Spatio temporal signal processing harnessing public transit for effective spatiotemporal

Abstract

Public transportation can be a potential source of generating a tremendous amount of data as a part of its daily operation. GPS (Global Positioning System) installed system can be used to track the position of buses and thereby collect a massive stream of traffic speed/ETA (Estimated Time of Arrival) data. An alternate approach called drive-by sensing where sensors can be installed on moving vehicles is a way of collecting highly-granular space/time datasets that can be merged with public transportation (buses) to provide a cost-effective solution. This approach can be used to sense a wide range of phenomena, including traffic speed, air pollution, road lighting, street surface quality, unsafe pedestrian movement, record parking violations, traffic congestion, and crowd flows. Our work mainly focuses on traffic speed and air quality data sensing. The data sampled using sensor sources contain missing values due to sensor malfunctioning or the irregularity in the sensor measurements. The missing data percentage further shoots up in case of drive-by sensing data collection. In this work, we explored three problems spatiotemporal sampling, estimation and prediction for effective and reliable public transportation data acquisition and analysis. First, we propose a Robust Variational Bayesian Subspace Filtering framework for missing data estimation and outlier removal. We also propose an Extreme Matrix completion for missing data estimation using Variational Bayesian Filtering with Subspace information for a higher percentage of missing data. We showed that incorporating the previous subspace information can reduce the sampling complexity of the data; therefore, it can be a potential algorithm to estimate the data in case of moving sensors. Second, we propose Regressive Facility Location, a sampling algorithm to pick sets of paths (using vehicles) that perform representative sampling in space and time.