Kenneth Tracton

An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices

Traffic state estimation is a challenging problem for the transportation community due to the limited deployment of sensing infrastructure. However, recent trends in the mobile phone industry suggest that GPS equipped devices will become standard in the next few years. Leveraging these GPS equipped devices as traffic sensors will fundamentally change the type and the quality of traffic data collected on large scales in the near future. New traffic models and data assimilation algorithms must be developed to efficiently transform this data into usable traffic information. In this work, we introduce a new partial differential equation (PDE) based on the Lighthill-Whitham-Richards PDE, which serves as a flow model for velocity. We formulate a Godunov discretization scheme to cast the PDE into a Velocity Cell Transmission Model (CTM-v), which is a nonlinear dynamical system with a time varying observation matrix. The Ensemble Kalman Filtering (EnKF) technique is applied to the CTM- v to estimate the velocity field on the highway using data obtained from GPS devices, and the method is illustrated in microsimulation on a fully calibrated model of I880 in California. Experimental validation is performed through the unprecedented 100-vehicle Mobile Century experiment, which used a novel privacy-preserving traffic monitoring system to collect GPS cell phone data specifically for this research.

TruCentive: A game-theoretic incentive platform for trustworthy mobile crowdsourcing parking services

The shortage of parking in crowded urban areas causes severe societal problems such as traffic congestion, environmental pollution, and many others. Recently, crowdsourced parking, where smartphone users are exploited to collect realtime parking availability information, has attracted significant attention. However, existing crowdsourced parking information systems suffer from low user participation rate and data quality due to the lack of carefully designed incentive schemes. In this paper, we address the incentive problem of trustworthy crowdsourced parking information systems by presenting an incentive platform named TruCentive, where high utility parking data can be obtained from unreliable crowds of mobile users. Our contribution is three-fold. First, we provide hierarchical incentives to stimulate the participation of mobile users for contributing parking information. Second, by introducing utility-related incentives, our platform encourages participants to contribute high utility data and thereby enhances the quality of collected data. Third, our active confirmation scheme validates the parking information utility by game-theoretically formulated incentive protocols. The active confirming not only validates the utility of contributed data but re-sells the high utility data as well. Our evaluation through user study on Amazon Mechanical Turk and simulation study demonstrate the feasibility and stability of TruCentive incentive platform.