Cross-city traffic flow joint prediction method based on deep migration learning

Abstract

The present invention provides a cross-city traffic flow joint prediction method based on deep migration learning, the method comprises three parts: firstly, dividing a city into regions, using a heatmap to represent city traffic, and abstracting traffic data at different time points into "image frames"; then using a ConvLSTM method to learn spatial dependence and time dependence of the data; andfinally, using condition maximum average difference to measure distribution distance of different spatio-temporal data. The method achieves the purpose of knowledge migration by introducing the condition maximum average difference to reduce difference between a source domain and a target domain. The method proposes to use the ConvLSTM and the conditional maximum average difference for the first time, uses an idea of migration learning to apply to urban traffic flow prediction, solves the problem of prediction difficulty caused by insufficient data in similar or related cities, improves accuracy of prediction and is of great significance to construction of smart transportation system, transportation planning and risk prediction for future cities.

Description

technical field [0001] The present invention proposes a cross-city traffic flow joint prediction method based on deep transfer learning, which relates to the field of intelligent transportation, and is mainly used for traffic flow prediction in cities with similar or related distributions but insufficient research data, and in the construction of intelligent transportation systems , urban traffic planning, traffic risk prediction, and command traffic dispatching are of great significance. Background technique [0002] Mobility is the pulse of cities, affecting the daily lives of millions of people. The application of a new generation of information technology enables humans to manage production and living conditions in a more refined and dynamic manner, by embedding and equipping sensors into power supply systems, water supply systems, transportation systems, buildings and oil and gas pipelines in every corner of the world Among the various objects in the production and liv...

Images