2010/11/30 by Jan de Gier, Timothy M Garoni, Omar Rojas · 57 citations
Computer Science · Engineering · Physics and Astronomy · Social Sciences · #Floating car data #Network Traffic and Congestion Control #Network traffic simulation #State (computer science) #Traffic bottleneck #Traffic congestion reconstruction with Kerner's three-phase theory #Traffic control and management #Traffic flow (computer networking) #Traffic generation model #Traffic model #Transportation Planning and Optimization #Upstream (networking) #Upstream and downstream (DNA) #cond-mat.stat-mech #msc:82C05 #msc:82C20 #msc:82C70 #msc:90B20 #nlin.AO #nlin.CG #physics.soc-ph
paper · pdf · doi:10.1088/1742-5468/2011/04/p04008
published in Journal of Statistical Mechanics Theory and Experiment 2011(04), P04008 (Institute of Physics) · 41 pages, pdflatex
arxiv created 2011/02/20 · openalex publication_date 2011/04/08 · arxiv updated 2011/04/28 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
We present a model of traffic flow on generic urban road networks based on cellular automata. We apply this model to an existing road network in the Australian city of Melbourne, using empirical data as input. For comparison, we also apply this model to a square-grid network using hypothetical input data. On both networks we compare the effects of non-adaptive versus adaptive traffic lights, in which instantaneous traffic state information feeds back into the traffic signal schedule. We observe that not only do adaptive traffic lights result in better averages of network observables, they also lead to significantly smaller fluctuations in these observables. We furthermore compare two different systems of adaptive traffic signals, one which is informed by the traffic state on both upstream and downstream links and one which is informed by upstream links only. We find that, in general, both the mean and the fluctuation of the travel time are smallest when using the joint upstream–downstream control strategy.