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Inspired by new technologies to monitor parking occupancy and process market signals, we aim to expand the application of demand-responsive pricing in the parking industry. Based on a graphical Hotelling model wherein each garage has information for its incoming parking demand, we consider a general competitive spatial pricing in parking systems under an asymmetric information structure. We focus on the impact of urban network structure on the incentive of information sharing. Our analyses suggest that the garages are always better off in a circular-networked city, while they could be worse off in the suburbs of a star-networked city. Nevertheless, the overall revenue for garages is improved and the aggregate congestion is reduced under information sharing. Our results also suggest that information sharing helps garages further exploit the customers who in turn become worse-off. Therefore, policy-makers should carefully evaluate their transportation data policy since impacts on the service-providers and the customers are typically conflicting. Using the SFpark data, we empirically confirmed the value of information sharing. In particular, garages with higher price-demand elasticity and lower demand variance tend to enjoy larger benefits via information sharing. These insights support the joint design of parking rates structure and information systems. 

With the rising need for efficient and flexible short-distance urban transportation, more vehicle sharing companies are offering one-way car-sharing services. Electrified vehicle sharing systems are even more effective in terms of reducing fuel consumption and carbon emission. In this article, we investigate a dynamic fleet management problem for an Electric Vehicle (EV) sharing system that faces time-varying random demand and electricity price. Demand is elastic in each time period, reacting to the announced price. To maximize the revenue, the EV fleet optimizes trip pricing and EV dispatching decisions dynamically. We develop a new value function approximation with input convex neural networks to generate high-quality solutions. Through a New York City case study, we compare it with standard dynamic programming methods and develop insights regarding the interaction between the EV fleet and the power grid. 

Thermal fields exist widely in engineering systems and are critical for engineering operation, product quality and system safety in many industries. An accurate prediction of thermal field distribution, that is, acquiring any location of interest in a thermal field at the present and future time, is essential to provide useful information for the surveillance, maintenance, and improvement of a system. However, thermal field prediction using data acquired from sensor networks is challenging due to data sparsity and missing data problems. To address this issue, we propose a field spatiotemporal prediction approach based on transfer learning techniques by studying the dynamics of a 3D thermal field from multiple homogeneous fields. Our model characterizes the spatiotemporal dynamics of the local thermal field variations by considering the spatiotemporal correlation of the fields and harnessing the information from homogeneous fields to acquire an accurate thermal field distribution in the future. A real case study of thermal fields during grain storage is conducted to validate our proposed approach. Grain thermal field prediction results provide a deep insight of grain quality during storage, which is helpful for the manager of grain storage to make further decisions about grain quality control and maintenance.