UMTRI Speaker Series 2016: Yi Lu Murphey, PhD
Sponsor: UMTRI & ATLAS Center
Building Efficient Probability Transition Matrix using Machine Learning from Big Data for Personalized Driving Route Prediction
Personalized route prediction is an important technology in many applications related to intelligent vehicles and transportation systems. Current route prediction technologies used in most navigation systems are, by and large, based on either the shortest or the fastest route selection strategies. Personal traveling route prediction is a very challenging big data problem, as trips are getting longer and variations in routes growing. It is particularly challenging for real-time in-vehicle applications, since many embedded processors have limited memory and computational power. In this seminar, I will present a machine learning algorithm for modeling route prediction based on a Markov chain model, and a route prediction algorithm based on a probability transition matrix. I will also present two data reduction algorithms, one is developed to map large GPS based trips to a compact link-based standard route representation, and another a machine learning algorithm to significantly reduce the size of a probability transition matrix. The proposed algorithms are evaluated on real-world driving trip data collected in four months, where the data collected in the first three months are used as training and the data in the fourth month are used as testing. Our experiment results show that the proposed personal route prediction system generated more than 91% prediction accuracy in average among the test trips. The data reduction algorithms gave an 8:1 reduction in link-based standard route representation and 23:1 reduction in probability transition matrix.
Yi Lu Murphey is the Associate Dean for Graduate Education and Research and Professor in the Department of Electrical and Computer Engineering at the University of Michigan - Dearborn. Professor Murphey is actively involved in funded research in the areas of machine learning, computer vision, and intelligent systems with applications to engineering diagnostics, optimal vehicle power management, text data mining, and robotic vehicles. During the past several years she has received research grants from the State of Michigan 21st Jobs Fund, Ford Motor Company and US Army RDECOM-TARDEC to support her reserach in intelligent vehicle power management. Visit http://www-personal.engin.umd.umich.edu/~yilu/ for more details on her research
The seminar will be held in the UMTRI McCormick Conference room, 2901 Baxter Road, Ann Arbor, MI.
Please RSVP for the seminar by April 25, 2016.
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