Exploiting mobility for predictive urban analytics & operations

• Main Author: JAYARAJAH, Kasthuri
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Urban Analytics; Mobile Computing; Social Networks; Numerical Analysis and Scientific Computing; Social Media; Urban Studies and Planning
• Online Access: https://ink.library.smu.edu.sg/etd_coll/227; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1227&context=etd_coll
• Institution: Singapore Management University

As cities worldwide invest heavily in smart city infrastructure, it invites opportunities for a next wave of urban analytics. Unlike its predecessors, urban analytics applications and services can now be real-time and proactive -- they can (a) leverage situational data from large deployments of connected sensors, (b) capture attributes of a variety of entities that make up the urban fabric (e.g., people and their social relationships, transport nodes, utilities, etc.), and (c) use predictive insights to both proactively optimize urban operations (e.g., HVAC systems in smart buildings, buses in the transportation network, crowd-workers, etc.) and promote smarter policy decisions (e.g., land use decisions pertaining to the positioning of retail establishments, incentives and rebates for businesses). Individual and collective mobility has been long-touted as a key enabler of urban planning studies. With everyday artefacts that a city's population interacts with being increasingly embedded with hardware (e.g., contact-less smart fare cards that people tap-in and out of buses and metro), and due to the sheer uptake of location-based social media platforms in recent years, a wealth of mobility information is made available for both online and offline processing. This thesis makes two principal contributions -- it explores how such abundantly available mobility information can be (a) integrated with other urban data to provide aggregated insights into demand for urban resources, and (b) used to understand relationships among people and predict their movement behavior (including deviations from normal patterns). Additionally, this thesis introduces opportunities and offers preliminary evidence of how mobility information can be used to support a more efficient urban sensing infrastructure. First, the thesis explores how mobility can be combined with other urban data for better policy decisions and resource utilization prediction. It first investigates how aggregate mobility data from heterogeneous sources such as public transportation and social media, can aid in quantifying urban constructs (e.g., customer visitation patterns, mobility dynamics of neighborhoods) and then demonstrate their use, as an example, in predicting the survival chances of individual retailers, a key performance measure of land use decisions of a city. In the past, studies have relied on the predictability of mobility to generate various urban insights. In a complementary effort, by demonstrating the ability to predict instances of unpredictability, sufficiently in advance, this thesis explores opportunities to proactively optimize urban operations by harnessing such unpredictability. First it looks at individual mobility at campus-scale, to discover and quantify social ties. It then describes a framework to detect episodes of future anomalous mobility using social tie-aware mobility information, and then use such early warnings to demonstrate its use in an exemplar smart campus application; task assignments of workers of a mobility-aware crowd-sourcing platform. In a final exposition of emerging possibilities of using mobility for real-time, operational optimization, I introduce a paradigm for collaboration between co-located sensors in dense deployments that exploits human mobility, at short spatio-temporal scales. As preliminary work, this thesis investigates how associations between densely co-located cameras with partially overlapping views can reinforce inferences for better accuracy, and offers evidence of the feasibility to run adaptive, light-weight operations of deep learning networks that drastically cut down on processing latencies. This thesis provides additional examples of real--time, in-situ, mobility-driven urban applications, and concludes with key future directions.