Time forecasting method and device

Abstract

The invention provides a time forecasting method and device and relates to the filed of dynamic traffic information service. The time forecasting method and device can improve accuracy of travel time forecasting on the unusual traffic condition. The time forecasting method includes dividing a path from the departing place to the destination into N links; dividing the N links into X link sets according to the relevance of historical travel time of the N links; acquiring link matrixes corresponding to the X link sets respectively, and making the time in the preset time interval before the forecasting time to be the historical time; performing Autoregressive Integrated Moving Average (ARIMA) training on the link matrixes corresponding to X link sets respectively to obtain the travel time of the X link sets at the forecasting time; acquiring the sum of the travel time of the X link sets at the forecasting time to serve as the travel forecasting time of the forecasting time from the departing place to the destination. The time forecasting method and device are used for travel time forecasting.

Description

technical field [0001] The invention relates to the field of dynamic traffic information services, in particular to a time prediction method and device. Background technique [0002] Travel time prediction is an important part of traffic information services. Through travel time prediction, not only can dynamic route planning be effectively performed to avoid congestion points, but departure time can also be reasonably planned to avoid congestion time periods. [0003] The historical data of road link travel time presents periodic characteristics according to the characteristic day of the week and each time period in a day, and presents a more obvious continuous feature in time, so the prediction of road link travel time based on time series is more common. The general idea and method are as follows: firstly extract the average value of travel time of a specific link in each time period of each characteristic day of each week, then make a difference between the historical tr...

AI Technical Summary

Problems solved by technology

However, due to the fact that today's urban traffic is often affected by events, its statistical characteristics are not stable, and the urban traffic situation presents a spatial correlation, that is, if the downstream link is congested, the congestion will go upstream along its adjacent link Transfer, the time series differentiated according to the above method is difficult to meet the stationarity requirements, so in abnormal traffic conditions, the accuracy of time prediction is low

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