Abstract：Traffic congestion, traffic safety, and traffic related environmental problems caused by rapid urbanization have become increasingly important with "human-centered" intelligent transportation systems (ITS) needed that are based on individual driving characteristics. Thus, common driving styles were analyzed to more effectively utilize the existing road transport resources. However, driving styles are difficult to identify and quantify, which leads to difficulties modelling the individual driving characteristics. In this paper, the driving characteristics of 16 drivers on actual roads were analyzed using a topical model. The hidden driving characteristics were extracted and used to convert the data from "driving style-driving behaviour data" into "driving style-driving state-driving behaviour data". The discovered driving style structural information provides theoretical support for more efficient intelligent transportation systems. The reconstructed model data is shown to correlate well with the original data which verifies the model for analyzing driving style diversity.
Wang J, Zhang L, Zhang D, et al. An adaptive longitudinal driving assistance system based on driver characteristics[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1):1-12.
Marsden G, Mcdonald M, Brackstone M. Towards an understanding of adaptive cruise control[J]. Transportation Research Part C-Emerging Technologies, 2001, 9(1):33-51.
郭敏, 杜怡曼, 吴建平. 微观交通仿真基础理论及应用案例[M]. 北京:人民交通出版社, 2007.GUO Min, DU Yiman, WU Jianping. The Basic Theory of Microscopic Traffic Simulation and Its Applications[M]. Beijing:China Communications Press, 2007. (in Chinese)
Jarasuniene A. Research into intelligent transport systems (ITS) technologies and efficiency[J]. Transport, 2007, 22(2):61-67.
杨超, 奚宽武. 城镇化与交通协调发展的国际经验与启示[J]. 交通世界(运输·车辆), 2014(8):36-42.YANG Chao, XI Kuanwu. International experience and implications of urbanization and coordinated development of transportation[J]. Transpoworld (Transport·Vehicle), 2014(8):36-42. (in Chinese)
Piao J N, Mcdonald M. Low speed car following behaviour from floating vehicle data[C]//Proceedings of the IEEE IV 2003:Intelligent Vehicles Symposiums. Columbus, USA:IEEE, 2003:462-467.
Reed M P, Green P A. Comparison of driving performance on-road and in a low-cost simulator using a concurrent telephone dialling task[J]. Ergonomics, 1999, 42(8):1015-1037.
Hoogendoorn S P, Van Zuylen H J, Schreuder M, et al. Microscopic traffic data collection by remote sensing[J]. Journal of the Transportation Research Board, 2003, 1855(1):121-128.
Brackstone M, Mcdonald M. An instrumented vehicle for microscopic monitoring of driver behaviour[C]//Proceedings of the IEEE-IEE Vehicle Navigation and Informations Systems Conference. Ottawa, Canada:IEEE, 1993:401-404.
Mcdonald M, Brackstone M A. The role of the instrumented vehicle in the collection of data on driver behaviour[C]//IEE Colloquium on Monitoring of Driver and Vehicle Performance. London, UK:IET, 1997:1-7.
Zelong L, Maozhen L, Yang L, et al. Performance evaluation of latent Dirichlet allocation in text mining[C]//Proceedings of the 8th International Conference on Fuzzy Systems and Knowledge Discovery. Shanghai, China:IEEE, 2011:2695-2698.
李庆丰. 基于主题模型的多文档自动文摘方法研究[D]. 大连:大连海事大学, 2013.LI Qingfeng. Research on the Method of Multi-document Summarization Based on Topic Model[D]. Dalian:Dalian Maritime University, 2013. (in Chinese)
李晓旭. 基于概率主题模型的图像分类和标注的研究[D]. 北京:北京邮电大学, 2012.LI Xiaoxu. The Study of Image Classification and Annotation Based on Probabilistic Topic Model[D]. Beijing:Beijing University of Posts and Telecommunications, 2012. (in Chinese)
Lienou M, Maitre H, Datcu M. Semantic annotation of satellite images using latent Dirichlet allocation[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(1):28-32.
Youngchul C, Junghoo C. Social-network analysis using topic models[C]//Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012). Portland, USA:ACM, 2012:565-574.
骆国靖. 基于主题模型的模块化网络和社区挖掘研究[D]. 杭州:浙江大学, 2008.LUO Guojing. The Study of Module Network and Community Mining Based on Topic Model[D]. Hangzhou:Zhejiang University, 2008. (in Chinese)
Deerwester S, Dumais S T, Furnas G W, et al. Indexing by latent semantic analysis[J]. Journal of the American Society for Information Science, 1990, 41(6):391-407.
Hofmann T. Probabilistic latent semantic analysis[C]//Proceedings of the Uncertainty in Artificial Intelligence. Stockholm, Sweden:Morgan Kaufmann, 1999:289-296.
Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3(4-5):993-1022.
Smith A, Roberts G O. Bayesian computation via the Gibbs sampler and related Markov-chain Monte-Carlo methods[J]. Journal of the Royal Statistical Society Series B-Methodological, 1993, 55(1):3-23.