Method for rapidly and accurately forecasting travel time of vehicles for passing through road sections

Abstract

The invention discloses a method for rapidly and accurately forecasting the travel time of vehicles for passing through road sections, and belongs to the field of intelligent transportation. The method includes the steps of (1) obtaining information of all pass stations and history vehicle information in a time period, (2) obtaining vehicle caching of the starting point pass station A, (3) judging whether forecasting of the travel time of the vehicles for traveling from the pass station A to the pass station Bi adjacent to the pass station A is completed or not, (4) conducting finding on a vehicle record with the pass station A as a starting point and the pass station Bi as a terminal point in a license number matching mode, (5) calculating the travel time of the vehicles for traveling from the starting point pass station A to the terminal point pass station Bi in the current time period and the prior time period, (6) forecasting the travel time of the vehicles in the next time period with a Kalman algorithm, (7) calculating a jamming coefficient, and (8) completing the method for detecting and forecasting the travel time. By means of the method, the travel time of the vehicles for passing through the adjacent pass station road sections can be rapidly and accurately forecasted, and the method is suitable for intelligent transportation systems, traffic pass systems, road traffic management systems, traveler information systems and traffic flow information detection systems.

Description

technical field [0001] The invention relates to the field of intelligent transportation, in particular to a method for quickly and accurately predicting the travel time of a vehicle through a road section. Background technique [0002] Road section travel time is the most important information that is widely concerned and can best reflect the traffic operation status. Compared with the location traffic parameters, it can better evaluate the smoothness of the road, reflect the transportation efficiency of the road, and reflect the real-time road traffic. crowded situation. Road segment travel time prediction is the key research content of traffic flow guidance system, and it is considered to be one of the most effective means to predict the problem of modern urban congestion. [0003] In the prior art, there are mainly methods such as neural network prediction method, Kalman filter prediction method, time series method and GPS floating car technology when predicting travel t...

AI Technical Summary

Problems solved by technology

The invention effectively solves the problems of inaccurate prediction results and complex calculations in the prior art when predicting the travel time of road sections

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