Long Vu, Sarah Kefayati, et al.
AMIA Annual Symposium
We present a new approach to lightweight intelligent transportation systems. Our approach does not rely on traditional expensive infrastructures, but rather on advanced machine learning algorithms. It takes images from traffic cameras at a limited number of locations and estimates the traffic over the entire road network. Our approach features two main algorithms. The first is a probabilistic vehicle counting algorithm from low-quality images that falls into the category of unsupervised learning. The other is a network inference algorithm based on an inverse Markov chain formulation that infers the traffic at arbitrary links from a limited number of observations. We evaluated our approach on two different traffic data sets, one acquired in Nairobi, Kenya, and the other in Kyoto, Japan.
Long Vu, Sarah Kefayati, et al.
AMIA Annual Symposium
Yasunori Yamada, Tetsuro Morimura
IJCAI 2016
Takashi Imamichi, Hidetoshi Numata, et al.
WSC 2011
Tsuyoshi Idé, Keerthiram Murugesan, et al.
KAIS