BSI: A System for Predicting and Analyzing Accident Risk
Abstract
In recent years, the rapid growth of motor vehicle ownership brings great pressure to the road traffic system and inevitably leads to a large number of traffic accidents. Therefore, it is a demanding task to build a well-developed system to identify the high-risk links, i.e., black spots, of a road network. However, most of the existing works focus on identifying black spots in a road network simply based on the statistic data of accidents, which leads to low accuracy. In this demonstration, we present a novel system called BSI, to predict and analyze the high-risk links in a road network by adequately utilizing the spatial-temporal features of accidents. First, BSI predicts the trend of accidents by a spatial-temporal sequence model. Then, based on predicted results, K-means method is utilized to discover the roads with the highest accident severity. Finally, BSI identifies the central location and coverage of a high-risk link by a modified DBSCAN clustering model. BSI can visualize the final identified black spots and provide the results to the user.
Keywords
Black spots Spatial-temporal sequence ClusteringReferences
- 1.De Oña, J., López, G., Mujalli, R., Calvo, F.J.: Analysis of traffic accidents on rural highways using latent class clustering and Bayesian networks. Accid. Anal. Prev. 51, 1–10 (2013)CrossRefGoogle Scholar
- 2.Debrabant, B., Halekoh, U., Bonat, W.H., Hansen, D.L., Hjelmborg, J., Lauritsen, J.: Identifying traffic accident black spots with poisson-tweedie models. Accid. Anal. Prev. 111, 147–154 (2018)CrossRefGoogle Scholar
- 3.Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient K-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 7, 881–892 (2002)CrossRefGoogle Scholar
- 4.Kisilevich, S., Mansmann, F., Keim, D.: P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. In: Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application, pp. 38–41. ACM (2010)Google Scholar
- 5.Liao, Z.G., Liu, B.M., Guo, Z.Y.: Road black spot identification method based on information assignment. Zhongguo Gonglu Xuebao 20(4), 122–126 (2007)Google Scholar
- 6.Pfeifer, P.E., Deutsch, S.J.: A starima model-building procedure with application to description and regional forecasting. Trans. Inst. Br. Geogr. 5, 330–349 (1980)CrossRefGoogle Scholar
- 7.Vijay, G., Ramesh, A., Kumar, M.: Identification of black spot location for providing improvement measures on selected stretch of a national highway in hyderabad, india. i-Manager’s J. Civ. Eng. 7(3), 6 (2017)CrossRefGoogle Scholar