Abstract
Short-term traffic speed prediction plays an important role in the field of Intelligent Transportation Systems (ITS). Usually, traffic speed forecasting can be divided into single-step-ahead and multi-step-ahead. Compared with the single-step method, multi-step prediction can provide more future traffic condition to road traffic participants for guidance decision-making. This paper proposes a multi-step traffic speed forecasting by using ensemble learning model with traffic speed detrending algorithm. Firstly, the correlation analysis is conducted to determine the representative features by considering the spatial and temporal characteristics of traffic speed. Then, the traffic speed time series is split into a trend set and a residual set via a detrending algorithm. Thirdly, a multi-step residual prediction with direct strategy is formulated by the ensemble learning model of stacking integrating support vector machine (SVM), CATBOOST, and K-nearest neighbor (KNN). Finally, the forecasting traffic speed can be reached by adding predicted residual part to the trend one. In tests that used field data from Zhongshan, China, the experimental results indicate that the proposed model outperforms the benchmark ones like SVM, CATBOOST, KNN, and BAGGING.
Highlights
Various types of vehicles have pushed human society forward by making the mobility of people and goods possible, providing faster and more comfortable travel experience, facilitating social interactions, and so on
Apart from consequences like global warming and fossil fuel depletion, traffic congestion is one of the most negative effects that can be perceived by each traffic participant and it can inevitably result in a series of problems, such as traffic accidents, energy overconsumption, and significant travel delay [1]
support vector machine (SVM) could deal with overfitting problem and have good generalization performance because SVM can construct a mapping from one dimensional input vector into high-dimensional space by the use of reproducing kernels
Summary
Various types of vehicles have pushed human society forward by making the mobility of people and goods possible, providing faster and more comfortable travel experience, facilitating social interactions, and so on. Transportation and traffic researchers believe that the Intelligent Transportation Systems (ITS) is a promising solution to improve transportation management and can provide much better services that can eventually lead to less congestion than traditional methods [3,4] Among such services, traffic prediction plays an important role in ITS because forecasting information can be utilized to support traffic guidance, signal optimization, and so on. Based on the aforementioned issues, a novel multi-step speed prediction model is proposed by considering spatial-temporal dependencies and using ensemble learning. A novel multi-step prediction with detrending and direct strategy is achieved by the ensemble learning model of stacking (DDSELM) to forecast travel speed using spatial-temporal characteristics.
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