Utilizing Import Vector Machines to Identify Dangerous Pro-active Traffic Conditions

TL;DR

This paper demonstrates that Import Vector Machines can effectively identify dangerous traffic conditions in real-time, offering a computationally efficient alternative to Support Vector Machines for crash risk analysis.

Contribution

It introduces the application of Import Vector Machines to traffic safety, showing they perform comparably to SVMs but with reduced computational complexity.

Findings

IVMs successfully identify dangerous traffic conditions in real-time.

IVMs use fewer training points than SVMs, improving computational efficiency.

Classification performance of IVMs is comparable to SVMs.

Abstract

Traffic accidents have been a severe issue in metropolises with the development of traffic flow. This paper explores the theory and application of a recently developed machine learning technique, namely Import Vector Machines (IVMs), in real-time crash risk analysis, which is a hot topic to reduce traffic accidents. Historical crash data and corresponding traffic data from Shanghai Urban Expressway System were employed and matched. Traffic conditions are labelled as dangerous (i.e. probably leading to a crash) and safe (i.e. a normal traffic condition) based on 5-minute measurements of average speed, volume and occupancy. The IVM algorithm is trained to build the classifier and its performance is compared to the popular and successfully applied technique of Support Vector Machines (SVMs). The main findings indicate that IVMs could successfully be employed in real-time identification of dangerous pro-active traffic conditions. Furthermore, similar to the "support points" of the SVM, the IVM model uses only a fraction of the training data to index kernel basis functions, typically a much smaller fraction than the SVM, and its classification rates are similar to those of SVMs. This gives the IVM a computational advantage over the SVM, especially when the size of the training data set is large.