DP-LRT: An Urban Short-term Traffic Speed Forecasting Method Based on Data Driven

Yu-long HU, Lun LUO

Abstract


Until recently, most short-term traffic speed forecasting algorithms have been applied on freeway, arterial or corridor. Short-term traffic speed forecasting on urban road network formed a more complex problem than freeway predictions due to constraints such as signalization. This paper proposes a Date Pattern and Likelihood Ratio Test statistics based (DP-LRT) method for urban short-term traffic speed forecasting from large scale taxi GPS data. Experiment showed that the proposed method reduced the mean absolute percent error and improved the prediction success rate compared with K-NN method, which was a representative data-driven method.

Keywords


Short-term traffic forecasting, Urban traffic, Likelihood ratio test


DOI
10.12783/dtcse/cscbd2019/29998

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