Link Flow Estimation on an Isolated Intersection Based on Deep Learning Models
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This paper considers the problem of short-term estimation of the link flow on an isolated intersection by means of artificial intelligence (AI). The problem is formulated as a supervised learning regression task. Several architectures of neural networks are presented to predict the link flow: recurrent neural network (RNN), convolutional neural network (CNN), and a combination of the two, with RNN treating the time dependencies and CNN extracting the spatial properties from traffic. The models are trained on synthetic data that are generated by a simulator. These models are compared with a fully-connected network. Numerical experiments are carried out on CPU and GPU. They show that deep learning models with special architecture can estimate the link flow much better than a traditional neural network on a single intersection. The obtained link flow estimates can be used in different traffic control algorithms in order to minimize the transport network congestion.
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E.P. Box George, M. Jenkins, C. Reinsel Gregory, G.M. Ljung, Time Series Analysis: Forecasting and Control (Wiley Series in Probability and Statistics, 2015).
J. Brownlee, Comparing Classical and Machine Learning Algorithms for Time Series Forecasting, Lecture notes, Deep Learning for time series, Machine Learning Mastery Pty. Ltd., Australia, 2018.
J. Song, Mathematical modelling and simulations of traffic flow, Ph.D. dissertation, Dept. Math., University of Michigan, Ann Arbor, MI, 2019.
C. Keerthika, N. Greeshma, P. Vyshnavi, K. K. Reddy, K. Indhira, V. M. Chandrasekaran, Mathematical model for traffic flow, International Journal of Engineering and Technology, Vol. 7 (No 4.10, Issue 10): 940- 941, 2018.
J. Nubert, N.G. Truong, A. Lim, H.I. Tanujaya, L. Lim, A.V. Vu, Traffic Density Estimation using a Convolutional Neural Network, Machine Learning Project, National University of Singapore, Singapore, 2018
S. Du, T. Li, X. Gong, S. Horng, A hybrid method for traffic flow forecasting using multimodal deep learning, International Journal of Computational Intelligence Systems, Vol 13 (Issue 1): 85-97, January 2020.
A. Hasnat, F. I. Rahman, Traffic flow prediction performance comparison between ARIMA and Monte Carlo simulation, Transport & Logistics the International Journal, Vol. 19 (No 46): 12-21, June 2019.
S. O. Mousavizadeh Kashi, M. Akbarzadeh, A framework for short-term traffic flow forecasting using the combination of wavelet transformation and artificial neural networks, Journal of Intelligent Transportation Systems, vol. 23 (no. 2): 60-71, 2019.
X. Luo, D. Li, S. Zhang, Traffic flow prediction during the holidays based on DFT and SVR, Journal of Sensors, Vol. 2019 (Special Issue: Sensing and Data-Driven Control for Smart Building and Smart City Systems): Article ID 6461450, 10 pages, January 2019.
J. Chai, A. Li, Deep Learning in Natural Language Processing: A-state-of-the-art Survey, 2019 International Conference on Machine Learning and Cybernetics (ICMLC), pp. 1-6, Kobe, Japan, July 2019.
A. M. Al-Safar, H. Tao, M. A. Talab, Review of deep convolutional neural networks in image classification, 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), pp. 26-31, Jakarta, Indonesia, October 2017.
N. G. Polson, V. Sokolov, Deep learning for short-term traffic flow prediction, Transportation Research Part C Emerging Technologies, Vol. 79: 1-17, 2017.
Z. Zheng, Y. Yang, J. Liu, H-N. Dai, Y. Zhang, Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics, IEEE Transactions on Intelligent Transportation Systems, Vol. 20 (Issue 10): 3927-3939, October 2019.
Y. Li, R. Yu, C. Shahabi, Y. Liu, Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting, International Conference on Learning Representations, pp. 1–12, Vancouver, BC, Canada, May 2018.
D. Kurmankhojayev, G. Tolebi, N.S. Dairbekov, Road Traffic Demand estimation and Traffic Signal control, International Conference on Engineering & MIS, Article No.:2, pp. 1-5, Astana, Kazakhstan, June 2019.
S. Hochreiter, J. Schmidhuber, Long-short term memory, Neural Computation, Vol.9 (Issue 8):1735-1780, 1997.
K. Cho, V. M. Bart, C. Gulcehre, D. Bahdanau, F. Bougares, H. Holger, Y. Bengio, Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724-1734, Doha, Qatar, October 2014.
D. Krajzewicz, J. Erdmann, M. Behrisch, L. Bieker, Recent Development and Applications of SUMO - Simulation of Urban MObility, International Journal On Advances in Systems and Measurements, Vol. 5 (Issue 3 & 4):128-138, 2012.
F. Chollet, Deep Learning with Python. Version 6. (Manning Publications, 2017, pp. 212-213).
M. Abadi et al., TensorFlow Large-Scale Machine Learning on Heterogeneous Distributed Systems, Google Research, 2015
P. Kingma, J. Ba, Adam: A method for stochastic optimization, The 3rd International Conference for Learning Representations (ICLR): 13 pages, arXiv:1412.6980, San-Diego, CA, USA, May 2015.
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