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Events for June 11, 2025
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PhD Dissertation Defense - Chrysovalatnis Anastasiou
Wed, Jun 11, 2025 @ 12:30 PM - 02:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Recovering Trajectories From Location Data Probablistically
Date and Time: Wednesday, June 11th, 2025 | 12:30p - 2:30p
Location: PHE 106
Committee Members: Cyrus Shahabi (Chair), Jose-Luis Ambite, Marlon Boarnet
Abstract: Understanding urban mobility is crucial for effective city planning, transportation management, and the development of responsive location-based services. However, challenges associated with real-world trajectory data often significantly hamper the derivation of robust insights. These include privacy restrictions limiting access to detailed movement histories, inherent sparsity in collected data points, and uncertainty stemming from sensor inaccuracies. Existing approaches rely on deterministic assumptions (like shortest paths), or necessitate extensive calibration or large, potentially biased training datasets, hindering progress.
This thesis addresses these critical challenges by developing and evaluating a suite of novel data-driven and probabilistic methodologies. We first introduce a purely data-driven technique for time-dependent reachability analysis that leverages raw trajectory data directly, thereby bypassing the complexities of traditional graph-based. To handle data sparsity effectively, we propose time-variant, road network-constrained probabilistic models ("bridgelets"), which realistically represent the inherent uncertainty of movement between sparse location samples. Furthermore, we develop a comprehensive framework (VPE), to reliably estimate vehicle visit probabilities on road segments using observations from uncertain and potentially unreliable roadside sensors.
The practical effectiveness of the proposed methods is rigorously evaluated through extensive experiments using large-scale, real-world datasets from various cities. Quantitative and qualitative results demonstrate that our probabilistic and data-driven approaches significantly improve accuracy and efficiency compared to baseline and traditional techniques. Collectively, the contributions of this thesis provide practical, robust, and innovative tools for researchers, planners, and policymakers to gain deeper, more reliable insights into complex urban mobility dynamics, enabling more informed decision-making even when faced with prevalent data limitations.Location: Charles Lee Powell Hall (PHE) - 106
Audiences: Everyone Is Invited
Contact: Chrysovalantis Anastasiou
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
