Asymmetric Cell Transmission Model-Based, Ramp-Connected Robust Traffic Density Estimation under Bounded Disturbances

Vishnoi, Suyash C.; Nugroho, Sebastian A.; Taha, Ahmad F.; Claudel, Christian; Banerjee, Taposh

doi:10.23919/acc45564.2020.9147391

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…We use x i (t) ∈ Z ≥0 , y i (t) ∈ R ≥0 , and z i (t) ∈ R ≥0 to denote the traffic density, traffic inflow, and traffic outflow in lane i at time t, respectively. The traffic dynamics [35], [36] in lane i can be expressed as…”

Section: Macroscopic Traffic Flow Modelmentioning

confidence: 99%

MPC-Based Emergency Vehicle-Centered Multi-Intersection Traffic Control

Hosseinzadeh

Sinopoli

Kolmanovsky

et al. 2023

IEEE Trans. Contr. Syst. Technol.

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This paper proposes a traffic control scheme to alleviate traffic congestion in a network of interconnected signaled lanes/roads. The proposed scheme is emergency vehicle-centered, meaning that it provides an efficient and timely routing for emergency vehicles. In the proposed scheme, model predictive control is utilized to control inlet traffic flows by means of network gates, as well as configuration of traffic lights across the network. Two schemes are considered in this paper: i) centralized; and ii) decentralized. In the centralized scheme, a central unit controls the entire network. This scheme provides the optimal solution, even though it might not fulfil real-time computation requirements for large networks. In the decentralized scheme, each intersection has its own control unit, which sends local information to an aggregator. The main responsibility of this aggregator is to receive local information from all control units across the network as well as the emergency vehicle, to augment the received information, and to share it with the control units. Since the decision-making in decentralized scheme is local and the aggregator should fulfil the above-mentioned tasks during a traffic cycle which takes a long period of time, the decentralized scheme is suitable for large networks, even though it may provide a sub-optimal solution. Extensive simulation studies are carried out to validate the proposed schemes, and assess their performance. Notably, the obtained results reveal that traveling times of emergency vehicles can be reduced up to ∼50% by using the centralized scheme and up to ∼30% by using the decentralized scheme, without causing congestion in other lanes.

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Section: Macroscopic Traffic Flow Modelmentioning

confidence: 99%

MPC-Based Emergency Vehicle-Centered Multi-Intersection Traffic Control

Hosseinzadeh

Sinopoli

Kolmanovsky

et al. 2023

IEEE Trans. Contr. Syst. Technol.

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“…In traffic density estimation problems, it is of interest to detect the mode (congested or uncongested) of the traffic first before deciding on a model to be used for estimation [16]. Motivated by the NYC data behavior, the traffic intensity in this application can also be modeled as statistically periodic.…”

Section: Congestion Mode Detection On Highwaysmentioning

confidence: 99%

Quickest Joint Detection and Classification of Faults in Statistically Periodic Processes

Banerjee

Padhy

Taha

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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An algorithm is proposed to detect and classify a change in the distribution of a stochastic process that has periodic statistical behavior. The problem is posed in the framework of independent and periodically identically distributed (i.p.i.d.) processes, a recently introduced class of processes to model statistically periodic data. It is shown that the proposed algorithm is asymptotically optimal as the rate of false alarms and the probability of misclassification goes to zero. This problem has applications in anomaly detection in traffic data, social network data, ECG data, and neural data, where periodic statistical behavior has been observed. The effectiveness of the algorithm is demonstrated by application to real and simulated data.

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“…A variation to the original ACTM is introduced in the modeling of the ramps which here are treated as normal segments rather than point queues as in the original approach. The approach in this paper is similar to that in [47]. Herein, we define new functions referred to as the demand function δ i [•] and the supply function σ i [•] to simplify the ensuing expressions.…”

Section: Nonlinear Discrete-time Modeling Of Traffic Network With Rampsmentioning

confidence: 99%

“…The purpose of this metric is to provide numerical assurance on the behavior of performance output z against nonzero, time-varying unknown inputs w. Our prior work [53] deals with a robust observer design using the concept of L ∞ stability for traffic density estimation purpose assuming nonlinear continuous-time traffic dynamics model corresponding with the Greenshield's model. Furthermore, our recent work [47] develops a robust L ∞ observer for the discretetime model corresponding with the ACTM. In the sequel, we reproduce a simple numerical procedure from [47] to find an observer gain L that, if successfully solved, renders the estimation error dynamics (23) to be L ∞ stable with performance level µ.…”

Section: A Robust Observer For State Estimationmentioning

confidence: 99%

“…Furthermore, our recent work [47] develops a robust L ∞ observer for the discretetime model corresponding with the ACTM. In the sequel, we reproduce a simple numerical procedure from [47] to find an observer gain L that, if successfully solved, renders the estimation error dynamics (23) to be L ∞ stable with performance level µ. Compared with [47], this paper includes the mathematical proof for the next result.…”

Section: A Robust Observer For State Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Where Should Traffic Sensors Be Placed on Highways?

Nugroho¹,

Vishnoi²,

Taha³

et al. 2021

Preprint

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This paper investigates the practical engineering problem of traffic sensors placement on stretched highways with ramps. Since it is virtually impossible to install bulky traffic sensors on each highway segment, it is crucial to find placements that result in optimized network-wide, traffic observability. Consequently, this results in accurate traffic density estimates on segments where sensors are not installed. The substantial contribution of this paper is the utilization of control-theoretic observability analysis-jointly with integer programming-to determine traffic sensor locations based on the nonlinear dynamics and parameters of traffic networks. In particular, the celebrated asymmetric cell transmission model is used to guide the placement strategy jointly with observability analysis of nonlinear dynamic systems through Gramians. Thorough numerical case studies are presented to corroborate the proposed theoretical methods and various computational research questions are posed and addressed. The presented approach can also be extended to other models of traffic dynamics.

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Asymmetric Cell Transmission Model-Based, Ramp-Connected Robust Traffic Density Estimation under Bounded Disturbances

Cited by 8 publications

References 17 publications

MPC-Based Emergency Vehicle-Centered Multi-Intersection Traffic Control

MPC-Based Emergency Vehicle-Centered Multi-Intersection Traffic Control

Quickest Joint Detection and Classification of Faults in Statistically Periodic Processes

Where Should Traffic Sensors Be Placed on Highways?

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