Reliable Traffic State Identification Using High-Resolution Data: A Consistent Offline-Online Dynamic Time Warping-Based Time Series Clustering Approach

Reliable real-time traffic state identification (TSI) provides key support for traffic management and control. Although substantial efforts have been devoted to TSI, considering the high dynamics and stochasticity of traffic flows, there remain challenges in providing reliable and consistent TSI results, especially in online network-level applications. In this study, we propose a time series clustering-based offline-online modeling framework for reliable TSI using high-resolution traffic data. Specifically, in the proposed framework, the offline module extracts representative traffic state patterns from massive historical data, which serve as the state references in the online module when performing real-time TSI with streaming information. Instead of point data, the proposed framework uses high-resolution traffic data in the form of time series, providing rich information on traffic flows and details on their short-term fluctuations and stable long-term trends. In the offline module, considering the fuzziness of traffic states, we introduce a fuzzy c-means based clustering method for offline traffic flow series clustering and traffic state pattern extraction, within which the dynamic time warping algorithm is adopted for measuring the similarity between different time series, and the optimal number of clusters is determined by a proposed critical segment-based method to reach consistent TSI in network-wide applications. In the online module, a dynamic programming-based real-time TSI approach is developed to produce reliable and smooth identification results. Extensive numerical experiments on a 20-mi-long freeway corridor in California, USA, were performed to validate the proposed framework. Results demonstrate the effectiveness of the proposed framework.