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Short-term traffic state estimation and prediction based on spatiotemporal neural networks
Spatiotemporal neural networks (NN) models have recently achieved competitive results for short-term traffic prediction and achieved outstanding outcomes. However, two problems still require further study in terms of model performance and inner mechanism understanding: (1). The forecasting model affected by many aspects like model inputs, model structure, external factors, and optimisation function. How to design an appropriate framework for short-term traffic state prediction? (2). The existing related studies mainly use the knowledge and advantage from the neural network field, but how to incorporate transport domain kn owledge with the above framework? Therefore, th e aims of this study are: (1) Presenting an overall framework from data management to model training for traffic network state estimation and prediction, which provides better forecasting results and APIs for other applications. (2) Understanding the relations of components inside the framework and improving it by integrating transport domain knowledge. The outcomes of this study provide a general framework of the NN-based traffic forecasting model for practice and a better understanding, which will benefit the short-term traffic forecasting related research and industry.
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