Ziyi Shi

Emerging mobility services such as Urban Air Mobility (UAM) complicate mode-choice prediction because travelers may position them differently relative to existing modes. Conventional econometric and machine-learning models mainly capture substitution indirectly through error distributions or population-level structures. They are not designed to flexibly capture traveler-specific pairwise substitution patterns across multiple modes. This study proposes a Persona-Conditioned Graph Neural Network (PC-GNN) that learns individual substitution structures with nonlinear preferences. Because such structures are difficult to identify from sparse stated-choice observations alone, large language models (LLMs) are introduced to elicit diverse priors from a subset of travelers and store them in a reusable belief bank. The priors contain persona vectors and substitutability matrices, representing the individual substitution structures of these travelers. For the remaining travelers, observed attributes are combined with retrieved LLM-elicited priors to construct multiple conditioned GNNs, and a retrieval-mixture module then produces the final prediction. We further relate PC-GNN to established discrete choice models and derive an approximation bound to characterize this retrieval-mixture mechanism. Using a stated-preference survey of nearly 1,600 respondents in the Guangdong-Hong Kong-Macao Greater Bay Area, the analysis reveals heterogeneity in UAM substitution. High-speed rail (HSR) emerges as its primary substitute, while car-oriented travelers exhibit a substitution pattern involving both HSR and private cars. PC-GNN also improves the mode-choice predictive accuracy by about 13% over second-best baselines. Ablation and scalability analyses further confirm the value of persona augmentation, individual-specific graphs, and behaviorally informed retrieval.

Hybrid bike-sharing systems (operating both bikes and ebikes) are emerging worldwide for flexibility and sustainability. Excessive rebalancing, while addressing supply-demand imbalances, causes financial burdens and resource waste. Hence, long-term daily operations (e.g., rebalancing at midnights for upcoming days) have practical economic significance, but they remain underexplored due to difficulties in tracking high within-day and between-day demand variability and the interplay between demand and daily inventory management strategies. Besides, cooperative operation of bikes and ebikes expands solution space, given their inherent demand coupling. This study is the first to explore long-term daily inventory strategies for the hybrid system (ebikes get charged at stations), determining spatial-heterogeneous inventory at midnight to maximize profits. To address the demand variability perception issue, we develop a recurrent-attentive neural process (RANP) model to predict hour-to-hour demand of the upcoming day. The RANP is integrated into a long-term optimization model, referred to as the generative-models-informed Markov decision process (GMI-MDP), where two cooperative intelligent agents determine the bike–ebike allocation based on demand perception and system rewards. A suite of numerical experiments utilizing a real-world dataset from New York is carried out, and various strategies are compared. The proposed method, through online and offline tests, demonstrates superiority over other MDP-based and rule-based methods, achieving a faster solution-seeking process and more stable rewards than MDP with exact upcoming demand. By comparing inventory volatility, we offer insights into managerial operations.

Dockless Bike-Sharing (DBS) is an eco-friendly, convenient, and popular form of ride-sharing. Metro-oriented DBS systems have the potential to promote sustainable transportation. However, the availability of DBS near metro stations often suffers from either scarcity or overabundance. To investigate the factors contributing to this imbalance, this paper examines the nonlinear influences and interactions that impact the DBS system near metro stations, with Shenzhen, China serving as a case study. An ensemble learning approach is employed to predict the imbalance state. Then, the machine learning interpretation method (i.e., SHapley Additive exPlanations) is used to quantify the contribution of effects, discover the strength of interactions between factors and uncover their underlying interactive connections. The results indicate the influence of external factors and the relations between pairwise variables (e.g., road density and the day of the week) for each imbalanced state. Provide two quantized sets of factors that can result in the supply-demand imbalance and support future transport planning decisions to enhance the accessibility and sustainability of Metro-oriented DBS systems.