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Flexing

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Urban Arterial Travel Time Prediction with State-Space Neural Networks and Kalman Filters

这是题目
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1楼 2010-04-30 10:45:18

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2楼2010-04-30 10:53:44

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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

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 ]