H. Zuylen

Publications176
h-index29
Citations3,169
Highly Influential Citations146
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Travel time unreliability on freeways: Why measures based on variance tell only half the story
TLDR
We argue this implies first of all that (besides the variance of travel times) skew must be considered an important contributing factor to travel time unreliability. Expand
Accurate freeway travel time prediction with state-space neural networks under missing data
Accuracy and robustness with respect to missing or corrupt input data are two key characteristics for any travel time prediction model that is to be applied in a real-time environment (e.g. for… Expand
Urban link travel time estimation based on sparse probe vehicle data
TLDR
In the urban signalized network, travel time estimation is a challenging subject especially because urban travel times are intrinsically uncertain due to fluctuations in traffic demand and supply, traffic signals, stochastic arrivals at the intersections, etc. Expand
Monitoring and Predicting Freeway Travel Time Reliability: Using Width and Skew of Day-to-Day Travel Time Distribution
TLDR
We propose a new metric for measuring travel time unreliability based on the skew and width of the day-to-day travel time distribution for freeway corridors. Expand
Construct support vector machine ensemble to detect traffic incident
TLDR
This study presents the applicability of support vector machine (SVM) ensemble for traffic incident detection by combining the common criteria, detection rate (DR), false alarm rate (FAR), mean time to detection (MTTD), and classification rate (CR). Expand
Localized Extended Kalman Filter for Scalable Real-Time Traffic State Estimation
TLDR
In this paper, it is shown that the L-EKF is much faster than the traditional Global EKF, that it scales much better with the network size, and that it leads to estimates with nearly the same accuracy. Expand
Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters
TLDR
A hybrid model for predicting urban arterial travel time on the basis of so-called state-space neural networks (SSNNs) and the extended Kalman filter (EKF) is presented. Expand
Link-level vulnerability indicators for real-world networks
It is computationally expensive to find out where vulnerable parts in a network are. In literature a variety of methods were introduced that use relatively simple selection criteria (measured in… Expand
A framework for robustness analysis of road networks for short term variations in supply
There is a growing awareness that road networks, are becoming more and more vulnerable to unforeseen disturbances like incidents and that measures need to be taken in order to make road networks more… Expand
Bayesian committee of neural networks to predict travel times with confidence intervals
Short-term prediction of travel time is one of the central topics in current transportation research and practice. Among the more successful travel time prediction approaches are neural networks and… Expand
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