Road traffic service level prediction method based on space-time characteristic aggregation

Abstract

The invention has the observation characteristic that road traffic time domain characteristic and road traffic space domain characteristic are used for fusing multiple data so as to obtain the service level predicting result by a maximum entropy model, so that the predicting result is ensured to be more accurate; and by adopting the technology for prediction, the traffic control department can issue the crowding situation of the roads in the urban area, the traffic state and the service level in advance according to the predicting result, so that the invention provides reference for going out, leads the public to avoid the rush hour and the crowding road sections, is beneficial to inducing and evacuating the traffic, effectively eases the traffic pressure, and provides decision support for traffic guidance.

Description

technical field [0001] The invention belongs to the field of forecasting and forecasting of road traffic status and service level, and in particular relates to a road traffic service level forecasting method based on spatio-temporal feature aggregation. Background technique [0002] The most commonly used models in the field of traffic state forecasting mainly include historical trend models, neural network models, time series models, Kalman filter models, and non-parametric regression models, and the closest to this technology is the time series model. A time series is a collection of observations arranged in time order. The main feature of the time series model is to recognize the dependencies and correlations between observations. It is a dynamic model that can be applied to dynamic forecasting. [0003] In the past, road traffic state prediction was mainly aimed at time-related traffic data, that is, for the time-domain characteristics of traffic, so the time series mod...

AI Technical Summary

Problems solved by technology

But the ensuing problem is that the previous forecasting models and algorithms cannot integrate multiple data well when performing multiple data fusion, such as the time series model, because it is mainly aimed at time-related Time domain features, while space domain features do not have obvious time series characteristics, so when performing data fusion, its limitations are revealed

The road traffic state prediction obtained by it will produce a large error

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