Macroscopic freeway model calibration with partially observed data, a case study

Dervisoglu, Gunes; Kurzhanskiy, Alexander A.; Gomes, Guilherme Ferreira; Horowitz, Roberto

doi:10.1109/acc.2014.6859026

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…One important limitation of such datasets is that they are initially introduced to collect aggregated data relevant for calibrating and validating macroscopic descriptions of the traffic flow [9,10,11,12]. Microscopic descriptions of traffic [13,14,15,16,17] can be challenging to validate in detail without data at the level of the individual vehicle.…”

Section: Motivation: the Need For Empirical Traffic Datamentioning

confidence: 99%

Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data

Stern

Cui

et al. 2019

Transportation Research Part C: Emerging Technologies

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High-fidelity vehicle trajectory data is becoming increasingly important in traffic modeling, especially to capture dynamic features such as stop-and-go waves. This article presents data collected in a series of eight experiments on a circular track with human drivers. The data contains smooth flowing and stop-and-go traffic conditions. The vehicle trajectories presented in this article are collected using a panoramic 360degree camera, and fuel rate data is recorded via an on-board diagnostics scanner installed in each vehicle. The video data from the 360-degree camera is processed with an offline unsupervised algorithm to extract vehicle trajectories from experimental data. The trajectories are highly accurate, with a mean positional bias of less than 0.01 m and a standard deviation of 0.11 m. The velocities are also validated to be highly accurate with a bias of 0.02 m/s and standard deviation of 0.09 m/s. The source code and data used in this article are published with this work.

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Section: Motivation: the Need For Empirical Traffic Datamentioning

confidence: 99%

Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data

Stern

Cui

et al. 2019

Transportation Research Part C: Emerging Technologies

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“…The attacks for the first example, box objective (to be described), use a macroscopic freeway model of a 19.4 mile stretch of the I15 South Freeway in San Diego California. The model was split into 125 links with 9 onramps and was calibrated [58,59] using loop-detector measurements available through the PeMS loop-detector system [1]. Figure 3(a) is a Space-time diagram of the I15 freeway.…”

Section: First Attack: Congestion-on-demandmentioning

confidence: 99%

Creating complex congestion patterns via multi-objective optimal freeway traffic control with application to cyber-security

Reilly

Martin

Payer

et al. 2016

Transportation Research Part B: Methodological

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This article presents a study on freeway networks instrumented with coordinated ramp metering and the ability of such control systems to produce arbitrarily complex congestion patterns within the dynamical limits of the traffic system. The developed method is used to evaluate the potential for an adversary with access to control infrastructure to enact high-level attacks on the underlying freeway system. The attacks are executed using a predictive, coordinated ramp metering controller based on finite-horizon optimal control and multi-objective optimization techniques. The efficacy of the control schemes in carrying out the prescribed attacks is determined via simulations of traffic network models based on the cell transmission model with onramps modeled as queue buffers. Freeway attacks with highlevel objectives are presented on two illustrative examples: congestion-on-demand, which aims to create precise, user-specified pockets of congestion, and catch-me-if-you-can, which attempts to aid a fleeing vehicle from pursuant vehicles.

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Macroscopic freeway model calibration with partially observed data, a case study

Cited by 2 publications

References 10 publications

Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data

Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data

Creating complex congestion patterns via multi-objective optimal freeway traffic control with application to cyber-security

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