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Traffic speed prediction is a critically important component of intelligenttransportation systems (ITS). Recently, with the rapid development of deeplearning and transportation data science, a growing body of new traffic speedprediction models have been designed, which achieved high accuracy andlarge-scale prediction. However, existing studies have two major limitations.First, they predict aggregated traffic speed rather than lane-level trafficspeed; second, most studies ignore the impact of other traffic flow parametersin speed prediction. To address these issues, we propose a two-streammulti-channel convolutional neural network (TM-CNN) model for multi-lanetraffic speed prediction considering traffic volume impact. In this model, wefirst introduce a new data conversion method that converts raw traffic speeddata and volume data into spatial-temporal multi-channel matrices. Then wecarefully design a two-stream deep neural network to effectively learn thefeatures and correlations between individual lanes, in the spatial-temporaldimensions, and between speed and volume. Accordingly, a new loss function thatconsiders the volume impact in speed prediction is developed. A case studyusing one-year data validates the TM-CNN model and demonstrates itssuperiority. This paper contributes to two research areas: (1) traffic speedprediction, and (2) multi-lane traffic flow study.

Two-Stream Multi-Channel Convolutional Neural Network (TM-CNN) for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact