Motorway traffic flow modelling, estimation and control with vehicle automation and communication systems

Papamichail, Ioannis; Bekiaris-Liberis, Nikolaos; Delis, A. I.; Manolis, Diamantis; Mountakis, Kiriakos-Simon; Nikolos, Ioannis K.; Roncoli, Claudio; Papageorgiou, Markos

doi:10.1016/j.arcontrol.2019.09.002

Cited by 38 publications

(11 citation statements)

References 76 publications

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“…Van Hinsberge et al [37] use Bayesian analysis to update prior probabilities of the parameters into posterior probabilities; Hoogendoorn & Hoogendoorn [12] use a generalized maximum likelihood estimation approach. Other methods for microscopic model calibration in the presence of automatic and connected vehicles are summarized in a recent study by Papamichail et al [27].…”

Section: Related Workmentioning

confidence: 99%

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Wang

Gunter

Nice

et al. 2021

IEEE Trans. Intell. Veh.

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Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF).We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulationbased optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real-time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.

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Section: Related Workmentioning

confidence: 99%

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Wang

Gunter

Nice

et al. 2021

IEEE Trans. Intell. Veh.

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“…Several modeling approaches have been proposed to model the interaction between the normal flow and automated vehicles. While some research works move in the direction of microscopic models [7], macroscopic models are often preferred for control applications due to their lower computational complexity. Some extensions to traditional macroscopic models have been studied in order to model the presence of intelligent vehicles moving in the neighbouring human-driven traffic flow.…”

Section: Introductionmentioning

confidence: 99%

Traffic Control Via Platoons of Intelligent Vehicles for Saving Fuel Consumption in Freeway Systems

Piacentini

Goatin

Ferrara

2021

IEEE Control Syst. Lett.

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In this paper a coupled PDE-ODE model describing the interaction between the bulk traffic flow and a platoon of connected vehicles is adopted to develop a control action aiming at reducing the fuel consumption of the overall traffic flow. The platoon is modeled as a capacity restriction acting on the surrounding traffic. The trajectory of the initial and final points of the platoon are optimized by means of a model predictive control strategy, acting on the speeds of the front-end and backend of the platoon, thus resulting in controlling both the speed and the length of the platoon. The approach is assessed in simulations.

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“…Results validated via MATLAB and VISSIM simulation indicated that increased rates of CAV penetration produced significant improvement in energy efficiency, meanwhile, with the increasing traffic, the significance is reduced. I. Papamichail et al (Papamichail, et al, 2019) identified the novel problems in traffic management and presented a summarized discussion on problems in relation with management systems for motorway traffic. Their work aimed to improve the vehicle automation and communication systems even in case of dynamic traffic environment.…”

Section: Prominent Ai Issuesmentioning

confidence: 99%

Information Management Challenges in Autonomous Vehicles

Ghansiyal¹,

Mittal²,

Kar

2021

Journal of Cases on Information Technology

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The focus of automobile industry is towards producing efficient driverless cars that are risk free and with zero tolerance to safety violations thereby following in the footsteps of autonomous robots. In this study, the author elaborates on the vulnerabilities relevant to internet of things technology implementation in these connected cars, commonly termed as internet of vehicles. This topic has already been discussed frequently by the research community; however, the main contribution of the paper is to establish the connection of information management with autonomous systems, an aspect that other literatures lack. The focus of the study is on presenting a brief introduction to the foundation technologies used in the connected vehicles. It also aims to summarize the various security methods that have been used infrequently and could be further explored in future research.

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Motorway traffic flow modelling, estimation and control with vehicle automation and communication systems

Cited by 38 publications

References 76 publications

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Traffic Control Via Platoons of Intelligent Vehicles for Saving Fuel Consumption in Freeway Systems

Information Management Challenges in Autonomous Vehicles

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