Traffic jam prediction method based on multi-source data and variable-weight combination prediction model

Abstract

The invention belongs to the traffic management technology field and relates to a traffic jam prediction method based on multi-source data and a variable-weight combination model. The research time isdivided into different time segments, vehicle GPS data and weather data of each time segment are collected, the vehicle GPS data is processed, and the average vehicle operation speed of the researchroad segment at the research time segment is acquired; an ARIMA model is trained through historical data, and ARIMA model parameters are calibrated; a BP neural network model is trained through historical data; certain weight values are assigned to the ARIMA model and the BP neural network model to acquire a combined model, and weight value error determination is carried out through the ARIMA model and the BP neural network model; the future traffic flow speed is predicted through the variable-weight combination prediction model, and traffic jam conditions are determined. The method is advantaged in that the GPS data is employed, road detection equipment sot and manpower cost can be reduced, the variable-weight combination prediction model is utilized to enhance model adaptability, prediction accuracy is improved, and a traffic jam problem can be solved in an auxiliary mode.

Description

technical field [0001] The invention relates to the field of traffic management, in particular to a traffic jam prediction method based on multi-source data and variable weight combined prediction models. Background technique [0002] In recent years, due to the increase of people's motor vehicle ownership, traffic congestion has become an increasingly serious social problem, which has brought more and more negative effects on social economic operation and personal well-being. By predicting the average running speed of vehicles on road sections in the future and evaluating traffic congestion conditions, it can provide a basis for urban traffic control and road guidance systems, making it more effective to organize traffic flow and alleviate traffic congestion. [0003] Due to the existence of a large number of uncertain factors, short-term traffic flow often has highly complex nonlinear characteristics, which makes it difficult to obtain accurate prediction results. Forecas...

AI Technical Summary

Problems solved by technology

[0003] Due to the existence of a large number of uncertain factors, short-term traffic flow often has highly complex nonlinear characteristics, which makes it difficult to obtain accurate prediction results

Forecasting technology for short-term traffic flow can be roughly divided into two categories: one is traditional statistical algorithm models, such as historical average model, moving average model, Kalman filter model, linear regression model, autoregressive sliding model, etc. The model considerations are relatively simple, the calculation is relatively simple, and it has the advantages of static stability, but it cannot accurately reflect the uncertainty and nonlinear characteristics of the traffic flow process; the other type is a model based on artificial intelligence technology, the typical representative is the BP neural network model , with strong dynamic nonlinear mapping ability

These pure traffic flow forecasting methods all have their unique information characteristics and applicable conditions, and can only reflect the future situation from different angles. There is no method that can be used under different conditions at different times Maintain absolutely excellent forecasting performance, so the application of a single forecasting model for complex traffic flow forecasting has a certain one-sidedness, and a large amount of analysis and judgment are often required to select the best method before forecasting

Images