Stochastic Demand Dynamic Traffic Models Using Generalized Beta-Gaussian Bayesian Networks

Castillo, Enrique; Nogal, María; Menéndez, José María; Sánchez-Cambronero, Santos; Jimenez, P.

doi:10.1109/tits.2011.2173933

Cited by 43 publications

(23 citation statements)

References 26 publications

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“…In other words, this statement avoids derivatives. Replacing differential equations by functional equations is very relevant and should not be overlooked (see Wu et al [28] and Castillo et al [29]).…”

Section: Traffic Flow Dynamicsmentioning

confidence: 99%

“…At a given instant, we can know the traffic characteristics at other locations in previous instants, and, looking for similarities or using inference, we can predict future traffic flows. For example, Castillo et al [29] develop a stochastic demand dynamic traffic model to predict traffic variables (link travel times, flows, densities, etc.) and their time evolution in real networks.…”

Section: Dynamic Modelsmentioning

confidence: 99%

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A State-of-the-Art Review of the Sensor Location, Flow Observability, Estimation, and Prediction Problems in Traffic Networks

et al. 2015

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A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations.

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Section: Traffic Flow Dynamicsmentioning

confidence: 99%

Section: Dynamic Modelsmentioning

confidence: 99%

A State-of-the-Art Review of the Sensor Location, Flow Observability, Estimation, and Prediction Problems in Traffic Networks

et al. 2015

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“…The shifted gamma models, which are an example of this type of extension, have already been suggested by Castillo et al (2013b) for traffic flows and the locationscale beta has been used by Castillo et al (2012).…”

Section: Extension To Location-scale Familiesmentioning

confidence: 99%

“…Beta models . The extended location‐scale beta model has been used by Castillo et al () to provide a more realistic and a positive range for the variables and extra degrees of freedom. More precisely, they proposed to transform the marginals of a multivariate normal to generalized beta variables, that is, variables with range [ a i , b i ] instead of the [0,1] range of standard beta variables. 7.…”

Section: Introductionmentioning

confidence: 99%

On the Probabilistic and Physical Consistency of Traffic Random Variables and Models

Castillo

Calviño

Nogal

et al. 2014

Computer aided Civil Eng

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“…[15]. In the field of transportation, BN has been used for travel behavior prediction [16,17], incident prediction and classification [18,19], and demand estimation [20][21][22]. Continuous BN was developed for traffic flow prediction [12-14, 23, 24], but previous studies have not 1112 Z. ZHU ET AL.…”

mentioning

confidence: 99%

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Zhu

Peng

Xiong

et al. 2016

J of Advced Transportation

118

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SUMMARYTraffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short-term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included.

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Stochastic Demand Dynamic Traffic Models Using Generalized Beta-Gaussian Bayesian Networks

Cited by 43 publications

References 26 publications

A State-of-the-Art Review of the Sensor Location, Flow Observability, Estimation, and Prediction Problems in Traffic Networks

A State-of-the-Art Review of the Sensor Location, Flow Observability, Estimation, and Prediction Problems in Traffic Networks

On the Probabilistic and Physical Consistency of Traffic Random Variables and Models

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

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