arXiv subjects

Hongzheng Shi

Publications and source records attributed to Hongzheng Shi.

3 recordsLinked to original sources

arXiv2026

Joint pricing and matching for dynamic high-capacity ride-sharing considering passengers' choice uncertainty

This work investigates the uncertainty-aware joint pricing and matching problem for dynamic high-capacity ride-sharing services, where passengers are assumed to be price-elastic and decide whether to accept a ride-sharing offer based on the upfront prices provided by the platform. We formulate the studied problem as a two-stage stochastic program, where the first stage optimizes upfront price decisions for passengers, and the second-stage recourse problem captures passenger-vehicle assignment based on passengers' uncertain choices. To enhance computational efficiency, we introduce a novel relaxation-based gradient descent-guided search algorithm that leverages the problem's structural properties. Initially, the algorithm generates a feasible solution for the first-stage problem via relaxation. It then iteratively improves the solution via a search process guided by the derived gradient information. In particular, scenario reduction is applied to eliminate unnecessary scenarios when calculating the gradient, thereby reducing the overall computational burden. Numerical experiments demonstrate that, compared to solving the stochastic program directly, the proposed algorithm can accelerate computation speed by thousands of times while achieving optimality gaps of no more than 1.1%. Finally, we validate the benefits of considering passengers' choice uncertainty through large-scale simulation using real-world datasets and road networks over two large cities. The results demonstrate that, on average, the proposed method can increase the revenue by 5.2% and the service rate by 8.2% compared to the baseline approaches. This study provides a valuable reference for transportation network companies to design pricing strategies for ride-sharing to enhance service efficiency and improve revenue.

eess.SY↗

arXiv2025

Fire-EnSF: Wildfire Spread Data Assimilation using Ensemble Score Filter

As wildfires become increasingly destructive and expensive to control, effective management of active wildfires requires accurate, real-time fire spread predictions. To enhance the forecasting accuracy of active fires, data assimilation plays a vital role by integrating observations (such as remote-sensing data) and fire predictions generated from numerical models. This paper provides a comprehensive investigation on the application of a recently proposed diffusion-model-based filtering algorithm -- the Ensemble Score Filter (EnSF) -- to the data assimilation problem for real-time active wildfire spread predictions. Leveraging a score-based generative diffusion model, EnSF has been shown to have superior accuracy for high-dimensional nonlinear filtering problems, making it an ideal candidate for the filtering problems of wildfire spread models. Technical details are provided, and our numerical investigations demonstrate that EnSF provides superior accuracy, stability, and computational efficiency, establishing it as a robust and practical method for wildfire data assimilation. Our code has been made publicly available.

cs.LG↗

arXiv2025

HRSim: An agent-based simulation platform for high-capacity ride-sharing services

The rapid growth of ride-sharing services presents a promising solution to urban transportation challenges, such as congestion and carbon emissions. However, developing efficient operational strategies, such as pricing, matching, and fleet management, requires robust simulation tools that can replicate real-world dynamics at scale. Existing platforms often lack the capacity, flexibility, or open-source accessibility needed to support large-scale, high-capacity ride-sharing services. To address these gaps, we introduce HRSim, an open-source, agent-based High-capacity Ride-sharing Simulator. HRSim integrates real-world road networks and demand data to simulate dynamic ride-sharing operations, including pricing, routing, matching, and repositioning. Its module design supports both ride-sharing and solo-hailing service modes. Also, it includes a visualization module for real-time performance analysis. In addition, HRSim incorporates integer linear programming and heuristic algorithms, which can achieve large-scale simulations of high-capacity ride-sharing services. Applications demonstrate HRSim's utility in various perspectives, including quantifying carbon emissions, scaling ride-sharing performance, evaluating new strategies, etc. By bridging the gap between theoretical research and practical implementation, HRSim serves as a versatile testbed for policymakers and transportation network companies to optimize ride-sharing systems for efficiency and sustainability.

eess.SY↗