Name
Title | ||
|---|---|---|
Two-Stream Multi-Channel Convolutional Neural Network (TM-CNN) for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact. |
Abstract | ||
|---|---|---|
Traffic speed prediction is a critically important component of intelligent transportation systems. Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed that achieved high accuracy and large-scale prediction. However, existing studies have two major limitations. First, they predict aggregated traffic speed rather than lane-level traffic speed; second, most studies ignore the impact of other traffic flow parameters in speed prediction. To address these issues, the authors propose a two-stream multi-channel convolutional neural network (TM-CNN) model for multi-lane traffic speed prediction considering traffic volume impact. In this model, the authors first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial-temporal multi-channel matrices. Then the authors carefully design a two-stream deep neural network to effectively learn the features and correlations between individual lanes, in the spatial-temporal dimensions, and between speed and volume. Accordingly, a new loss function that considers the volume impact in speed prediction is developed. A case study using 1-year data validates the TM-CNN model and demonstrates its superiority. This paper contributes to two research areas: (1) traffic speed prediction, and (2) multi-lane traffic flow study. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1177/0361198120911052 | TRANSPORTATION RESEARCH RECORD |
DocType | Volume | Issue |
Journal | 2674.0 | 4.0 |
ISSN | Citations | PageRank |
0361-1981 | 1 | 0.37 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ke Ruimin | 1 | 89 | 6.69 |
| Wan Li | 2 | 1 | 0.37 |
| Zhiyong Cui | 3 | 16 | 1.39 |
| Yinhai Wang | 4 | 292 | 39.37 |