Predictive Control for Urban Traffic Networks: Initial Evaluation

Oliveira, Lucas B.; Camponogara, Eduardo

doi:10.3182/20071017-3-br-2923.00067

Cited by 14 publications

(3 citation statements)

References 4 publications

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“…As part of urban traffic simulation studies, urban traffic modeling is a common analytical approach to assess and optimize vehiclebased studies (Amadio et al, 2018), urban traffic flow (Ledoux, 1997), urban traffic control system (Boillot et al, 2006) or prediction (De Oliveira and Camponogara, 2007), mode choice behavior (Garcia-Aunon et al, 2019), urban traffic networks (Li et al, 2016), etc. Some studies also utilize combined modeling analyses, such as the development of a nexus between traffic flow and urban networks (Gartner and Stamatiadis, 2002), or mathematical methods (Wang et al, 2006), multi-agent methods (Ou et al, 2000), multi-objective methods (Tang and Wang, 2007), prediction methods (Nigarnjanagool and Dia, 2005), network-based studies (Schadschneider et al, 2005), integrated methods (Li and Zhao, 2008), etc.…”

Section: State Of the Artmentioning

confidence: 99%

Urban Traffic Optimization Based on Modeling Analysis of Sector-Based Time Variable: The Case of Simulated Ningbo, China

Cheshmehzangi¹,

Ardakani²

2021

Front. Sustain. Cities

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This paper studies the important factor of sector-based time variable, which is critical to urban mobility patterns in an urban environment. In particular, this study analyzes urban traffic optimization based on modeling analysis of a simulated urban environment. In doing so, we develop and assess the urban traffic model based on three key components of agents, urban map, and mobility pattern. We develop an urban sample based on a medium-to-large city in China, which is represented by Manhattan grid pattern layout. By developing a homogeneous urban layout, we distribute the urban blocks of various sectors across the sample model. Through simulation studies, we model urban traffic based on “conventional operation hours” and “proposed operation hours” of all sectors. This urban traffic model is used to study the impact of the proposed approach on urban traffic based on two measured metrics of end-to-end delay (ETE) and Agent queue count (AQC). By suggesting a new sector-based time variable, we then evaluate the urban traffic model based on multiple active agent ratio. The findings from this simulation uncover the importance of sector-based time variable in optimizing urban traffic.

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Section: State Of the Artmentioning

confidence: 99%

Urban Traffic Optimization Based on Modeling Analysis of Sector-Based Time Variable: The Case of Simulated Ningbo, China

Cheshmehzangi¹,

Ardakani²

2021

Front. Sustain. Cities

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“…With the aim of contributing to the evolving technology of DMPC, this work presents computational and simulated analysis of the application of a DMPC framework to a representative urban traffic control (UTC) problem. The MPC formulation in is cast in this framework so that the advantages of scalability and tolerance to failure are brought to the solution. The UTC scenario is a simulated model of the downtown region of the city of Macaé , Brazil, considering a detailed description of the roadways, traffic flows, and other modeling parameters that closely reproduce actual traffic patterns.…”

Section: Introductionmentioning

confidence: 99%

Distributed MPC for urban traffic networks: A simulation‐based performance analysis

Souza

Camponogara

Kraus

et al. 2014

Optim Control Appl Methods

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The operation of urban traffic networks with distributed model predictive control (DMPC) can be more flexible than centralized strategies because DMPC allows for graceful expansion of the control infrastructure, localized reconfiguration, and tolerance to faulty operation. Yet, computational performance is less efficient than with centralized model predictive control (MPC) because of the added complexity brought about by distribution schemes. To assess the trade-off between flexibility and performance, in this paper we assess the tolerance to failure and performance of DMPC in contrast with centralized control. The problem of concern is the signal setting of green time in a representative traffic network modeled in a commercial microscopic traffic simulator. A software tool is developed for implementing and simulating the DMPC framework in tandem with the simulator. Comparisons of DMPC with MPC and a baseline feedback control strategy that does not use constrained optimization show that DMPC can achieve performance gains with respect to the baseline case and enhance tolerance to failure. Computations for DMPC are less efficient than with centralized MPC; nevertheless, the time taken by DMPC is well below the required for field use. Although the true distributed deployment of DMPC requires special hardware, its implementation in a central cluster can be made without loss of operational flexibility. DISTRIBUTED MPC FOR URBAN TRAFFIC NETWORKS Figure 4. Linear network of the traffic region of Macaé under real-time control.By further eliminating the linear dependencies from this compact formulation, we arrive at a convenient reformulation of the original problem:This local view of the agents is convenient for formulating the traffic control problem according to the store-and-forward model, whereby traffic junctions match subsystems; DISTRIBUTED MPC FOR URBAN TRAFFIC NETWORKS i to P i ; b w i ; § A real-valued function within the interior set that tends to infinity as the solution approaches the boundary of any constraint.

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“…Combining it with control theory, the method called TUC (Vaya Dinopoulou et al, 2005) is formulated as a quadratic programming form, and the controller is designed in the classic LQR control law. In later works (Aboudolas et al, 2007;de Oliveira and Camponogara, 2007;K. Aboudolas et al, 2009;Lucas Barcelos de Oliveira et al, 2010;Zhou et al, 2012;Ye et al, 2013), the MPC approach was proposed, and demonstrated that the significant improvements may be induced by replacing the standard LQR control law with the finite moving horizon method such as model predictive control.…”

Section: Introductionmentioning

confidence: 99%

A Novel MPC with Chance Constraints for Signal Splits Control in Urban Traffic Network

Zhou¹,

et al. 2014

IFAC Proceedings Volumes

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Predictive Control for Urban Traffic Networks: Initial Evaluation

Cited by 14 publications

References 4 publications

Urban Traffic Optimization Based on Modeling Analysis of Sector-Based Time Variable: The Case of Simulated Ningbo, China

Urban Traffic Optimization Based on Modeling Analysis of Sector-Based Time Variable: The Case of Simulated Ningbo, China

Distributed MPC for urban traffic networks: A simulation‐based performance analysis

A Novel MPC with Chance Constraints for Signal Splits Control in Urban Traffic Network

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