Group-based optimization of a time-dependent TRANSYT traffic model for area traffic control

Wong, Stephen C. P.; Wong, Wai-Tak; Leung, Cho-Kin; Tong, CO

doi:10.1016/s0191-2615(01)00004-2

Cited by 83 publications

(31 citation statements)

References 11 publications

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“…In addition, some researchers were interested in forecasting the urban traffic using a wide range of methods, such as ARIMA [12], generalized linear models, and neural networks [13]. Interestingly, Smith et al [14] in a resemblance of historical norm, nonparametric regression, time series, and neural network models discover that the nonparametric regression model meaningfully exceed in performance comparing to the other models [15].…”

Section: Literature Reviewmentioning

confidence: 99%

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Modeling and Simulation of Urban Traffic Signals

Salimifard¹,

Ansari²

2013

IJMO

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Abstract-Traffic signals play a vital role in effective traffic management. In this paper we apply Arena simulation tool box for modeling and simulation of urban traffic signal system. It has been shown that Arena could be used in simulation of traffic signals system, despite the fact that Arena is not designed for modeling urban traffic signal control. We presented a non-trivial case study to illustrate how Arena is capable of modeling traffic systems.

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Section: Literature Reviewmentioning

confidence: 99%

“…3, No. 2, April 2013 During the past decades, researchers have made immense attempt to develop signal matched control strategies for traffic network [12]. Baras et al [16] used random-dot process theory to set up a mathematical model of an urban traffic control system.…”

Section: Khodakaram Salimifard and Mehdi Ansari Modeling And Simulatimentioning

confidence: 99%

Modeling and Simulation of Urban Traffic Signals

Salimifard¹,

Ansari²

2013

IJMO

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“…The bus dynamics is controlled by varying the loading parameter, the cycle time of signal, and the degree of speedup. The typical urban traffic signal control systems are TRAN-SYT (Wong et al 2002), SCOOT (Robertson, Bretherton 1991) and SCATS (Sims, Dobinson 1980). There are several difficulties and challenges in the previous traffic signal optimization research Kerner 2002;McKenney, White 2013):…”

Section: Introductionmentioning

confidence: 99%

A Multi-Intersection Model and Signal Timing Plan Algorithm for Urban Traffic Signal Control

Wang

et al. 2014

Transport

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Abstract. The urban traffic signal control system is complex, non-linear and non-equilibrium in real conditions. The existing methods could not satisfy the requirement of real-time and dynamic control. In order to solve these difficulties and challenges, this paper proposes a novel Multi-Intersection Model (MIM) based on Cellular Automata (CA) and a Multi-Intersection Signal Timing Plan Algorithm (MISTPA), which can reduce the delay time at each intersection and effectively alleviate the traffic pressure on each intersection in the urban traffic network. Our work is divided into several parts: (1) a multi-intersection model based on CA is defined to build the dynamic urban traffic network; (2) MISTPA is proposed, which truly reflects the real-time demand degree to green time of the traffic flow at each intersection. The MISTPA is composed Single Intersection Volume Algorithm (SIVA), Single-Lane Volume Algorithm (SLVA) and single intersection signal timing plan algorithm (SISTPA). Extensive experiments show that when the saturation is greater than 0.3, the MIM and the MISTPA achieve good performance, and can significantly reduce the vehicle delay time at each intersection. The average delay time of the traffic flow at each intersection can obviously be reduced. Finally, a practical case study demonstrates that the proposed model and the corresponding algorithm are correct and effective.

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“…In contrast, the group-based approach (GA) directly assigns green times to traffic movements without maintaining a specific stage structure [1]. Many theoretical analyses [2][3][4] have indicated that GA is more efficient than SA in terms of its operational performance, as indicated largely by the factor of delay, owing to its high flexibility in signal phasing and the allocation of green times to traffic movements.…”

Section: Introductionmentioning

confidence: 99%

Investigating Operational Benefits of Group-based Signal Control in Japan Through a Stochastic Approach

Tang

Nakamura

2011

Int. J. ITS Res.

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This study intends to investigate operational performance of the group-based signal control approach (GA) in Japan. A cycle-based stochastic approach incorporating the HCM 2000 delay model and Monte Carlo simulation was proposed. To facilitate its application, extensive field data was collected at 11 typical intersections in Germany and Japan to understand the variability of input variables and their correlations. A series of case studies were eventually conducted to investigate the applicability of GA under various conditions, i.e., degree of saturation (DS), split ratio (SR), and the coefficient of variation (COV) of traffic demand. Conclusions supported that the operational performance of GA deteriorated with increasing SR and decreasing DS. GA is apparently advantageous for SRs less than 0.8, COVs less than 0.2, and optimum cycle lengths greater than 90 s.

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Group-based optimization of a time-dependent TRANSYT traffic model for area traffic control

Cited by 83 publications

References 11 publications

Modeling and Simulation of Urban Traffic Signals

Modeling and Simulation of Urban Traffic Signals

A Multi-Intersection Model and Signal Timing Plan Algorithm for Urban Traffic Signal Control

Investigating Operational Benefits of Group-based Signal Control in Japan Through a Stochastic Approach

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