Spatially-Intensive Decision Tree Prediction of Traffic Flow across the entire UK Road Network
Pages 116 - 119
Abstract
This paper introduces a novel approach to predicting UK-wide daily traffic counts on all roads in England and Wales, irrespective of sensor data availability. A key finding of this research is that many roads in a network may have no local connection, but may still share some common law, and this fact can be exploited to improve simulation. In this paper we show that: (1) Traffic counts are a function of dependant spatial, temporal and neighbourhood variables; (2) Large open-source data, such as school location and public transport hubs can, with appropriate GIS and machine learning, assist the prediction of traffic counts; (3) Real-time simulation can be scaled-up to large networks with the aid of machine learning and, (4) Such techniques can be employed in real-world tools. Validation of the proposed approach demonstrates an 88.2% prediction accuracy on traffic counts across the UK.
References
[1]
H. Yin, S. Wong, J. Xu, and C. Wong, "Urban traffic flow prediction using a fuzzy-neural approach," Transportation Research Part C: Emerging Technologies, vol. 10, no. 2, pp. 85--98, 2002.
[2]
S. Sun, C. Zhang, and G. Yu, "A bayesian network approach to traffic flow forecasting," IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 1, pp. 124--132, March 2006.
[3]
J. WANG, P. SHANG, and X. ZHAO, "A new traffic speed forecasting method based on bi-pattern recognition," Fluctuation and Noise Letters, vol. 10, no. 01, pp. 59--75, 2011. {Online}. Available:
[4]
Y. Lv, Y. Duan, W. Kang, Z. Li, and F. Y. Wang, "Traffic flow prediction with big data: A deep learning approach," IEEE Transactions on Intelligent Transportation Systems, pp. 865--873, April 2015.
[5]
P. Gonnet, "A queue-based distributed traffic micro-simulation," Citeseer. 2001.
[6]
I. S. Sitanggang, R. Yaakob, N. Mustapha, and A. A. B. Nuruddin, "An extended id3 decision tree algorithm for spatial data," in Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2011 IEEE International Conference on, June 2011, pp. 48--53.
[7]
K. Koperski, J. Han, and N. Stefanovic, "An efficient two-step method for classification of spatial data," in proceedings of International Symposium on Spatial Data Handling (SDH'98), 1998, pp. 45--54.
[8]
J. Vasic and H. J. Ruskin, "Cellular automata simulation of traffic including cars and bicycles," Physica A: Statistical Mechanics and its Applications, vol. 391, no. 8, pp. 2720--2729, 2012.
[9]
K. Nagel and M. Rickert, "Parallel implementation of the transims micro-simulation," Parallel Computing, vol. 27, no. 12, 2001.
[10]
DepartmentForTransport, "Statistics at DfT. 2016," https://www.gov.uk/government/organisations/department-for-transport/about/statistics, 2016.
[11]
P. Guhathakurta, Long-range monsoon rainfall prediction of 2005 for the districts and sub-division Kerala with artificial neural network. Current Science, 90,773--779, 2005.
[12]
D. V. Budescu, "Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression." Psychological Bulletin 114.3, 1993.
[13]
R. D. Yaro and R. J. Raymond, "State planning in the northeast," Land Lines: July 2000, Volume 12, Number 4, 2000.
[14]
S. B. Imandoust and M. Bolandraftar, "Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background," International Journal of Engineering Research and Applications, vol. 3, no. 5, pp. 605--610, 2013.
[15]
M. Zontul, F. Aydin, G. Dogan, S. Sener, and O. Kaynar, "Wind speed forecasting using reptree and bagging methods in kirklareli-turkey," Journal of Theoretical and Applied Information Technology.2013.
[16]
R. A. Rinkal Patel, "A reduced error pruning technique for improving accuracy of decision tree learning," International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958, 2014.
[17]
T. M. Mitchell, Machine Learning, 1st ed. New York, NY, USA: McGraw-Hill, Inc., 1997.
[18]
T. Potuzak, "Division of road traffic network for distributed simulation performed on heterogeneous clusters," in Engineering of Computer Based Systems (ECBS), 2012 IEEE 19th International Conference and Workshops on, April 2012, pp. 117--125.
[19]
D. Zehe, A. Knoll, W. Cai, and H. Aydt, "{SEMSim} cloud service: Large-scale urban systems simulation in the cloud," Simulation Modelling Practice and Theory, vol. 58, Part 2, pp. 157--171, 2015, special issue on Cloud Simulation.
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Published In
September 2016
205 pages
ISBN:9781509035045
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IEEE Press
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Published: 21 September 2016
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DS-RT '16: International Symposium on Distributed Simulation and Real-Time Applications
September 21 - 23, 2016
Uxbridge, United Kingdom
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