arXiv subjects

Wencheng Bao

Publications and source records attributed to Wencheng Bao.

4 recordsLinked to original sources

arXiv2026

Computing Absorbing-Frequency Centrality: Complexity, Estimators, and Scalable Algorithms for Stochastic Networks

In a stochastic network, where edges fail and weights may vary across realizations, the node of highest betweenness is itself random. Absorbing-frequency centrality (AFC) scores each node by how often it is reported as the node of highest betweenness: the reported betweenness maximizer traces an absorbing Markov chain, and AFC is the normalized expected pre-absorption occupancy. Computing it exactly is intractable: we prove that evaluating even a single transition probability of the AFC chain is #P-hard, by a reduction from two-terminal network reliability, and that the expected absorption time and AFC scores inherit this hardness. We develop a matrix estimator that builds the kernel row-wise and solves one linear system, and a parallelizable episode estimator over absorption trajectories that is strongly consistent and asymptotically normal, with finite-sample guarantees tied to the empirical error decay. A computational study reports scalability on Erdős-Rényi, Watts-Strogatz, and Barabási-Albert graphs, and a monitor placement experiment in which AFC's advantage over pooled counting generally grows with reliability heterogeneity.

cs.SI↗

arXiv2026

Risk-Averse Stochastic User Equilibrium on Uncertain Transportation Networks

Extreme weather events, like flooding, disrupt urban transportation networks by reducing speeds and capacities, and by closing roadways. These hazards create regime-dependent uncertainty in link performance and travel-time distribution tails, challenging conventional traffic assignment that relies on the expectation of cost or mean excess of cost summation. This study develops a risk- and ambiguity-aware traffic assignment framework coupling stochastic supply driven by hazard impacts, endogenous route choice with choice set truncation, and tail-risk management within a tractable convex truncated stochastic user equilibrium (TSUE) formulation. Travelers' perceived costs use a normalized mean-CVaR certainty equivalent encoding tail sensitivity into two interpretable parameters ($α$ and $λ$) while preserving convexity. We propose two complementary treatments. TSUE-Stochastic Programming (TSUE-SP) optimizes a nominal risk-aware TSUE balancing average performance and adverse-tail outcomes. TSUE-Distributionally Robust Optimization (TSUE-DRO) protects against calibration error and distributional misspecification by incorporating robustness over a $1$-Wasserstein ambiguity set, and when appropriate, over structured regime-dependent sets for piecewise-stationary hazards (non-stationary distribution case). Duality yields a scenario-based second-order cone program solved via Benders cuts. On a stylized grid network representing downtown Chicago, western corridor traffic increases $67.9\%$ with TSUE-SP and $100.9\%$ with TSUE-DRO relative to a baseline not impacted by the hazard. The formulations redistribute flows without large-scale rerouting, illustrating how tail weighting and distributional ambiguity fine-tune rather than subvert equilibrium choices in hazard-prone networks.

math.OC↗

arXiv2024

Application of Kalman Filter in Stochastic Differential Equations

In areas such as finance, engineering, and science, we often face situations that change quickly and unpredictably. These situations are tough to handle and require special tools and methods capable of understanding and predicting what might happen next. Stochastic Differential Equations (SDEs) are renowned for modeling and analyzing real-world dynamical systems. However, obtaining the parameters, boundary conditions, and closed-form solutions of SDEs can often be challenging. In this paper, we will discuss the application of Kalman filtering theory to SDEs, including Extended Kalman filtering and Particle Extended Kalman filtering. We will explore how to fit existing SDE systems through filtering and track the original SDEs by fitting the obtained closed-form solutions. This approach aims to gather more information about these SDEs, which could be used in various ways, such as incorporating them into parameters of data-based SDE models.

eess.SY↗

arXiv2023

Machine learning assist nyc subway navigation safer and faster

Mainstream navigation software, like Google and Apple Maps, often lacks the ability to provide routes prioritizing safety. However, safety remains a paramount concern for many. Our aim is to strike a balance between safety and efficiency. To achieve this, we're devising an Integer Programming model that takes into account both the shortest path and the safest route. We will harness machine learning to derive safety coefficients, employing methodologies such as generalized linear models, linear regression, and recurrent neural networks. Our evaluation will be based on the Root Mean Square Error (RMSE) across various subway stations, helping us identify the most accurate model for safety coefficient estimation. Furthermore, we'll conduct a comprehensive review of different shortest-path algorithms, assessing them based on time complexity and real-world data to determine their appropriateness in merging both safety and time efficiency.

cs.SI↗