2000/01/19 by W. Knospe, L. Santen, Knospe, W. +5 · 1 citation
Computer Science · Decision Sciences · Engineering · Mathematics · Physics and Astronomy · #Algorithm #Anticipation (artificial intelligence) #Artificial intelligence #Automaton #Automotive engineering #Brake #Cellular Automata and Applications #Cellular automaton #Computer science #Computer security #Empirical research #Engineering #FOS: Physical sciences #Focus (optics) #Mathematics #Microscopic traffic flow model #Physics #Real-time computing #Simulation Techniques and Applications #Statistical Mechanics (cond-mat.stat-mech) #Statistics #Theoretical computer science #Traffic control and management #Traffic flow (computer networking) #Traffic generation model #cond-mat.stat-mech
paper · pdf · doi:10.48550/arxiv.cond-mat/0001276
published in arXiv (Cornell University) (Cornell University) · 6 pages, 4 figures, uses Springer Macros 'lncse', to appear in "Traffic and Granular Flow '99: Social, Traffic, and Granular Dynamics" edited by D. Helbing, H. J. Herrmann, M. Schreckenberg, and D. E. Wolf (Springer, Berlin)
arxiv created 2000/01/19 · openalex publication_date 2000/01/19 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Although traffic simulations with cellular-automata models give meaningful results compared with empirical data, highway traffic requires a more detailed description of the elementary dynamics. Based on recent empirical results we present a modified Nagel-Schreckenberg cellular automaton model which incorporates both a slow-to-start and an anticipation rule, which takes into account especially brake lights. The focus in this article lies on the comparison with empirical single-vehicle data.