Leveraging Large-scale Urban Data Analysis in Designing Intelligent Urban Transportation Systems
Abstract: Urbanization’s rapid progress has modernized many peoples’ lives but also engendered many big issues, such as traffic congestion, energy consumption and safety. Thanks to the ubiquitous mobile sensing data harvested from various devices (e.g., smartphones, in-vehicle navigation systems), the analysis, design and implementation of intelligent urban transportation systems that tackle theses big issues become possible. While many previous works have comprehensively studied various aspects regarding the dynamics of cities, we believe the operation of metropolitan cities can be further improved from the perspectives of human mobility, vehicle traffic, and public safety with the ever-growing ubiquitous urban sensing techniques.
The proposed research will design systems or methods based on extensive analysis of various aspects of urban mobility. Specifically, we will focus on three areas: (1) Mobile Opportunistic Networks with focus on human mobility; (2) Urban Transportation Systems with focus on vehicle traffic; and (3) safety and privacy issues emerging from urbanization.
For each area of proposed research, we aim to improve upon existing approaches to increase the efficiency/convenience/safety of urban transportation systems. We will evaluate the proposed methods through comparing their performance with that of the state-of-art methods on both large-scale urban datasets and real-world criteria and implementations.
Committee members: Haiying Shen (Advisor), John A. Stankovic (Chair), David Evans, John Lach, Brian Smith (Department of Civil and Environmental Engineering)