Multiple-Feature-Based Vehicle Supply–Demand Difference Prediction Method for Social Transportation

Abstract

Big data for social transportation brings unprecedented opportunities for us to solve the transportation problems that cannot be solved by traditional methods and build the next generation of the intelligent transportation system (ITS). As one of the important functions of the ITS, supply-demand difference prediction for autonomous vehicles provides a decision basis for its control. In this article, a new learning process is proposed with Multiple feature Extraction and Fusion utilizing the combination of deep and shallow Features (MEFF) (the spatial deep features, (short and long) temporal deep features, and fuzzy shallow (semantic) features). The spatial deep features are captured with residual network and dimension reduction in spatial deep block. The fuzzy shallow (semantic) features are captured with multiattention fuzzy mechanism in the fuzzy shallow block. With the fused spatial deep features and fuzzy shallow features, the temporal deep features are captured with long short-term memory (LSTM) and attention mechanism in the temporal and prediction block to get the final prediction results. Based on two different distributions of membership attention (mean distribution and Gaussian distribution) in the fuzzy shallow block, our process MEFF has two methods, i.e., MEFF-mean method and MEFF-Gaussian method. Extensive experiments show that our methods provide more accurate and stable prediction results than the existing state-of-art-methods.