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Smart city article details

Title	Multimodal Traffic Travel Time Prediction
ID_Doc	38582
Authors	Fan S.; Li J.; Lv Z.; Zhao A.
Year	2021
Published	Proceedings of the International Joint Conference on Neural Networks, 2021-July
DOI	http://dx.doi.org/10.1109/IJCNN52387.2021.9533356
Abstract	With the continuous growth of urban population, it is urgent for people to accurately plan the travel time. Therefore, travel time prediction of urban areas has become a key research direction in the field of smart cities. At present, several studies on travel time prediction are only conducted on a single mode, where the prediction process only treats a certain vehicle as an isolated traffic state on the route. However, the factors affecting traffic are extremely complex, thus making it very difficult to produce a comprehensive forecast. Based on this situation, the mixed existing model and mutual influence of multiple modes of transportation in the city are fully considered, and a multimodal deep learning model namely MC-GRU (Multimodal Convoluted Gated Recurrent Unit Network) is proposed. At the same time, to solve the problem of some objective factors, such as departure time and travel distance, we propose an attribute module to deal with these implicit factors. In addition, to explore the interaction between different modes of vehicles, a feature fusion module for obtaining the interaction effect between different modes of vehicles is proposed. Finally, we use GRU to learn the long-term dependence. MC-GRU can realize the accurate prediction of travel time in multimodal traffic state, as well as implement travel time prediction for three types of travel modes. The experimental results show that MC-GRU achieves higher prediction accuracy on a challenging real world dataset as compared with MAE, MAPE and RMSE. © 2021 IEEE.
Author Keywords	attribute module; feature fusion module; GRU; multimodal

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