Large-Scale Traffic Congestion Prediction based on Multimodal Fusion and Representation Mapping
This addresses traffic congestion prediction for urban transportation systems, but it appears incremental as it builds on existing machine-learning models with novel modules.
The paper tackles the problem of predicting traffic congestion at arbitrary locations on a large-scale map by proposing an end-to-end framework using convolutional neural networks, which achieves significant results and efficient inference on real-world datasets.
With the progress of the urbanisation process, the urban transportation system is extremely critical to the development of cities and the quality of life of the citizens. Among them, it is one of the most important tasks to judge traffic congestion by analysing the congestion factors. Recently, various traditional and machine-learning-based models have been introduced for predicting traffic congestion. However, these models are either poorly aggregated for massive congestion factors or fail to make accurate predictions for every precise location in large-scale space. To alleviate these problems, a novel end-to-end framework based on convolutional neural networks is proposed in this paper. With learning representations, the framework proposes a novel multimodal fusion module and a novel representation mapping module to achieve traffic congestion predictions on arbitrary query locations on a large-scale map, combined with various global reference information. The proposed framework achieves significant results and efficient inference on real-world large-scale datasets.