Adaptive Kalman filtering for multi-step ahead traffic flow prediction

Abstract

Abstract-Given the importance of continuous traffic flow forecasting in most of Intelligent Transportation Systems (ITS) applications, where every new traffic data become available in every few minutes or seconds, the main objective of this study is to perform a multi-step ahead traffic flow forecasting that can meet a trade-off between accuracy, low computational load, and limited memory capacity. To this aim, based on adaptive Kalman filtering theory, two forecasting approaches are proposed. We suggest solving a multi-step ahead prediction problem as a filtering one by considering pseudo-observations coming from the averaged historical flow or the output of other predictors in the literature. For taking into account the stochastic modeling of the process and the current measurements we resort to an adaptive scheme. The proposed forecasting methods are evaluated by using measurements of the Grenoble south ring.

Grenoble Traffic Lab: An Experimental Platform for Advanced Traffic Monitoring and Forecasting [Applications of Control]

“…The problem of highway travel-time forecasting has been widely studied, and several solutions have been proposed in the literature depending on how the available historical and current-time traffic information is handled [35]- [38]. This section briefly presents the (February 7-17, 2014).…”

Urban traffic flow prediction: A MapReduce based parallel multivariate linear regression approach

“…So far, a number of traffic flow prediction model and method have been developed by effort of scientists. Such are, for instance, multivariate time series method, Kalman filter method, nonparametric regression model, support vector machine forecasting model, etc [2][3][4][5][6][7][8][9][10].…”

Spatiotemporal variable and parameter selection using sparse hybrid genetic algorithm for traffic flow forecasting

“…On the whole, 10 sites are related to the target site 21 besides itself, which are 5,7,8,11,13,14,18,19,23, and 24. Among them, sites 8, 11, and 23 contribute to the target site 21 as the role of direct upstream sites.…”

“…They employ time series analysis theory to model the temporal variation of traffic flow and forecast the future trends. Some typical models include Kalman state space filtering models, [5][6][7] autoregressive integrated moving averaging (ARIMA), 8 seasonal ARIMA (SARIMA), 9,10 k-nearest neighbor, 11 ridge regression, 12 and so on. These methods mainly characterize the temporal correlation of traffic flow at a specific location and perform well when the traffic variations are relatively stable.…”