Abstract
Urban traffic is an extremely complex dynamic system. Urban traffic modeling and forecasting is still a challenge, the main difficulty is how to determine supply and demand and how to parameterize the model. This paper tries to solve these problems with the help of a large number of floating taxi data. We describe the first solution to the challenge of finding a taxi destination. The tasks included at the beginning of its trajectory prediction of a taxi destination, it is expressed as the GPS point of variable length sequences, and related information, such as the departure time, the driver id and customer information. We use a neural network based approach that is almost completely automated. The architecture we are trying to use is a multi-layer perception, bidirectional recursive neural network, and a model inspired by the recently introduced memory network. Our approach can be easily adapted to other applications, with the goal of predicting the fixed-length output of a variable length sequence.
Foundation item: Supported by Jiangxi provincial education department science and technology research project (GJJ161333).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Doetsch, P., Kozielski, M., Ney, H.: Fast and robust training of recurrent neural networks for offline handwriting recognition. In: 2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 279–284. IEEE (2014)
Graves, A., Mohamed, A.-R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649. IEEE (2013)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)
Zhou, X., Taylor, J.: DTALite: a queue-based macroscopic traffic simulator for fast model evaluation and calibration. Cogent Eng. 1(1), 961345 (2014)
Sak, H., Senior, A., Beaufays, F.: Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv preprint arXiv:1402.1128 (2014)
Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. arXiv preprint arXiv:1502.03044 (2015)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473,2014.12
Li, P., Mirchandani, P., Zhou, X.: Hierarchical multiresolution traffic simulator for metropolitan areas: architecture, challenges, and solutions. Transp. Res. Rec.: J. Transp. Res. Board 2497, 63–72 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Z., Li, S. (2018). Mathematical Model of Cellular Automata in Urban Taxi Network – Take GanZhou as an Example. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_11
Download citation
DOI: https://doi.org/10.1007/978-981-13-1651-7_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1650-0
Online ISBN: 978-981-13-1651-7
eBook Packages: Computer ScienceComputer Science (R0)