Behaviour of traffic on a link with traffic light boundaries

被引:1
|
作者
Zhang, Lele [1 ,4 ]
Finn, Caley [1 ,3 ]
Garoni, Timothy M. [2 ,4 ]
de Gier, Jan [1 ,4 ]
机构
[1] Univ Melbourne, Sch Math & Stat, Melbourne, Vic 3010, Australia
[2] Monash Univ, Sch Math Sci, Clayton, Vic 3800, Australia
[3] Univ Savoie Mt Blanc, LAPTh, CNRS, 9 Chemin Bellevue,BP 110, F-74941 Annecy Le Vieux, France
[4] ARC Ctr Excellence Math & Stat Frontiers ACEMS, Melbourne, Vic, Australia
关键词
Time-dependent boundary; Hydrodynamics; Domain wall theory; Cellular automata; MACROSCOPIC FUNDAMENTAL DIAGRAM; MODEL; DYNAMICS; WAVES; FLOW;
D O I
10.1016/j.physa.2018.02.201
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
This paper considers a single link with traffic light boundary conditions at both ends, and investigates the traffic evolution over time with various signal and system configurations. A hydrodynamic model and a modified stochastic domain wall theory are proposed to describe the local density variation. The Nagel-Schreckenberg model (NaSch), an agent based stochastic model, is used as a benchmark. The hydrodynamic model provides good approximations over short time scales. The domain wall model is found to reproduce the time evolution of local densities, in good agreement with the NaSch simulations for both short and long time scales. A systematic investigation of the impact of network parameters, including system sizes, cycle lengths, phase splits and signal offsets, on traffic flows suggests that the stationary flow is dominated by the boundary with the smaller split. Nevertheless, the signal offset plays an important role in determining the flow. Analytical expressions of the flow in relation to those parameters are obtained for the deterministic domain wall model and match the deterministic NaSch simulations. The analytic results agree qualitatively with the general stochastic models. When the cycle is sufficiently short, the stationary state is governed by effective inflow and outflow rates, and the density profile is approximately linear and independent of time. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:116 / 138
页数:23
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