请帮忙查询一下论文是否被SCI检索

Question

论文是：Urban Arterial Travel Time Prediction with State-Space Neural Networks and Kalman Filters
作者信息：Liu, H., Lint, van, J.W.C., Zuylen, van, H.J., Salomons, M.,
如果查到的话请贴出检索信息，谢谢！

Answer 1

文件名应该是Predicting urban arterial travel time with state-space neural networks and Kalman filters吧，已经可以检索到了，检索信息如下（BibTex的bib文件内容）：

@inproceedings{ ISI:000245460800012,

Author = {Liu, Hao and van Zuylen, Henk and van Lint, Hans and Salomons, Maria},

Book-Group-Author = {{Natl Acad, TRB}},

Title = {{Predicting urban arterial travel time with state-space neural networks

   and Kalman filters}},

Booktitle = {{ARTIFICIAL INTELLIGENCE AND ADVANCED COMPUTING APPLICATIONS}},

Series = {{TRANSPORTATION RESEARCH RECORD}},

Year = {{2006}},

Number = {{1968}},

Pages = {{99-108}},

Note = {{85th Annual Meeting of the Transportation-Research-Board, Washington,

   DC, JAN 22-26, 2006}},

Organization = {{Transportat Res Board}},

Abstract = {{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.}},

Publisher = {{NATL ACAD SCI}},

Address = {{2101 CONSTITUTION AVE, WASHINGTON, DC 20418 USA}},

Type = {{Proceedings Paper}},

Language = {{English}},

Affiliation = {{Liu, H (Reprint Author), Delft Univ Technol, Fac Civil Engn \& Geosci, POB 5048, NL-2600 GA Delft, Netherlands.

   Delft Univ Technol, Fac Civil Engn \& Geosci, NL-2600 GA Delft, Netherlands.

   Natl ITS Ctr Engn \& Technol, Beijing 100088, Peoples R China.}},

ISSN = {{0361-1981}},

ISBN = {{978-0-309-09977-6}},

Keywords-Plus = {{REAL-TIME; PERFORMANCE}},

Subject-Category = {{Engineering, Civil; Transportation; Transportation Science \& Technology}},

Number-of-Cited-References = {{22}},

Times-Cited = {{1}},

Doc-Delivery-Number = {{BFY64}},

Unique-ID = {{ISI:000245460800012}},

}

Answer 2

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-Board
Washington, DC, JAN 22-26, 2006
Transportat 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: English
KeyWords 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, Netherlands
2. Natl ITS Ctr Engn & Technol, Beijing 100088, Peoples R China
出版商: NATL ACAD SCI, 2101 CONSTITUTION AVE, WASHINGTON, DC 20418 USA
IDS 号: BFY64
ISSN: 0361-1981
ISBN: 978-0-309-09977-6