1999/05/14 by L. Neubert, Ludger Santen, L. Santen +4 · 2 citations
Computer Science · Engineering · Physics and Astronomy · #Computer science #Data mining #Engineering #Headway #Identification (biology) #Machine learning #Road traffic #Series (stratigraphy) #Simulation #Three-phase traffic theory #Time Series Analysis and Forecasting #Time series #Traffic Prediction and Management Techniques #Traffic congestion #Traffic congestion reconstruction with Kerner's three-phase theory #Traffic control and management #Transport engineering #cond-mat
paper · pdf · doi:10.1103/physreve.60.6480
12 pages, 19 figures, RevTeX
arxiv created 1999/05/14 · openalex publication_date 1999/12/01 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In the present paper, single-vehicle data of highway traffic are analyzed in great detail. By using the single-vehicle data directly, empirical time headway distributions and speed-distance relations can be established. Both quantities yield relevant information about the microscopic states. Several fundamental diagrams are also presented, which are based on time-averaged quantities and compared with earlier empirical investigations. In the remaining part, time-series analyses of the averaged as well as the single-vehicle data are carried out. The results will be used in order to propose objective criteria for an identification of the different traffic states, e.g., synchronized traffic.