Applicable filtering framework for online multiclass freeway network estimation

Ngoduy, Dong

doi:10.1016/j.physa.2007.10.013

Cited by 32 publications

(20 citation statements)

References 31 publications

(47 reference statements)

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“…Recently, many research studies have focused on traffic state estimation problem (Wang et al 2011;Munoz et al 2003 ;Munoz et al 2006;Tampere and Immers 2007;Sun, Munoz and Horowitz 2004, Ngoduy 2008, Ngoduy 2011a. Of particular relevance to the present paper is the work of Wang and Papageorgiou (2005).…”

Section: Introductionmentioning

confidence: 94%

“…Wang et al (2011) applied this methodology for traffic state estimation in a freeway network of 100 km in Italy. Ngoduy (2008) proposed a framework that utilizes a particle filtering algorithm with a second-order traffic flow model to estimate traffic for a section of freeway; and in Ngoduy (2008;2011a) utilized an unscented Kalman filter algorithm with a macroscopic traffic flow model for freeway traffic state estimation. Park and Lee (2004) used a Bayesian technique to estimate travel speed for a link of an urban arterial using data from a dual loop detector.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of traveller information and route choice based on real-time estimated traffic state

Ahmed

Ngoduy

Watling

2015

Transportmetrica B: Transport Dynamics

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Prediction of traveller information and route choice based on real-time estimated traffic stateAccurate depiction of existing traffic states is essential to devise effective real-time traffic management strategies using Intelligent Transportation Systems (ITS). Existing applications of Dynamic Traffic Assignment (DTA) methods are mainly based on either the prediction from macroscopic traffic flow models or measurements from the sensors and do not take advantage of the traffic state estimation techniques, which produce estimate of the traffic states which has less uncertainty than the prediction or measurement alone. On the other hand, research studies which highlight estimation of real-time traffic state are focused only on traffic state estimation and have not utilized the estimated traffic state for DTA applications. In this paper we propose a framework which utilizes real-time traffic state estimate to optimize network performance during an incident through traveller information system. The estimate of real-time traffic states is obtained by combining the prediction of traffic density using Cell Transmission Model (CTM) and the measurements from the traffic sensors in Extended KalmanFilter (EKF) recursive algorithm. The estimated traffic state is used for predicting travel times on alternative routes in a small traffic network and the predicted travel times are communicated to the commuters by a variable message sign (VMS). In numerical experiments on a two-route network, the proposed estimation and information method is seen to significantly improve travel times and network performance during a traffic incident.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Prediction of traveller information and route choice based on real-time estimated traffic state

Ahmed

Ngoduy

Watling

2015

Transportmetrica B: Transport Dynamics

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“…Kalman filtering technique can be accommodated with irregular variation. It is effective in single-step prediction but lacks in accuracy in multi-step prediction [13] .…”

Section: S Zhong Et Al: a Hybrid Model Based On Support Vector Machmentioning

confidence: 99%

A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

Zhong¹,

et al. 2015

PROMET

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“…Other methods have been recently proposed to enable the on-line prediction of traffic state, including travel time, in urban networks using heterogeneous data (Nantes et al, 2015). Such methods rely on traffic flow models, as a means of predicting the next state of traffic, which are embedded in the Bayesian estimation fitlers such as Kalman filters or particle filters (Ngoduy, 2008, 2011, Wang and Papageorgiou, 2005, Wang et al, 2007.…”

Section: Probabilistic Travel Time Progressionmentioning

confidence: 99%

Probabilistic travel time progression and its application to automatic vehicle identification data

Nantes

Ngoduy

Miska

et al. 2015

Transportation Research Part B: Methodological

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Travel time has been identified as an important variable to evaluate the performance of a transportation system. Based on the travel time prediction, road users can make their optimal decision in choosing route and departure time. In order to utilize adequately the advanced data collection methods that provide realtime different types of information, this paper is aimed at a novel approach to the estimation of long roadway travel times, using Automatic Vehicle Identification (AVI) technology. Since the long roads contain a large number of scanners, the AVI sample size tends to reduce and, as such, computing the distribution for the total road travel time becomes difficult. In this work, we introduce a probabilistic framework that extends the deterministic travel time progression method to dependent random variables and enables the off-line estimation of road travel time distributions. In the proposed method, the accuracy of the estimation does not depend on the size of the sample over the entire corridor, but only on the amount of historical data that is available for each link. In practice, the system is also robust to small link samples and can be used to detect outliers within the AVI data.

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Applicable filtering framework for online multiclass freeway network estimation

Cited by 32 publications

References 31 publications

Prediction of traveller information and route choice based on real-time estimated traffic state

Prediction of traveller information and route choice based on real-time estimated traffic state

A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

Probabilistic travel time progression and its application to automatic vehicle identification data

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