Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Industrial and Systems Engineering | en_US |
| dc.contributor.advisor | Xu, Min (ISE) | en_US |
| dc.contributor.advisor | Lee, Ka Man Carman (ISE) | en_US |
| dc.creator | Wang, Yilun | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14534 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Personalized service and operation optimization for emerging mobility solutions : from ground to air | en_US |
| dcterms.abstract | Urban mobility stands at a critical crossroads as rapid urbanization transforms how people and goods move through cities. By 2050, nearly 70% of the global population will live in urban areas, creating unprecedented challenges in meeting the mobility needs of growing populations. Various mobility solutions have been implemented to satisfy the increasing demand for on-demand transportation services, such as on-demand ride services for individuals (e.g., e-hailing), on-demand delivery services for parcels (e.g., same-day delivery), and the upcoming urban air mobility services. While these emerging ground and aerial services offer considerable potential to improve fleet utilization and reduce congestion, their successful implementation faces a fundamental challenge: balancing personalized service delivery with system-wide efficiency in dynamic and stochastic operational environments characterized by fluctuating demand, uncertain passengers behavior, and complex operational constraints. Current approaches often prioritize one at the expense of the other, leading to solutions that may face economic challenges or fail to meet passengers expectations. This critical gap demands innovative approaches that can effectively balance both priorities. | en_US |
| dcterms.abstract | This thesis aims to address this challenge through a series of studies on advanced operations, particularly through personalized service and intelligent operating strategies for emerging mobility solutions across both ground and air domains to improve passenger satisfaction while ensuring economically viable urban mobility systems for the future. | en_US |
| dcterms.abstract | Study I in Chapter 3 investigates the integration of parcel delivery and passenger ride services by a single fleet of mobility-on-demand companies. A dynamic confirmation, compensation, and routing problem is formulated to incentivize passengers to accept package delivery integration into existing passenger transport operations. We propose an innovative anticipatory policy underpinned by a novel regression-tree-based value function approximation to determine whether to serve newly arrived passengers, compensation amounts, and route decisions in a stochastic and dynamic environment. | en_US |
| dcterms.abstract | Study II in Chapter 4 extends this investigation to urban air mobility by designing and evaluating a novel urban air taxi service model that enables passenger pooling and flight transfers. This model explores the fundamental trade-offs between operational costs and individual passenger experience to establish the viability and potential of this advanced service modality. We formulate an integer programming model based on a hybrid time-space and time-space-battery graph and develop an adaptive arc generation algorithm to efficiently solve this problem. | en_US |
| dcterms.abstract | Study III in Chapter 5 builds upon the foundations established in the previous studies to tackle the ultimate challenge of seamlessly integrating ground and air transportation into a unified, passenger-centric mobility service. To this end, we investigate a choice-aware air taxi service, where multiple multimodal trip options are offered to dynamically arriving passengers. We propose an efficient anticipatory policy supported by a value function approximation based on deep neural networks to optimize option offering, pricing, and dispatching decisions in a stochastic and dynamic environment. | en_US |
| dcterms.abstract | Together, these three studies form a progressive and interconnected exploration of how intelligent personalized service mechanisms and routing can address the operational challenges of emerging mobility solutions from ground to air integrated systems. Managerial insights are derived from numerical experiments to support future mobility operations. | en_US |
| dcterms.extent | 1 volume (various pagings) : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2026 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Urban transportation -- Planning | en_US |
| dcterms.LCSH | Urban transportation -- Passenger traffic | en_US |
| dcterms.LCSH | Ridesharing | en_US |
| dcterms.LCSH | Vertically rising aircraft | en_US |
| dcterms.LCSH | Transportation demand management | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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