Adaptive Control Strategies for Urban Network Traffic via a Decentralized Approach With User-Optimal Routing

Chow, Ahf; Sha, Rui; Li, Ying

doi:10.1109/tits.2019.2955425

Cited by 34 publications

(12 citation statements)

References 30 publications

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“…Another proposed approach was to adjust route-choice in a first stage, either off-line or in real-time, and account for it when adjusting signal timing. In [5] drivers are informed about prevailing conditions and undertake the shortest path updated on a node by node basis. A decentralized controller then computes signal plans based on route-choice information captured through turning ratios.…”

Section: Related Workmentioning

confidence: 99%

“…Since the signal times change dynamically, the UE is not known beforehand. Achieving Dynamic User Equilibrium is challenging due to transient nature of traffic, especially when considering a relatively small control horizon [5].…”

Section: B Demandmentioning

confidence: 99%

“…In this scenario, the control strategy considers the individual choice behavior, but cannot act on it. The approach followed here, however, is to tackle the traffic signal control and routing problem simultaneously by benefiting from V2I communication to provide both signal times and routing in multi-destination networks with connected vehicles [4], [5].…”

mentioning

confidence: 99%

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Multi-Commodity Traffic Signal Control and Routing With Connected Vehicles

Souza

Carlson

Müller

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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A real-time traffic management policy that integrates traffic signal control and multi-commodity routing of connected vehicles in networks with multiple destinations is developed. The proposed policy is based on a multi-commodity formulation of the store-and-forward model and assumes all vehicles are able to exchange information with the infrastructure. Vehicles share information about their current location and final destination. Based on this information, the strategy determines both optimized signal timings at every intersection and vehiclespecific routing information at every link of the network. The control actions, i.e., signal times and routing information, are updated at every cycle and delivered by a finite horizon optimal control problem cast into a rolling horizon framework. The underlying optimization problem is convex, and thus the method is suitable for real-time operation in large networks. The method is validated via a micro-simulation study in networks with up to twenty intersections and, in all simulations, outperforms a real-time traffic-responsive signal control strategy that is based on a single-commodity store-and-forward model. The scalable computation effort for increasing network sizes and prediction horizon confirms the computational efficiency of the method. Index Terms-Traffic signal control, multi-commodity routing, connected vehicles, store-and-forward model, rolling horizon. I. INTRODUCTIONW ITH the evolving vehicle automation and communication technology, an increasing share of vehicles is able to exchange information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication architectures. New and enhanced traffic management strategies such as ramp metering, platooning, traffic information and routing, and dynamic pricing for managed lanes may benefit of this

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Section: Related Workmentioning

confidence: 99%

Section: B Demandmentioning

confidence: 99%

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Multi-Commodity Traffic Signal Control and Routing With Connected Vehicles

Souza

Carlson

Müller

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…As the result of the decentralization, these approaches are scalable and can be real-time; however, rather than global optimization, they mostly locally control signals and may find a suboptimal solution [8,[18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…e decentralized approach reaches an optimal level of about 22%. In [19], they present an alternative decentralized solution approach based on the neighbourhood concept, analyzing a real urban network of 58 signal-controlled. e discrepancies between the centralized and decentralized controllers are about 20%-30% with a reduction to 8%-15% once the network traffic router is introduced.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Decentralized VS Centralized Control for the Traffic Signal Synchronization Problem

Adacher

Tiriolo

2020

Journal of Advanced Transportation

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This paper suggests the adoption of a spatial decomposition method to solve the signal synchronization problem. A good signal setting maximizes the number of vehicles passing through intersections, while minimizing gas emissions and possible delays experienced by drivers. The signals synchronization issue can be defined as the problem of finding the offsets, the green timings, and the cycle length for a series of controlled intersections, minimizing the total delay of the network subject to admissibility constraints. In this paper, the authors optimized the signal setting through a new Surrogate Method calculating the objective function via the CTM UT model while performing a simulation. A spatial decomposition approach is here suggested with a simultaneous analysis of different levels of cooperation among subnetworks. This study tries to identify a subnetwork that might be representative of the entire network while taking into consideration two factors: efficiency and efficacy. A comparison between centralized and decentralized control is performed.

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ITS Traffic Management with Connected Vehicles: An Overview

Wu,

Zhu

2023

Communication, Computation and Perception Technologies for Internet of Vehicles

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Adaptive Control Strategies for Urban Network Traffic via a Decentralized Approach With User-Optimal Routing

Cited by 34 publications

References 30 publications

Multi-Commodity Traffic Signal Control and Routing With Connected Vehicles

Multi-Commodity Traffic Signal Control and Routing With Connected Vehicles

Performance Analysis of Decentralized VS Centralized Control for the Traffic Signal Synchronization Problem

ITS Traffic Management with Connected Vehicles: An Overview

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