Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

TL;DR

This paper introduces a novel methodology using Locality-Preserving Non-negative Matrix Factorization to analyze large-scale transportation network traffic patterns, enabling better modeling and long-term forecasting of traffic dynamics.

Contribution

The paper proposes a new approach combining LPNMF and clustering to extract and analyze large-scale spatio-temporal traffic patterns from network-wide data.

Findings

Successfully extracted meaningful large-scale traffic patterns

Demonstrated the method's effectiveness on simulated data

Provided insights into spatial-temporal traffic flow characteristics

Abstract

Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.