Journal of Transportation Technologies

Volume 4, Issue 1 (January 2014)

ISSN Print: 2160-0473   ISSN Online: 2160-0481

Google-based Impact Factor: 1.62  Citations  h5-index & Ranking

Real-Time Short-Term Forecasting Based on Information Management

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DOI: 10.4236/jtts.2014.41002    4,897 Downloads   7,412 Views  Citations

ABSTRACT

Traffic congestions and road accidents continue to increase in industry countries. There are three basic strategies to relieve congestion. The first strategy is to increase the transportation infrastructure. However, this strategy is very expensive and can only be accomplished in the long-term. The second strategy is to limit the traffic demand or make traveling more expensive that will be strongly opposed by travelers. The third strategy is to focus on efficient and intelligent utilization of the existing transportation infrastructures. This strategy is gaining more and more attention because its well. Currently, the Intelligent Transportation System (ITS) is the most promising approach to implementing the third strategy. Various forecast schemes have been proposed to manage the traffic data. Many studies showed that the moving average schemes offered meaningful results compared to different forecast schemes. This paper considered the moving average schemes, namely, simple moving average, weighted moving average, and exponential moving average. Furthermore, the performance analysis of the shortterm forecast schemes will be discussed. Moreover, the real-time forecast model will consider the abnormal condition detection.

Share and Cite:

J. Raiyn and T. Toledo, "Real-Time Short-Term Forecasting Based on Information Management," Journal of Transportation Technologies, Vol. 4 No. 1, 2014, pp. 11-21. doi: 10.4236/jtts.2014.41002.

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