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Chen, Y, Guizani, M, Zhang, Y, Wang, L, Crespi, N, Lee, GM and Wu, T (2018) When Traffic Flow Prediction and Wireless Big Data Analytics Meet. IEEE Network, 33 (3). pp. 161-167. ISSN 0890-8044
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Abstract
In this article, we verify whether or not prediction performance can be improved by fitting the actual data to optimize the parameter values of a prediction model. The traffic flow prediction is an important research issue for solving the traffic congestion problem in an Intelligent Transportation System (ITS). The traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns. Such prediction is possible by analyzing the realtime transportation data from correlative roads and vehicles. The verification in this article is conducted by comparing the optimized and the normal time series prediction models. With the verification, we can learn that the era of big data is here and will become an important aspect for the study of traffic flow prediction to solve the congestion problem. Experimental results of a case study are provided to verify the existence of the performance improvement in the prediction, while the research challenges of this data-analytics-based prediction are presented and discussed.
| Item Type: | Article |
|---|---|
| Additional Information: | © 2018 IEEE |
| Uncontrolled Keywords: | 0906 Electrical And Electronic Engineering, 0805 Distributed Computing |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Institute of Electrical and Electronics Engineers |
| Date Deposited: | 05 Jul 2018 14:32 |
| Last Modified: | 04 Sep 2021 10:21 |
| DOI or ID number: | 10.1109/MNET.2018.1800134 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/8921 |
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