Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data

Zongtao Duan · Yun Yang · Kai Zhang · Yuanyuan Ni · Saurab Bajgain

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Traffic Prediction and Management Techniques Traffic control and management Hydrological Forecasting Using AI

Abstract

The urban traffic flow prediction is a significant issue in the intelligent transportation system. In consideration of nonlinear and spatial-temporal features of urban traffic data, we propose a deep hybrid neural network improved by greedy algorithm for urban traffic flow prediction with taxi GPS trace. The proposed deep neural network model first combines the convolutional neural network (CNN), which extracts the spatial features, with the long short term memory (LSTM), which captures the temporal information, to predict urban traffic flow. Then, the proposed model is trained by a greedy policy to short time consumption and improves accuracy when a network goes deeper. Experimental results with real taxis GPS trajectory data from Xi'an city show that the improved deep hybrid CNN-LSTM model can achieve higher prediction accuracy and shorter time consumption compared with existing methods.

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References

2017End-to-End Multimodal Emotion Recognition Using Deep Neural Networks 2017Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data 2016Citywide Traffic Volume Estimation Using Trajectory Data

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2020A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data 2020Urban flow prediction from spatiotemporal data using machine learning: A survey 2019Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges 2021DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting

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