Routing and charging game in ride-hailing service with electric vehicles

This paper studies the routing and charging behaviors of electric vehicles in a competitive ride-hailing market. When the vehicles are idle, they can choose whether to continue cruising to search for passengers, or move a charging station to recharge. The behaviors of individual vehicles are then modeled by a Markov decision process (MDP). The state transitions in the MDP model, however, depend on the aggregate vehicle flows both in service zones and at charging stations. Accordingly, the value function of each vehicle is determined by the collective behaviors of all vehicles. With the assumption of the large population, we formulate the collective routing and charging behaviors as a mean-field Markov game. We characterize the equilibrium of such a game, prove its existence, and numerically show that the competition among vehicles leads to ``inefficient congestion" both in service zones and at charging stations

An affordable nearly-zero-energy townhouse prototype for China's future low-carbon housing

As part of the 11th Five Year Plan on Energy Development (2006-2010), China once set up a 65% target for reducing the energy consumption of residential and public buildings by 2020. Then in 2016, the State Council of China directly referenced nearly-zero-energy buildings and PV micro-generation on the roof as the critical solutions to enhance building energy conservations and carbon emissions reductions. Most recently, in January 2019, China’s Technical Standard for Nearly Zero Energy Building (GB/T51350-2019) has been launched for implementation. All these policies aim to promote low-carbon buildings in China. However, in China’s housing sector, affordability has been a profound obstacle to implement low-carbon technologies. China’s high-rise high-density housing strategy also technically impairs the deployment of cost-effective micro renewable generations. Therefore, the main challenges not only lie with the cost of low-carbon technologies but the fundamental reform of housing strategy to enable building integrated renewable generation. This research aims to provide a new approachfor future low-carbon housing in China. It proposes an affordable nearly-zero-energy townhouse prototypein suburban area, which integrated affordable and replicable energy demand, energy storage and renewable energy supply solutions.This research has two main aspects of contribution to the existing literature. Firstly, on the theory side, this research fills the research gap on the low-rise low-carbon housing in China. It not only formulated the terminology and prototype of the affordable nearly-zero-energy townhouse but also provides a conceptual design framework to guide the design and integration process. Secondly, on the practical side, this research provides evidence for the large scale rollout of the approach by carrying out a real project case study. The case study covers the whole design, construction and commissioning process. It validated the applicability of this prototype at the regional and project level. It also evaluated the achieved indoor thermal and energy performances of the constructed house against its costs. This researchprovedthat the affordable nearly-zero-energy townhouse prototype couldapplyin a wide range of cities’ suburban areain China, and it suits forthe majority of climate zones. The embedded integration strategy of low-carbon technologies and the proposed design process are implementable in practice. The parameters defined in the national level prototype are competent to guide the development of project-level baseline building. The benchmarks set in the national level prototype are also capable of promoting affordable nearly-zero-energy performances with a reasonable costin real practice

HR-NeuS: Recovering High-Frequency Surface Geometry via Neural Implicit Surfaces

Recent advances in neural implicit surfaces for multi-view 3D reconstruction primarily focus on improving large-scale surface reconstruction accuracy, but often produce over-smoothed geometries that lack fine surface details. To address this, we present High-Resolution NeuS (HR-NeuS), a novel neural implicit surface reconstruction method that recovers high-frequency surface geometry while maintaining large-scale reconstruction accuracy. We achieve this by utilizing (i) multi-resolution hash grid encoding rather than positional encoding at high frequencies, which boosts our model's expressiveness of local geometry details; (ii) a coarse-to-fine algorithmic framework that selectively applies surface regularization to coarse geometry without smoothing away fine details; (iii) a coarse-to-fine grid annealing strategy to train the network. We demonstrate through experiments on DTU and BlendedMVS datasets that our approach produces 3D geometries that are qualitatively more detailed and quantitatively of similar accuracy compared to previous approaches