Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts

Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

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“…Compared to the framework by Shao et al [67,68], we argue that the IGLS-based formulation is statistical interpretable and more computationally efficient to solve on large-scale networks. As shown in Del Pino [23], each iteration in the IGLS resembles one gradient descent step in the Newton-Raphson algorithm.…”

Section: A Novel Iterative Formulation For Estimating Probabilistic Omentioning

confidence: 73%

“…In principle, the day-to-day variation is attributed to three sources, O-D demand variation, route choice variation and sensing measurement error. The impact of day-to-day O-D demand variation and measurement error on link/path flow have been thoroughly discussed by Yang et al [93], Waller et al [82], Shao et al [67]. Here we use a toy example to compare probabilistic O-D estimation results with and without the consideration of day-to-day route choice variation.…”

Section: An Illustrative Examplementioning

confidence: 99%

“…In addition, using uncongested networks simplifies the route choice model, and thus no network equilibrium under congestion is proposed. Shao et al [67], Yang et al [95] proposed a generalized model to estimate the mean and variance of O-D demand with MVN. Shao et al [68] further extended the model to estimate multi-class O-D demand and used L 1 regularizer to shrink the model dimensions.…”

Section: Introductionmentioning

confidence: 99%

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Statistical inference of probabilistic origin-destination demand using day-to-day traffic data

Ma

¹

,

Qian

²

2018

Transportation Research Part C: Emerging Technologies

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Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

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“…As transportation demand has a spatial nature, it seems a plausible hypothesis that OD lows that share the same origin or the same destination may have covariances different from zero. In a recent study, Shao et al (2014) proposed a model that estimates both the mean OD lows and their covariances from link counts. However, their approach greatly increases the number of parameters to be estimated, which demands a large amount of data in order to obtain estimates with good accuracy.…”

Section: Model Assump>onsmentioning

confidence: 99%

Statistical models for the estimation of the origin-destination matrix from traffic counts

Neto

¹

,

Oliveira-Neto

²

,

Loureiro

³

2017

Get access via publisher Add to dashboard Cite

Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

show abstract

Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts

Cited by 75 publications

References 56 publications

A general stochastic process for day-to-day dynamic traffic assignment: Formulation, asymptotic behaviour, and stability analysis

A general stochastic process for day-to-day dynamic traffic assignment: Formulation, asymptotic behaviour, and stability analysis

Statistical inference of probabilistic origin-destination demand using day-to-day traffic data

Statistical models for the estimation of the origin-destination matrix from traffic counts

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