Short-term traffic flow forecasting method based on road network space relation constraint Lasso

Abstract

A short-term traffic flow forecasting method based on road network space relation constraint Lasso is characterized in that road network constraint is realized by the least absolute shrinkage and selection operator Lasso, road network short-term traffic flow is forecast in three steps of road network analysis, model training and real-time forecasting. The invention provides the short-term traffic flow forecasting method based on the road network space relation constraint Lasso, actually measured expressway traffic flow data are combined, an Lasso algorithm is utilized to forecast the short-term traffic flow, a road network correlation analysis and variable selection method is given, and the expressway short-term traffic flow can be forecast relatively, accurately and rapidly.

Description

technical field [0001] The invention belongs to the fields of machine learning and data mining, is mainly used in expressway traffic management systems, and is specifically a short-term traffic flow prediction method based on Lasso constrained by road network spatial relations. Background technique [0002] In recent years, transportation systems across the globe have faced unprecedented challenges. With the development of the economy, the demand for traffic increases rapidly, and traffic congestion is not uncommon, and it is becoming more and more severe. Vehicles are slowing down and congestion time is increasing, and the traffic network has become very fragile. At the same time, the increase in travel time due to delays leads to increased economic losses and environmental pollution. The annual economic losses caused by traffic jams in the United States are as high as 63.1 billion U.S. dollars. The large cities with a population of more than one million in my country have ...

AI Technical Summary

Problems solved by technology

[0007] Short-term traffic flow forecasting serves the fast-changing traffic control and guidance, the forecast period is significantly shortened, the changing law of traffic flow is becoming more and more blurred, and the impact of disturbance is more intense. The uncertainty of short-term traffic flow is becoming more and more obvious. Increased difficulty

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