Abstract
Short-term traffic volume forecasting represents a critical need for Intelligent Transportation Systems. In this paper, we propose an improved K-Nearest Neighbor model, named I-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, I-KNN considers the spatial–temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. The results of the performance testing conducted in this paper demonstrates the superior predictive accuracy and drastically lower computational requirements of the I-KNN compared to either the neural network or the nearest neighbor approach. And also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.
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
Zhang, L., Wei, H., et al.: An improved k-nearest neighbor model for short-term traffic flow prediction. In: Intelligent and Integrated Sustainable Multimodal Transportation Systems Proceedings from the 13th COTA International Conference of Transportation Professionals (CICTP 2013), Procedia - Social and Behavioral Sciences, vol. 96, pp. 653–662 (2013)
Marx, V.: The big challenges of big data. Nature 498(7453), 255–260 (2013)
Zhang, J., Wang, F., Wang, K., Lin, W., Xu, X., Chen, C.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011)
Wang, Y., Papageorgiou, M., Messmer, A.: A real-time freeway network traffic surveillance tool. IEEE Trans. Control Syst. Technol. 14(1), 18–32 (2006)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.-Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Zhang, Y.: Special issue on short-term traffic flow forecasting. Transp. Res. Part C: Emerg. Technol. 43, 1–2 (2014)
Chandra, S., Al-Deek, H.: Predictions of freeway traffic speeds and volumes using vector autoregressive models. J. Intell. Transp. Syst. 13(2), 53–72 (2009)
Schof, M., Helbing, D.: Empirical features of congested traffic states and their implications for traffic modeling. Transp. Sci. 41(2), 135–166 (2007)
Jeong, Y.S., Byon, Y.J., Castro-Neto, M.M., Easa, S.M.: Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 14(4), 1700–1707 (2013)
Acknowledgements
The work is supported in part by Department of Education of Guangdong Province under Grant 2015KQNCX193.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, S., Zhang, D. (2016). The Short-Term Traffic Flow Prediction Based on MapReduce. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_55
Download citation
DOI: https://doi.org/10.1007/978-981-10-3614-9_55
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3613-2
Online ISBN: 978-981-10-3614-9
eBook Packages: Computer ScienceComputer Science (R0)