Economic Model Predictive Control of Large-Scale Urban Road Networks via Perimeter Control and Regional Route Guidance

Sirmatel, Isik Ilber; Geroliminis, Nikolas

doi:10.1109/tits.2017.2716541

Cited by 163 publications

(66 citation statements)

References 49 publications

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“…This section builds on [17], [18] model formulation and is used as a reference and for the development of notation. Consider an urban network partitioned in N homogeneous regions with well-defined MFDs.…”

Section: Aggregated Dynamics For a Partitioned Citymentioning

confidence: 99%

“…This problem can be solved in reasonable time by use of advanced nonlinear optimization toolboxes (e.g. ipopt 1 , see also [17]). Note that in [18] we have utilized this approach as a benchmark and compared the results with the LPV approach in a macroscopic simulation environment; here we apply the LPV approach to microsimulation which constitutes a very different plant and requires a significant effort for an implementation of online estimation and control.…”

Section: A Nonlinear Model Predictive Control (Nmpc)mentioning

confidence: 99%

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Linear-parameter-varying approximation of nonlinear dynamics for model predictive flow control of urban multi-region systems

Kouvelas

Saeedmanesh

Geroliminis

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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An alternative approach for real-time networkwide traffic control in cities that has recently gained a lot of interest is perimeter flow control. The focus of the current work is to study two aspects that are not covered in the perimeter control literature, which are: (a) integration of appropriate external demand information that has been considered system disturbance in the derivation of feedback control laws in previous works, and (b) mathematical formulation of the original nonlinear problem in a linear-parameter-varying (LPV) form, so that optimal control can be applied in a (rolling horizon) model predictive concept. This work presents the mathematical analysis of the optimal control problem, as well as the approximations and simplifications that are assumed in order to derive the formulation of a linear optimization problem. The developed scheme is applied to microsimulation in order to better investigate its applicability to real life conditions. The simulation experiments demonstrate the effectiveness of the scheme compared to fixed-time control as all the performance indicators are improved significantly.

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Section: Aggregated Dynamics For a Partitioned Citymentioning

confidence: 99%

Section: A Nonlinear Model Predictive Control (Nmpc)mentioning

confidence: 99%

Linear-parameter-varying approximation of nonlinear dynamics for model predictive flow control of urban multi-region systems

Kouvelas

Saeedmanesh

Geroliminis

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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“…Various researchers have tried to achieve this objective [29][30][31][32][33][34][35][36]. Li et al [37] investigated a perimeter control strategy for an oversaturated network.…”

Section: Introductionmentioning

confidence: 99%

“…They optimized the green duration allocation in order to maximize the throughput using a genetic algorithm to minimize queues and delays by optimizing phase sequences and offsets. However, their method used fixed signal timings, which does not reflect typical real-time traffic conditions [29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Network Perimeter Control

Bichiou

Elouni

Abdelghaffar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Urban traffic congestion is a chronic problem faced by many cities in the US and worldwide. It results in inefficient infrastructure use as well as increased vehicle fuel consumption and emission levels. Congestion is intertwined with delay, as road users waste precious hours on the road, which in turn reduces productivity. Researchers have developed, and continue to develop, tools and systems to alleviate this problem. Network perimeter control is one such tool that has been studied extensively. It attempts to control the flow of vehicles entering a protected area to ensure that the congested regime predetermined by the Network Fundamental Diagram (NFD) is not reached. In this paper, an approach derived from sliding mode control theory is presented. Its main advantages over proportional-integral controllers include (1) minimal tuning, (2) no linearization of the governing equations, (3) no assumptions with regard to the shape of the NFD, and (4) ability to handle various demand profiles without the need to retune the controller. A sliding mode controller was implemented and tested on a congested grid network. The results show that the proposed controller produces network-wide delay savings and disperses congestion effectively.Index Terms-Network fundamental diagram, network perimeter control, sliding mode control, traffic signal control

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“…Hence, the main contribution of this work is the formulation of the joint route guidance and demand management problem and its solution using an approximate MPC scheme. The consideration of demand management in the formulation is shown to potentially eliminate the formation of cycles 1 that may result from other state-ofthe-art route guidance solutions [6]. The reason is that cycles occur when there is a need to delay entrance within a region due to heavy congestion, something that can be avoided by restraining the vehicles from entering the network in the first place.…”

Section: Introductionmentioning

confidence: 99%

Joint route guidance and demand management for multi-region traffic networks

Menelaou

Timotheou

Kolios

et al. 2019

2019 18th European Control Conference (ECC)

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Traffic congestion occurs as demand surpasses the available capacity of a road network, resulting to lower speeds and longer journey times. To effectively alleviate the problem, the number of vehicles should be maintained below the network's critical density; with route guidance constituting the primary control strategy to achieve this. However, the effectiveness of route guidance is limited in highdemand conditions. In this work, we investigate a Model Predictive Control (MPC) framework that combines multiregional route guidance with a novel demand management method. Route guidance is used to minimize the network's density imbalance while demand management is utilized to reduce the conditions that cause congestion. This can be achieved by manipulating vehicle routes (i.e., using route guidance) and/or by instructing a portion of the vehicles to wait at their origin before commencing their journey (demand management). Simulations are conducted to evaluate the performance of the proposed MPC optimization indicating the substantial improvements that can be achieved in traffic flow performance.

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Economic Model Predictive Control of Large-Scale Urban Road Networks via Perimeter Control and Regional Route Guidance

Cited by 163 publications

References 49 publications

Linear-parameter-varying approximation of nonlinear dynamics for model predictive flow control of urban multi-region systems

Linear-parameter-varying approximation of nonlinear dynamics for model predictive flow control of urban multi-region systems

Sliding Mode Network Perimeter Control

Joint route guidance and demand management for multi-region traffic networks

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