City traffic flow breakdown prediction based on fuzzy rough set

Abstract

Abstract:In city traffic management, traffic breakdown is a very important issue, which is defined as a speed drop of a certain amount within a dense traffic situation. In order to predict city traffic flow breakdown accurately, in this paper, we propose a novel city traffic flow breakdown prediction algorithm based on fuzzy rough set. Firstly, we illustrate the city traffic flow breakdown problem, in which three definitions are given, that is, 1) Pre-breakdown flow rate, 2) Rate, density, and speed of the traffic flow breakdown, and 3) Duration of the traffic flow breakdown. Moreover, we define a hazard function to represent the probability of the breakdown ending at a given time point. Secondly, as there are many redundant and irrelevant attributes in city flow breakdown prediction, we propose an attribute reduction algorithm using the fuzzy rough set. Thirdly, we discuss how to predict the city traffic flow breakdown based on attribute reduction and SVM classifier. Finally, experiments are conducted by collecting data from I-405 Freeway, which is located at Irvine, California. Experimental results demonstrate that the proposed algorithm is able to achieve lower average error rate of city traffic flow breakdown prediction.

Traffic Network Modeling and Extended Max‐Pressure Traffic Control Strategy Based on Granular Computing Theory

“…On the other hand, the traffic data collected from different devices contains measure errors; these data may be incomplete, inaccurate, and inconsistent [10,11]. Rough set theory as one important part of granular computing theory [12] can deal with the uncertain features of traffic data well for the traffic problem solving [13]. Researchers have applied rough set theory in intelligent transportation system area, such as traffic data processing, traffic events recognition, and traffic flow prediction, since the rough representation of traffic data can express the actual situations of traffic network, improve the accuracy of traffic information, and obtain the hiding information.…”