Real-time road traffic congestion monitoring is an important and challenging problem. Most existing monitoring approaches require the deployment of infrastructure sensors or large-scale probe vehicles. Their installation is often expensive and temporal-spatial coverage is limited. Probe vehicle data are oftentimes noisy on urban arterials, and therefore insufficient to provide accurate congestion estimation. This paper presents a novel social-media based approach to traffic congestion monitoring, in which pedestrians, drivers, and passengers are treated as human sensors and their posted tweets in Twitter as observations of nearby ongoing traffic conditions. There are three technical challenges for road traffic monitoring based on Twitter, namely: 1) language ambiguity in the usage of trafficrelated terms; 2) uncertainty and low resolution of geographic location mentions; and 3) interactions between traffic-related events such as accidents and congestion. We propose a topic modeling based language model to address the first challenge and a collaborative inference model based on probabilistic soft logic (PSL) to address the second and third challenges. We present a unified statistical framework that combines those two models based on hinge loss Markov random fields (HLMRFs). In order to address the computational challenges incurred by the non-analytical integral of latent variables (factors) and the MAP estimation of a large number of location-dependent traffic congestion variables, we propose a fast approximate inference algorithm based on maximization expectation (ME) and the alternating directed method of multipliers (ADMM). Extensive evaluations over a variety of metrics on real world Twitter and INRIX probe speed datasets in two U.S. major cities demonstrate the efficiency and effectiveness of our proposed approach.
The dramatic market demand for flash memory in the past decade has vigorously driven technology evolution. Traditional memory scaling is facing huge lithographic barriers resulting in the inevitable pursuit of non-planar solutions. In this paper, we discuss the process technologies that are enabling us to overcome the challenges of extending the current planar platform while transitioning into future three-dimensional architectures.
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