Decentralized store-and-forward based strategies for the signal control problem in large-scale congested urban road networks

Pedroso, Leonardo; Batista, Pedro

doi:10.1016/j.trc.2021.103412

Cited by 15 publications

(6 citation statements)

References 25 publications

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“…A key characteristic of SFM is its ability to model trafc fows using a simplifed mathematical description, eliminating the need for discrete variables during optimization. Tis model has paved the way for optimization problems with polynomial complexity and has found practical applications in realistic networks [15,16]. In contrast to SFM, CTM divides links into smaller segments, enabling a more precise representation of non-uniform trafc states within every segment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Te minimum green and maximum green constraints are applied to G i,Kj (n) as follows: Vehicle demand is the sole consideration in trafc demand, and pedestrian crossing demand is not taken into account [6,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] 3 Trafc signal controllers can only accommodate two or four phases and do not support complex phase structures (e.g., phase overlap) [28][29][30][31][32] 4 Te ofset of every intersection is set to 0, and the ofset transition is not taken into account [20,21,23,28,31] 5…”

Section: Green Time Constraintsmentioning

confidence: 99%

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Modified Model Predictive Control for Coordinated Signals along an Arterial under Relaxing Assumptions

Zhang,

Xu,

Pan

et al. 2024

Journal of Advanced Transportation

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This paper proposes modified model predictive control (MMPC) for coordinated signals, aiming to enhance a model’s fidelity to the realistic traffic environment by relaxing typical assumptions. We focus on the arterial, where every intersection is equipped with a dual-ring-barrier signal controller that complies with the standards of the National Electric Manufacturers Association. MMPC employs the store-and-forward model to describe traffic flow, thereby transforming the signal control problem into a model-based rolling-horizon optimization problem, in which the prediction horizon is composed of several future sample intervals, commonly equal to the cycle length. A radar detector is used to collect vehicle data upstream of the stop line at every sampling instant. The optimization problem is solved to minimize the number of vehicles within the prediction horizon, and the next timing plan is determined based on the optimization results. Constraints are added and modified in order to incorporate the typical relaxed assumptions in the optimization process. For this purpose, MMPC introduces a transition-free ring-barrier structure, vehicle distribution ratio, and percent arrival before the end of green. Simulation results indicate that coordination can be maintained by MMPC without the need for transitions, and the estimation of current and future traffic states can be improved with the assistance of modified constraints. Compared with benchmark techniques, MMPC offers superior vehicle progression for coordinated movement and significant improvements in delays, number of stops, and total travel time from a system-wide perspective, with an acceptable small increase in runtime.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Green Time Constraintsmentioning

confidence: 99%

Modified Model Predictive Control for Coordinated Signals along an Arterial under Relaxing Assumptions

Zhang,

Xu,

Pan

et al. 2024

Journal of Advanced Transportation

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“…The brief presentation of the store-and-forward model follows Diakaki (1999); Aboudolas et al (2009); and Pedroso and Batista (2021) closely. The topology of the urban network is represented by a directed graph whose nodes correspond to the junctions and whose edges are the directional road links.…”

Section: Store-and-forward Modelmentioning

confidence: 99%

“…The definition of a feasible traffic network and a minimum complete stage strategy can be found in Pedroso and Batista (2021), which are very mild conditions that physically meaningful urban traffic networks and stage structures abide by. Most importantly, from Lemma 1, unless S = Z, which is rarely the case, the store-and-forward LTI system (24) is not controllable.…”

Section: Feedback-feedforward Signal Controlmentioning

confidence: 99%

“…Most importantly, from Lemma 1, unless S = Z, which is rarely the case, the store-and-forward LTI system (24) is not controllable. To circumvent this issue the technique proposed in Pedroso and Batista (2021) is employed. Denote the controllability matrix of system (24) by C. Leveraging the Canonical Structure Theorem (Rugh, 1996, Chap.…”

Section: Feedback-feedforward Signal Controlmentioning

confidence: 99%

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SAFFRON: Store-And-Forward model toolbox For urban ROad Network signal control in MATLAB

Pedroso

Batista

Papageorgiou

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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To cope with varying and highly uncertain traffic patterns, a novel network-wide traffic signal control strategy based on the store-and-forward model of a traffic network is proposed. On one hand, making use of a single loop detector in each road link, we develop an estimation solution for both the link occupancy and the net exogenous demand in every road link of a network. On the other hand, borrowing from optimal control theory, we design an optimal linear quadratic control scheme, consisting of a linear feedback term, of the occupancy of the road links, and a feedforward component, which accounts for the varying exogenous vehicle load on the network. Thereby, the resulting control scheme is a simple feedback-feedforward controller, which is fed with occupancy and exogenous demand estimates, and is suitable for real-time implementation. Numerical simulations of the urban traffic network of Chania, Greece, show that, for realistic surges in the exogenous demand, the proposed solution significantly outperforms tried-and-tested solutions that ignore the exogenous demand.

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Extensions to traffic control modeling store-and-forward

Stoilova

Stoilov

2023

Expert Systems with Applications

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Decentralized store-and-forward based strategies for the signal control problem in large-scale congested urban road networks

Cited by 15 publications

References 25 publications

Modified Model Predictive Control for Coordinated Signals along an Arterial under Relaxing Assumptions

Modified Model Predictive Control for Coordinated Signals along an Arterial under Relaxing Assumptions

SAFFRON: Store-And-Forward model toolbox For urban ROad Network signal control in MATLAB

Extensions to traffic control modeling store-and-forward

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