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Assessing Dynamic Neural Networks for Travel Time Prediction

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Applied Informatics and Communication (ICAIC 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 224))

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Abstract

Ularly suitable for predicting variables like travel time, but has not been adequately investigated. This study compares the travel time prediction performance of three dynamic neural network topologies with different memory settings. The results show that the time-delay neural networks out-performed the other two topologies. This topology also performed slightly better than the multilayer perceptron neural networks.

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References

  1. Ben-Akiva, M., Cuneo, D., Hasan, M., Jha, M., Yang, Q.: Evaluation of freeway control using a microscopic simulation laboratory. Transportation Research Part C: Emerging Technologies 11(1), 29–50 (2003)

    Article  Google Scholar 

  2. Hu, T.Y.: Evaluation framework for dynamic vehicle routing strategies under real-time information. Transportation Research Record, 1774, Transportation Research Board, Washington, DC, pp. 115–122 (2001)

    Google Scholar 

  3. Liu, Y., Lin, P.-W., Lai, X., Chang, G.-L., Marquess, A.: Developments and Applications of Simulation-Based Online Travel Time Prediction System: Traveling to Ocean City, Maryland. Transportation Research Record, 1959, Transportation Research Board, Washington, D.C, pp. 92–104 (2006)

    Google Scholar 

  4. D’Angelo, M., Al-Deek, H., Wang, M.: Travel time prediction for freeway corridors. Transportation Research Record 1676, 184–191 (1999)

    Article  Google Scholar 

  5. Ishak, S., Al-Deek, H.: Performance evaluation of short-term time-series traffic prediction model. Journal of Transportation Engineering 128(6), 490–498 (2002)

    Article  Google Scholar 

  6. Innamaa, S.: Short-term prediction of travel time using neural networks on an interurban highway. Transportation 32, 649–669 (2005)

    Article  Google Scholar 

  7. Park, D., Rilett, L.R.: Forecasting multiple period freeway link travel times using modular neural networks. Transportation Research Record, 1617, Transportation Research Board, Washington, DC, pp. 163–170 (1998)

    Google Scholar 

  8. Park, D., Rilett, L.R., Han, G.: Spectral basis neural networks for real-time link travel times forecasting. Journal of Transportation Engineering 125(6), 515–523 (1999)

    Article  Google Scholar 

  9. Rilett, L.R., Park, D.: Direct forecasting of freeway corridor travel times using spectral basis neural networks. Transportation Research Record, 1752, Transportation Research Board, Washington, DC, pp. 140–147 (2001)

    Google Scholar 

  10. Palacharla, P.V., Nelson, P.C.: Application of fuzzy logic and neural networks for dynamic travel time estimation. International Transactions in Operational Research 6, 145–160 (1999)

    Article  Google Scholar 

  11. Van Lint, J.W.C., Hoogendoorn, S.P., Van Zuylen, H.J.: Freeway travel time prediction with state-space neural networks -modeling state-space dynamics with recurrent neural networks. Transportation Research Record, 1811, Transportation Research Board, Washington, DC, pp. 30–39 (2002)

    Google Scholar 

  12. Van Lint, J.W.C.: Reliable real-time framework for short-term freeway travel time prediction. Journal of Transportation Engineering 132(12), 921–932 (2006)

    Article  Google Scholar 

  13. Liu, H., Van Zuylen, H., Van Lint, H., Salomons, M.: Predicting urban arterial travel time with state-space neural networks and Kalman filters. Transportation Research Record, 1968, Transportation Research Board, Washington, D.C, pp. 99–108 (2006)

    Google Scholar 

  14. Lingras, P., Sharma, S., Zhong, M.: Prediction of recreational travel using genetically designed regression and time-delay neural networks model. Transportation Research Record, 1805, Transportation Research Board, Washington, DC, pp. 16–24 (2002)

    Google Scholar 

  15. Ishak, S., Kotha, P., Alecsandru, C.: Optimization of dynamic neural networks performance for short-term traffic prediction. Transportation Research Record, 1836, Transportation Research Board, Washington, DC, pp. 45–56 (2003)

    Google Scholar 

  16. Haykin, S.: Neural networks – A comprehensive foundation, 2nd edn. Prentice-Hall Inc., Englewood Cliffs (1999)

    MATH  Google Scholar 

  17. Shen, L., Hadi, M.: Estimation of Segment Travel Time Based on Point Traffic Detector Measurements. In: Proceeding of Transportation Research Board 88th Annual Meeting, Washington, DC (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Civil and Transportation Engineering, South China University of Technology, Guangzhou, Guangdong, 510641, China

    Luou Shen

  2. School of Environmental and Civil Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430073, China

    Min Huang

Authors
  1. Luou Shen
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  2. Min Huang
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Editor information

Editors and Affiliations

  1. Shenzhen University, Nanhai Ave. 3688, 518060, Shenzhen, Guangdong, China

    Dehuai Zeng

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© 2011 Springer-Verlag Berlin Heidelberg

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Shen, L., Huang, M. (2011). Assessing Dynamic Neural Networks for Travel Time Prediction. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23214-5_62

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  • DOI: https://doi.org/10.1007/978-3-642-23214-5_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23213-8

  • Online ISBN: 978-3-642-23214-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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