12,486 research outputs found
Arriving on time: estimating travel time distributions on large-scale road networks
Most optimal routing problems focus on minimizing travel time or distance
traveled. Oftentimes, a more useful objective is to maximize the probability of
on-time arrival, which requires statistical distributions of travel times,
rather than just mean values. We propose a method to estimate travel time
distributions on large-scale road networks, using probe vehicle data collected
from GPS. We present a framework that works with large input of data, and
scales linearly with the size of the network. Leveraging the planar topology of
the graph, the method computes efficiently the time correlations between
neighboring streets. First, raw probe vehicle traces are compressed into pairs
of travel times and number of stops for each traversed road segment using a
`stop-and-go' algorithm developed for this work. The compressed data is then
used as input for training a path travel time model, which couples a Markov
model along with a Gaussian Markov random field. Finally, scalable inference
algorithms are developed for obtaining path travel time distributions from the
composite MM-GMRF model. We illustrate the accuracy and scalability of our
model on a 505,000 road link network spanning the San Francisco Bay Area
Improving Query Classification by Features’ Weight Learning
This work is an attempt to enhance query classification in call routing applications. A new method has been introduced to learn weights from training data by means of a regression model. This work has investigated applying the tf-idf weighting method, but the approach is not limited to a specific method and can be used for any weighting scheme. Empirical evaluations with several classifiers including Support Vector Machines (SVM), Maximum Entropy, Naive Bayes, and k-Nearest Neighbor (k-NN) show substantial improvement in both macro and micro F1 measures
TARANET: Traffic-Analysis Resistant Anonymity at the NETwork layer
Modern low-latency anonymity systems, no matter whether constructed as an
overlay or implemented at the network layer, offer limited security guarantees
against traffic analysis. On the other hand, high-latency anonymity systems
offer strong security guarantees at the cost of computational overhead and long
delays, which are excessive for interactive applications. We propose TARANET,
an anonymity system that implements protection against traffic analysis at the
network layer, and limits the incurred latency and overhead. In TARANET's setup
phase, traffic analysis is thwarted by mixing. In the data transmission phase,
end hosts and ASes coordinate to shape traffic into constant-rate transmission
using packet splitting. Our prototype implementation shows that TARANET can
forward anonymous traffic at over 50~Gbps using commodity hardware
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