Speed pattern recognition technique for short‐term traffic forecasting based on traffic dynamics

Abstract

This study introduces a new short-term traffic forecasting technique, based on the dynamic features of traffic data derived from vehicles moving in urban networks. The authors goal is to forecast the values of appropriate traffic status indicators such as average travel time or speed, for one or more time steps in the future until the next half hour. The proposed forecasting technique is based on road profiles generated from the application of data clustering techniques on real traffic data. Data clustering is applied after the original feature space is transformed to a new one of a significantly lower dimension. This transformation is based on the dynamic characteristics of current traffic, expressed in the form of the speed derivatives. To evaluate the proposed technique they used two-week historical data from the city of Berlin, Germany. Extensive evaluation results indicate improvement of the forecasting accuracy after comparison with a set of existing traffic forecasting techniques.

Highway travel time estimation using multiple data sources

“…The main purpose of the proposed algorithm is to provide the value of travel time as input to a travel time information system. Since the system should provide the driver with a departure‐based travel time information, some kind of forecast must be made [16]. The nature of the algorithm requires the ability of real‐time learning of new incident situations based on predefined structure of the model, thereby gradually improving the effectiveness of the algorithm by self‐learning.…”

High‐performance traffic speed forecasting based on spatiotemporal clustering of road segments

“…In [22], a non‐parametric clustering‐based technique that provides accurate traffic forecasting was established through the exploitation of traffic data dynamics, whereas in [23], an urban traffic flow prediction system using a multifactor pattern recognition model was established, which combined a Gaussian mixture model clustered with an ANN. Tang et al.…”

Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

“…A nature of the traffic time sequence is the difference characteristic. This is noticed in [52] and they also used the first derivative of the speed but in a different way. We can get one difference data from two consecutive instances.…”