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

arXiv: https://arxiv.org/abs/1903.01678 · HTML: https://mlsanity.com/paper/1903.01678/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systems](https://mlsanity.com/paper/2409.00494/) (2024)
- [Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting](https://mlsanity.com/paper/2302.08658/) (2023)
- [Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline](https://mlsanity.com/paper/2403.14941/) (2024)
- [GC-GRU-N for Traffic Prediction using Loop Detector Data](https://mlsanity.com/paper/2211.08541/) (2022)
- [Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations](https://mlsanity.com/paper/2212.12932/) (2022)

## Abstract (arXiv)

Traffic speed prediction is a critically important component of intelligent transportation systems (ITS). Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed, which 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, we 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, we first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial-temporal multi-channel matrices. Then we 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 one-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.