. 1451, pp. 658–666, 1998.
C. Fraley, A. Raftery, “Model-Based Clustering, Discriminant Analysis, and Density Estimation,” Journal of the American Statistical Asso- ciation, vol. 97, pp. 611–631, 2002.
Refbacks
- There are currently no refbacks.
ISSN: 0353-3670 (Print)
ISSN: 2217-5997 (Online)
COBISS.SR-ID 12826626