Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach

Leo Tišljarić,Sofia Fernandes,Tonči Carić,João Gama

doi:10.3390/app112412017

Abstract

The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, which often leads to potentially dangerous situations that can be described as anomalies. The tensor-based methods emerged only recently in applications related to traffic anomaly detection. They outperform other models regarding simultaneously capturing spatial and temporal components, which are of immense importance in traffic dataset analysis. This paper presents a tensor-based method for extracting the spatiotemporal road traffic patterns represented with the speed transition matrices, with the goal of anomaly detection. A novel anomaly detection approach is presented, which relies on computing the center of mass of the observed traffic patterns. The method was evaluated on a large road traffic dataset and was able to detect the most anomalous parts of the urban road network. By analyzing spatial and temporal components of the most anomalous traffic patterns, sources of anomalies can be identified. Results were validated using the extracted domain knowledge from the Highway Capacity Manual. The anomaly detection model achieved a precision score of 92.88%. Therefore, this method finds its usages for safety experts in detecting potentially dangerous road segments, urban traffic planners, and routing applications.

Highlights

The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, especially in rush hours
The grid-based map segmentation and the filtering were applied to the dataset, and the results are shown in Figure 7, where green represents the cell with the data, and red cells are excluded from this research
This paper presents a novel method for the extraction of road traffic patterns and anomaly detection using tensorbased method

Summary

Introduction

The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, especially in rush hours. System (ITS) solutions present applications that can be useful in detecting and dealing with problems that are related to congestion like increased pollution [1]. In this context, anomaly detection represents attractive research topic in the ITS field because it is one of the crucial parts in detecting dangerous and potentially life threatening situations on the road traffic network. This paper presents a tensor-based method for the extraction of the spatiotemporal road traffic patterns, with the aim of detecting anomalies on the urban road network. Every edge ei ∈ E from a graph G represents a road network segment with the starting vertex vi and the ending vertex v j. Where origin edge of transition is ei and destination edge is ei+1

Objectives

Methods

Results

Conclusion