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Urban Arterial Travel Time Prediction with State-Space Neural Networks and Kalman Filters 这是题目 查SCI检索号 谢谢 |
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qfw_68
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Flexing(金币+2, 博学EPI+1):谢谢 2010-04-30 11:19
YUE-jf(金币+1):你是怎么查到的呢？ 2010-04-30 11:40
Flexing(金币+2, 博学EPI+1):谢谢 2010-04-30 11:19
YUE-jf(金币+1):你是怎么查到的呢？ 2010-04-30 11:40
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Predicting urban arterial travel time with state-space neural networks and Kalman filters SCI检索号为： ISI:000245460800012 FN ISI Export Format VR 1.0 PT S AU Liu, H van Zuylen, H van Lint, H Salomons, M AF Liu, Hao van Zuylen, Henk van Lint, Hans Salomons, Maria GP Natl Acad, TRB TI Predicting urban arterial travel time with state-space neural networks and Kalman filters SO ARTIFICIAL INTELLIGENCE AND ADVANCED COMPUTING APPLICATIONS SE TRANSPORTATION RESEARCH RECORD LA English DT Proceedings Paper CT 85th Annual Meeting of the Transportation-Research-Board CY JAN 22-26, 2006 CL Washington, DC SP Transportat Res Board ID REAL-TIME; PERFORMANCE AB 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. C1 Delft Univ Technol, Fac Civil Engn & Geosci, NL-2600 GA Delft, Netherlands. Natl ITS Ctr Engn & Technol, Beijing 100088, Peoples R China. RP Liu, H, Delft Univ Technol, Fac Civil Engn & Geosci, POB 5048, NL-2600 GA Delft, Netherlands. NR 22 TC 1 PU NATL ACAD SCI PI WASHINGTON PA 2101 CONSTITUTION AVE, WASHINGTON, DC 20418 USA SN 0361-1981 BN 978-0-309-09977-6 J9 TRANSP RES REC PY 2006 IS 1968 BP 99 EP 108 PG 10 SC Engineering, Civil; Transportation; Transportation Science & Technology GA BFY64 UT ISI:000245460800012 ER [ Last edited by qfw_68 on 2010-4-30 at 11:03 ] |
3楼2010-04-30 10:56:28