| Author: | Gu, Yu |
| Title: | Advancing travel demand models : from individual choices to equilibrium analysis with mathematical programming methods |
| Advisors: | Chen, Anthony (CEE) Sun, Defeng (AMA) |
| Degree: | Ph.D. |
| Year: | 2024 |
| Award: | FCE Awards for Outstanding PhD Theses (2023/24) |
| Subject: | Transportation -- Mathematical models Choice of transportation -- Mathematical models Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Civil and Environmental Engineering |
| Pages: | xix, 294 pages : color illustrations |
| Language: | English |
| Abstract: | In the era of emerging technologies, a variety of innovative transport policies and mobility services have been implemented and promoted in the transition to future transportation systems, such as road pricing, customized bus, e-hailing, bike sharing, and shared parking. The fusion of emerging technologies has created opportunities to not only improve the transportation service level but also provide unprecedented service features, which can exert a transformative impact on the multi-dimensional travel behavior and hence the multi-level travel demand pattern in multi-modal transportation systems. Transportation planning, which is crucial to address the fundamental needs of emerging technologies, requires advanced network equilibrium models for accurate analysis of travel demands. Equilibrium models analyze aggregate travel demand considering the effect of interactions among travelers in the individual choice. The classical random utility models (RUMs), such as the multinomial logit model and its variants, have been primarily embedded in equilibrium models for reproducing individual travel choices with conventional mobility services. Despite allowing the computationally manageable mathematical programming (MP) formulation for equilibrium models, the embedded RUMs are restrictive in modeling the complex travel behavior with the features of emerging mobility services, including distinct magnitudes of travel disutility, provisions of loyalty subscription schemes, and mobility bundling strategies. In addition, the classical closed-form RUMs are mainly based on an additive utility function and restrictive assumptions that the random errors are identically and independently Gumbel distributed, which are inadequate to reflect the way individuals perceive travel disutility in emerging choice contexts. Although open-form choice models can address some of the issues, the lack of an analytical probability expression poses additional difficulties for model estimation, interpretation, and evaluation. Also, owing to the computationally burdensome evaluation of open-form probabilities, the equilibrium problem becomes intractable when the choice set contains more than a handful of alternatives in real-world applications using large-scale transportation networks. This research aims to advance the travel demand modeling considering both the multi-dimensional travel choice behavior with emerging service features at the disaggregate level and the mobility operational features and interactions among travelers at the aggregate level. To achieve this goal, the objectives of this research lie in the developments of (1) advanced RUMs with relaxed model assumptions, alternate utility functional form, and alternate distributional assumptions, to reproduce the heterogeneous multi-dimensional behavioral changes facing with different emerging mobility services, and (2) advanced equilibrium models with computationally manageable MP formulations while retaining consistent with the individual choices reproduced by the developed RUMs based on endogenous travel disutility. The contributions of this research are summarized as follows: (1) The properties and derivation of the state-of-the-art multiplicative random utility models (MRUMs) are investigated to facilitate the development of advanced travel choice models. On this basis, the applications of MRUM in accessibility and vulnerability assessment are proposed, facilitating the analyses of transportation system performances under both normal and abnormal conditions. (2) Advanced RUMs are developed to reproduce individual travel choices in multi-modal transportation systems with emerging mobility services. Different from conventional travel choice models, the proposed models can simultaneously address various behavioral issues arisen with the introduction of emerging policy and mobility services. Specifically, the heterogeneous perceptions of travel distance, scale heterogeneity with respect to mobility service quality, perceptual correlation in path cost perceptions, mode correlation among similar travel modes, spatial correlation among adjacent locations and overlapped routes, effect of mobility bundling strategies, and repeated choice behavior arisen from loyalty subscription schemes, are considered to reflect the behavioral reactions to various emerging mobilities at different choice dimensions. Further, an innovative closed-form MRUM with alternate distributional assumptions is proposed, which can capture the choice context with an emerging travel alternative that has unprecedented service features. The innovative closed-form MRUM can provide new behavioral insights into various decision-making scenarios in the transition to future transportation system. (3) Advanced network equilibrium models are developed to analyze aggregate travel demand patterns consistent with the individual travel choice behavior while considering interactions among travelers. Specifically, the proposed models respectively consider the equilibrium bi-criteria route choice in tolled networks, equilibrium mode choice in multi-modal systems with customized bus services, equilibrium of joint bundle and mode choice in multi-modal systems with various emerging mobility services, and equilibrium of joint destination and parking choice among spatially distributed locations with shared parking services. Benefiting from the properties of the developed RUMs, the proposed equilibrium models are formulated as equivalent MP problems. The MP formulation facilitates the understanding and interpretation of the equilibrium models, enables the application to real-world case studies using convergent and efficient solution algorithms, and facilitates the sensitivity analysis-based evaluation of transportation system performances. |
| Rights: | All rights reserved |
| Access: | open access |
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