Multi-time scale forecasting method for road traffic running situation

Abstract

The invention discloses a multi-time scale forecasting method for a road traffic running situation. Highway traffic parameters in different time scales are analyzed according to the running time-space characteristics of highway traffic flow; the highway road traffic running situations in different time scales are forecast by an exponential smoothing algorithm, a weighted average algorithm and a Kalman filtering method respectively; a highway road traffic running situation evaluation index system and a multi-time scale highway traffic flow running situation forecasting technology are constructed to implement the conversion from experience guide to science guide for the highway running management and the preliminary conversion from passive management to active management. Therefore, the running efficiency of a road traffic running situation forecasting system can be increased effectively, the running cost of the system is reduced, the coordination degree between road traffic guidance and management can be improved obviously, and an optimal policy is provided for improving a traffic management and control measure and planning a travel plan for a road traffic manager and a user to a large extent.

Description

technical field [0001] The invention belongs to the field of intelligent traffic control systems, and in particular relates to a multi-time scale prediction method for road traffic operation status. Background technique [0002] Road traffic operation situation prediction is the basis of intelligent transportation system to implement road traffic management and control. The accurate prediction of road traffic operation situation can ensure the safe and smooth operation of road traffic flow, help road traffic users to plan travel plans reasonably according to the changes in road traffic operation situation in the future, and help road traffic managers to know the future road traffic operation situation in advance so as to Based on the precise formulation of traffic control measures, road traffic congestion and road environmental pollution can be reduced, and road safety and traffic efficiency can be improved. [0003] In the early research on the prediction of highway traffi...

AI Technical Summary

Problems solved by technology

[0007] The traditional short-term traffic prediction module only includes the short-time scale traffic flow parameter data prediction model, and has not yet involved the joint prediction model of short-term traffic parameter prediction and traffic state influence

The state of road traffic has an important impact on the operation of road traffic flow, that is, when traffic congestion continues to increase, the prediction model of road traffic operation situation based on traffic flow parameter data alone can only reflect the change of road traffic operation situation at the current monitoring point situation, not the overall road traffic situation of the entire detection section

Therefore, using the traditional short-term road traffic operation situation prediction method will make the accuracy of the prediction results questioned, the credibility will be reduced, and it will have a huge impact on traffic management and control.

[0008] To sum up, there are still many deficiencies in the prediction method of road traffic operation situation, which are mainly reflected in: the reliability of the current road traffic operation situation prediction system, the accuracy of the prediction result is low, the calculation time is long, and it only stays in the shallow layer. At the level of traffic flow parameter prediction, a comprehensive multi-time scale prediction system for road traffic operation situation has not yet been formed. The accurate and real-time prediction of road traffic flow operation situation has not fully tapped the utilization potential of traffic parameter data.

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