Laura Wynter - Academia.edu (original) (raw)
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Higher Institute of Applied Sciences and Technology (HIAST)
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Papers by Laura Wynter
Transportation Research Part C: Emerging Technologies, 2011
ABSTRACT Real-time road traffic prediction is a fundamental capability needed to make use of adva... more ABSTRACT Real-time road traffic prediction is a fundamental capability needed to make use of advanced, smart transportation technologies. Both from the point of view of network operators as well as from the point of view of travelers wishing real-time route guidance, accurate short-term traffic prediction is a necessary first step. While techniques for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. We present a method that has proven to be able to meet this challenge. The method presented provides predictions of speed and volume over 5-min intervals for up to 1h in advance.
Complex Networks and Dynamic Systems, 2013
Abstract Accurate prediction of incident duration is critical for efficient incident management w... more Abstract Accurate prediction of incident duration is critical for efficient incident management which aims to minimize the impact of non-recurrent congestion. In this chapter, a hybrid tree-based quantile regression method is proposed for incident duration prediction and quantification of the effects of various incident and traffic characteristics that determine duration. Hybrid tree-based quantile regression incorporates the merits of both quantile regression modeling and tree-structured modeling: robustness to outliers, simple ...
The spatio-temporal relationship is an essential aspect of road traffic prediction. The fundament... more The spatio-temporal relationship is an essential aspect of road traffic prediction. The fundamental observation is that the traffic condition at a link is affected by the immediate past traffic conditions of some number of its neighboring links. A time lag function defines how traffic flows are related in the temporal dimension. In parallel, the spatial structure defines which neighboring links
this paper.Other issues persist and we have dealt with these by making certain assumptions on the... more this paper.Other issues persist and we have dealt with these by making certain assumptions on the objectives of the optimizationand its characteristics. These are described below
2014 IEEE International Conference on Data Mining Workshop, 2014
Lecture Notes in Computer Science, 2004
ABSTRACT In this work we model the relationship between the capacity and the Quality of Service (... more ABSTRACT In this work we model the relationship between the capacity and the Quality of Service (QoS) oered by the rm in a competitive scenario of two rm's working to maximize their prots. Using simple queueing theoretic models we study the sensitivity of a rm's market share to price, capacity and market size. Our preliminary studies yield important properties of the equilibrium solution which may further pro- vide important \engineering" guidelines for performance planning and pricing strategies.
Proceedings of the 19th IFAC World Congress, 2014
Lecture Notes in Computer Science, 2003
Proceedings of the 4th ACM conference on Electronic commerce - EC '03, 2003
Performance modeling has become increasingly important in the design, engineering and optimizatio... more Performance modeling has become increasingly important in the design, engineering and optimization of information technology (IT) infrastructures and applications. However, modeling work itself is time consuming and requires a good knowledge not only of the system, but also of modeling techniques. One of the biggest challenges in modeling complex IT systems consists in the calibration of model parameters, such as the service requirements of various job classes. We present an approach for solving this problem in the queueing network framework using inference techniques. This is done through a mathematical programming formulation, for which we propose an efficient and robust solution method. The necessary input data are end-to-end measurements which are usually easy to obtain. The robustness of our method means that the inferred model performs well in the presence of noisy data and further, is able to detect and remove outlying data sets. We present numerical experiments using data from real IT practice to demonstrate the promise of our framework and algorithm.
Transportation Research Part C: Emerging Technologies, 2011
ABSTRACT Real-time road traffic prediction is a fundamental capability needed to make use of adva... more ABSTRACT Real-time road traffic prediction is a fundamental capability needed to make use of advanced, smart transportation technologies. Both from the point of view of network operators as well as from the point of view of travelers wishing real-time route guidance, accurate short-term traffic prediction is a necessary first step. While techniques for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. We present a method that has proven to be able to meet this challenge. The method presented provides predictions of speed and volume over 5-min intervals for up to 1h in advance.
Complex Networks and Dynamic Systems, 2013
Abstract Accurate prediction of incident duration is critical for efficient incident management w... more Abstract Accurate prediction of incident duration is critical for efficient incident management which aims to minimize the impact of non-recurrent congestion. In this chapter, a hybrid tree-based quantile regression method is proposed for incident duration prediction and quantification of the effects of various incident and traffic characteristics that determine duration. Hybrid tree-based quantile regression incorporates the merits of both quantile regression modeling and tree-structured modeling: robustness to outliers, simple ...
The spatio-temporal relationship is an essential aspect of road traffic prediction. The fundament... more The spatio-temporal relationship is an essential aspect of road traffic prediction. The fundamental observation is that the traffic condition at a link is affected by the immediate past traffic conditions of some number of its neighboring links. A time lag function defines how traffic flows are related in the temporal dimension. In parallel, the spatial structure defines which neighboring links
this paper.Other issues persist and we have dealt with these by making certain assumptions on the... more this paper.Other issues persist and we have dealt with these by making certain assumptions on the objectives of the optimizationand its characteristics. These are described below
2014 IEEE International Conference on Data Mining Workshop, 2014
Lecture Notes in Computer Science, 2004
ABSTRACT In this work we model the relationship between the capacity and the Quality of Service (... more ABSTRACT In this work we model the relationship between the capacity and the Quality of Service (QoS) oered by the rm in a competitive scenario of two rm's working to maximize their prots. Using simple queueing theoretic models we study the sensitivity of a rm's market share to price, capacity and market size. Our preliminary studies yield important properties of the equilibrium solution which may further pro- vide important \engineering" guidelines for performance planning and pricing strategies.
Proceedings of the 19th IFAC World Congress, 2014
Lecture Notes in Computer Science, 2003
Proceedings of the 4th ACM conference on Electronic commerce - EC '03, 2003
Performance modeling has become increasingly important in the design, engineering and optimizatio... more Performance modeling has become increasingly important in the design, engineering and optimization of information technology (IT) infrastructures and applications. However, modeling work itself is time consuming and requires a good knowledge not only of the system, but also of modeling techniques. One of the biggest challenges in modeling complex IT systems consists in the calibration of model parameters, such as the service requirements of various job classes. We present an approach for solving this problem in the queueing network framework using inference techniques. This is done through a mathematical programming formulation, for which we propose an efficient and robust solution method. The necessary input data are end-to-end measurements which are usually easy to obtain. The robustness of our method means that the inferred model performs well in the presence of noisy data and further, is able to detect and remove outlying data sets. We present numerical experiments using data from real IT practice to demonstrate the promise of our framework and algorithm.