transportationresearchrecord期刊投稿

期刊名字 TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT缩写 TRANSPORT RES D-TR E 期刊ISSN 1361-9209 2014-2015最新影响因子 1.937 期刊官方网站 sciencedirect.com/science/journal/13619209 期刊投稿网址 ees.elsevier.com/trd/ 通讯方式 PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND, OX5 1GB 涉及的研究方向 工程技术-运输科技 出版国家 ENGLAND 出版周期 Bimonthly

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收录了！Predicting urban arterial travel time with state-space neural networks and Kalman filters作者: Liu H (Liu, Hao), van Zuylen H (van Zuylen, Henk), van Lint H (van Lint, Hans), Salomons M (Salomons, Maria)书籍团体作者: Natl Acad, TRB来源出版物: ARTIFICIAL INTELLIGENCE AND ADVANCED COMPUTING APPLICATIONS 丛书: TRANSPORTATION RESEARCH RECORD 期: 1968 页: 99-108 出版年: 2006 被引频次: 1 参考文献: 22 引证关系图 会议信息: 85th Annual Meeting of the Transportation-Research-BoardWashington, DC, JAN 22-26, 2006Transportat Res Board摘要: A hybrid model for predicting urban arterial travel time on the basis of so-called state-space neural networks (SSNNs) and the extended Kalman filter (EKF) is presented. Previous research demonstrated that SSNNs can address complex nonlinear spatiotemporal problems. However, SSNN models require off-line training with large sets of input-output data, presenting three main drawbacks: (a) great amounts of time and effort are involved in collecting, preparing, and executing these training sessions; (b) as the input-output mapping changes over time, the model requires complete retraining; and (c) if a different input set becomes available (e.g., from inductive loops) and the input-output mapping has to be changed, then retraining the model is impossible until enough time has passed to compose a representative training data set. To improve SSNN effectiveness, the EKF is proposed to train the SSNN instead of conventional approaches. Moreover, this network topology is derived from the urban travel time prediction problem. Instead of treating the neural network as a "black-box" model, the design explicitly reflects the relationships that exist in physical traffic systems. It allows the interpretation of neuron weights and structure in terms of the inherent mechanism of the network process with clear physical meaning. Model performance was tested on a densely used urban arterial in the Netherlands. Performance of this proposed model is compared with that of two existing models. Results of the comparisons indicate that the proposed model predicts complex nonlinear urban arterial travel times with satisfying effectiveness, robustness, and reliability.文献类型: Proceedings Paper语言: EnglishKeyWords Plus: REAL-TIME; PERFORMANCE通讯作者地址: Liu, H (通讯作者), Delft Univ Technol, Fac Civil Engn & Geosci, POB 5048, NL-2600 GA Delft, Netherlands地址: 1. Delft Univ Technol, Fac Civil Engn & Geosci, NL-2600 GA Delft, Netherlands2. Natl ITS Ctr Engn & Technol, Beijing 100088, Peoples R China出版商: NATL ACAD SCI, 2101 CONSTITUTION AVE, WASHINGTON, DC 20418 USAIDS 号: BFY64ISSN: 0361-1981ISBN: 978-0-309-09977-6