EAGER: Feedback-based Network Optimization for Smart Cities

Clemson University, Clemson SC

Investigators

Abstract

This EArly-concept Grant for Exploratory Research (EAGER) project will focus on human-infrastructure interactions within future smart cities. Specifically, the goal of this project is to develop a conceptual network optimization framework that exploits user feedback from crowd-sourced data. The next-generation ubiquitous traffic sensors, such as fixed traffic detectors, mobile sensors with location-based services (e.g., Google traffic), and traffic active mobile sensors (e.g., Waze), have been used to retrieve large and diverse geo-located and time-stamped data. In addition, data crowd-sourced from social media and mobile applications are increasingly available for understanding human mobility. On the other hand, existing network optimization models in transportation were largely developed without considering human behavior. This project will explore methods to design an electric vehicle wireless charging network of the future. Such a charging network will provide non-stop, in-motion charging particularly suitable for urban environments. For many users, wireless charging will be an opportunistic, emergency charging choice that supplements distributed charging resources at home, the workplace, and at retail facilities. Traditional network optimization models, which implicitly assume that charging demand is distributed and a given, may not result in a user-satisfactory solution in such tasks as finding the best routes that contain wireless charging network segments. The PIs aim to overcome this deficiency by developing: (i) user feedback-driven network optimization models that explicitly account for user satisfaction, and (ii) fast and scalable optimization methods for real-world, large-scale problem implementations that efficiently and effectively utilize crowd-sourced information. The results of this research will include a "living lab" assessment in which students will develop a mobile app to collect feedback about the route with wireless charging network segments. This feedback will be analyzed and incorporated in optimization models. This approach will be implemented using a combination of large-scale machine learning and optimization solvers. The mobile app will provide basic anonymized information about users, trip types, and route feedback through secured channels. These data and open information about all road segments will be used to explore relevant latent factors and create cost-sensitive support vector machine based classifiers to identify the most suitable segments for wireless charging network and adjust the optimization-based network design. Rather than using traditional, computationally expensive optimization solvers, the PIs will pursue an algebraic multigrid-based approach to cope with large-scale, real-world problems, and leverage their support vector machine solvers.