Using principal curves to analyse traffic patterns on freeways

Einbeck, Jochen; Dwyer, Jo

doi:10.1080/18128600903500110

Cited by 15 publications

(5 citation statements)

References 16 publications

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“…Equations (8,9) are now in the standard form of the CTM flow propagation. By this, a uniform representation is devised for the MCTM wherein the flow propagation law is characterized by the sending and receiving functions.…”

Section: The Cell Transmission Model and Its Modificationsmentioning

confidence: 99%

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Automatic calibration of fundamental diagram for first‐order macroscopic freeway traffic models

Zhong

Chen

Chow

et al. 2015

J of Advced Transportation

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Despite its importance in macroscopic traffic flow modeling, comprehensive method for the calibration of fundamental diagram is very limited. Conventional empirical methods adopt a steady state analysis of the aggregate traffic data collected from measurement devices installed on a particular site without considering the traffic dynamics, which renders the simulation may not be adaptive to the variability of data. Nonetheless, determining the fundamental diagram for each detection site is often infeasible. To remedy these, this study presents an automatic calibration method to estimate the parameters of a fundamental diagram through a dynamic approach. Simulated flow from the cell transmission model is compared against the measured flow wherein an optimization merit is conducted to minimize the discrepancy between model-generated data and real data. The empirical results prove that the proposed automatic calibration algorithm can significantly improve the accuracy of traffic state estimation by adapting to the variability of traffic data when compared with several existing methods under both recurrent and abnormal traffic conditions. Results also highlight the robustness of the proposed algorithm. The automatic calibration algorithm provides a powerful tool for model calibration when freeways are equipped with sparse detectors, new traffic surveillance systems lack of comprehensive traffic data, or the case that lots of detectors lose their effectiveness for aging systems. Furthermore, the proposed method is useful for off-line model calibration under abnormal traffic conditions, for example, incident scenarios.The boundary conditions are not in the exact form as those in [10,23], but an equivalent formulation derived to convert the MCTM into a uniform characteristics of the sending and receiving functions as will be shown later. This formulation also makes the MCTM more suitable as a network loading model for the automatic calibration method. AUTOMATIC CALIBRATION OF FUNDAMENTAL DIAGRAM

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Section: The Cell Transmission Model and Its Modificationsmentioning

confidence: 99%

“…Typically, models are calibrated to replicate a single chosen day of traffic flow. Despite its importance, empirical calibration and validation of FDs, in particular for dynamic traffic modeling, is very limited [9].…”

Section: Introductionmentioning

confidence: 99%

Automatic calibration of fundamental diagram for first‐order macroscopic freeway traffic models

Zhong

Chen

Chow

et al. 2015

J of Advced Transportation

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“…However, curved relationships are commonplace in real data. We provide two motivating examples; one is a speed flow data set on a California freeway (Einbeck & Dwyer, 2011) and the other is an ecological data set of horse mussels in New Zealand (Camden, 1989). Figure 2a shows that vehicle flow, per 5 minutes, and speed, in miles per hour, on the freeway have a curved relationship.…”

Section: Introductionmentioning

confidence: 99%

Curved factor analysis with the Ellipsoid-Gaussian distribution

Song¹,

Dunson²

2022

Preprint

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There is a need for new models for characterizing dependence in multivariate data. The multivariate Gaussian distribution is routinely used, but cannot characterize nonlinear relationships in the data. Most non-linear extensions tend to be highly complex; for example, involving estimation of a non-linear regression model in latent variables. In this article, we propose a relatively simple class of Ellipsoid-Gaussian multivariate distributions, which are derived by using a Gaussian linear factor model involving latent variables having a von Mises-Fisher distribution on a unit hyper-sphere. We show that the Ellipsoid-Gaussian distribution can flexibly model curved relationships among variables with lower-dimensional structures. Taking a Bayesian approach, we propose a hybrid of gradient-based geodesic Monte Carlo and adaptive Metropolis for posterior sampling. We derive basic properties and illustrate the utility of the Ellipsoid-Gaussian distribution on a variety of simulated and real data applications.

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“…Martínez and Ludeña [3] and Stanford and Raftery [4] tried to detect curvilinear structures in noisy images. Einbeck and Dwyer [5] employed curvilinear structure identification to analyze the freeway traffic patterns. Dong and Mcavoy [6] applied curvilinear structure for processing monitoring.…”

Section: Introductionmentioning

confidence: 99%

Ridge–based curvilinear structure detection for identifying road in remote sensing image and backbone in neuron dendrite image

Kong

Govindaraj

Zhang

2018

Multimed Tools Appl

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The curvilinear structure detection is widely applied in many real tasks, such as the fiber classification, river finding, blood vessel detection, and so on. In this paper, we proposed to use the ridgebased curvilinear structure detection (RCSD) for the road extraction from the remote sensing images. First, we employed the morphology trivial opening operation to filter out almost all the small clusters of noise and the small paths. Then RCSD was used to find the road from the remote sensing images. The experiments showed that our proposed method is efficient and give better results than the current existing road-detection methods. Considering the similar structure between backbone in the neuron dendrite images and the road in remote sensing images, we extended the application of RCSD to the backbone detection in neuron dendrite images. The results on backbone detection also proved the efficiency of RCSD.

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Using principal curves to analyse traffic patterns on freeways

Cited by 15 publications

References 16 publications

Automatic calibration of fundamental diagram for first‐order macroscopic freeway traffic models

Automatic calibration of fundamental diagram for first‐order macroscopic freeway traffic models

Curved factor analysis with the Ellipsoid-Gaussian distribution

Ridge–based curvilinear structure detection for identifying road in remote sensing image and backbone in neuron dendrite image

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