Models and algorithms for the screen line-based traffic-counting location problems

Yang, Hai; Yang, Chao; Gan, Lu

doi:10.1016/j.cor.2004.08.011

Cited by 94 publications

(33 citation statements)

References 6 publications

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“…Yang & Zhou (1998) proposed a sensor deployment framework to maximize such utilities. This framework has been extended to accommodate turning traffic information (Bianco et al, 2001), existing installations and O-D information content (Ehlert et al, 2006), the screen line problem (Yang et al, 2006), time-varying network flows (Fei et al, 2007) and unobserved link flow estimation (Hu et al, 2009). The work of Bianco et al (2006) considered the problem of locating the minimum number of counting sensors on the network nodes in order to determine arc flow volumes of the entire network.…”

Section: Related Workmentioning

confidence: 99%

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Puzis

Altshuler

Elovici³

et al. 2012

Journal of Intelligent Transportation Systems

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Section: Related Workmentioning

confidence: 99%

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Puzis

Altshuler

Elovici³

et al. 2012

Journal of Intelligent Transportation Systems

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“…Some examples are given in Yang and Zhou (1998); Yang et al (2006); Castillo et al (2008c); and Mínguez et al (2010).…”

Section: Difficulties In Sampling Datamentioning

confidence: 98%

Two Applications of Statistics to Traffic Models

Castillo

Nogal

Calviño

2014

Communications in Statistics - Theory and Methods

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Two statistical applications for estimation and prediction of flows in traffic networks are presented. In the first, the number of route users are assumed to be independent α-shifted gamma (θ, λ 0 ) random variables denoted H (α, θ, λ 0 ), with common λ 0 . As a consequence, the link, OD (origin-destination) and node flows are also H (α, θ, λ 0 ) variables. We assume that the main source of information is plate scanning, which permits us to identify, totally or partially, the vehicle route, OD and link flows by scanning their corresponding plate numbers at an adequately selected subset of links. A Bayesian approach using conjugate families is proposed that allows us to estimate different traffic flows. In the second application, a stochastic demand dynamic traffic model to predict some traffic variables and their time evolution in real networks is presented. The Bayesian network model considers that the variables are generalized Beta variables such that when marginally transformed to standard normal become multivariate normal. The model is able to provide a point estimate, a confidence interval or the density of the variable being predicted. Finally, the models are illustrated by their application to the Nguyen Dupuis network and the Vermont-State example. The resulting traffic predictions seem to be promising for real traffic networks and can be done in real time.

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“…Yang and Zhou (1998) proposed four basic sensor location rules for OD estimation, the essence of which was to cover as much traffic flow as possible on different OD pairs. All path flow between each OD pair is required to be intercepted ( Yang et al, 2006 ). Using these coverage rules, a mobile traffic sensor was introduced to monitor the transportation network.…”

Section: Nomenclaturementioning

confidence: 99%