Evaluation of Forecast Scheme Performances Based on Statistical Error Measurements
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2014, Vol 3, Issue 8
Abstract
This paper will review various forecast schemes. Some of the forecast schemes are based on data mining. Forecast schemes are introduced to reduce urban traffic congestion and to manage the travel information. The underlying reason is that the capacity of transportation traffic system is regularly exceeded by traffic demand. We will evaluate and compare forecast schemes performance based on statistical error measurements. 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, which will be strongly disapproved of by travelers. The third strategy is focus on efficient and intelligent utilization of the existing transportation infrastructure. This strategy is a best trade-off and gains more and more attention. Currently, the Intelligent Transportation System (ITS) is the most promising approach to implementation of the third strategy. Various forecast schemes have been introduced to manage the travel information like clustering, classification schemes. Some of the most often used methods to traffic forecast are limited to expressways during daytime hours. Meanwhile the robustness and accuracy of exponential smoothing forecasting has led to its widespread use in applications where a large number of series necessitates an automated procedure, such as inventory control.
Authors and Affiliations
Ebrahim Kittani, Mothana, Jamal Raiyn
Keywords
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