arXiv subjects

Eleftheria Kontou

Publications and source records attributed to Eleftheria Kontou.

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↗

arXiv2022

Evacuation Route Planning for Alternative Fuel Vehicles

As the number of adopted alternative fuel vehicles increases, it is crucial for communities (especially those that are susceptible to hazards) to make evacuation plans that account for such vehicles refueling needs. During emergencies that require preemptive evacuation planning, travelers using alternative fuel vehicles are vulnerable when evacuation routes do not provide access to refueling stations on their way to shelters. In this paper, we formulate and solve a novel seamless evacuation route plan problem, by designing $k$-minimum spanning trees with hop constraints that capture the refueling needs of each $k \in K$ vehicle fuel type on their way to reach a shelter. We develop a branch-and-price algorithm based on a matheuristic column generation approach to solve the evacuation problem. We apply the proposed framework to the Sioux Falls transportation network with existing infrastructure deployment and present numerical experiments. Specifically, we discuss the evacuation travel and refueling times under scenarios of various alternative fuel vehicles driving ranges. Our findings show that the characteristics of each vehicle fuel type, like driving range and the refueling infrastructure topology, play a pivotal role in determining evacuation route plans. This means that an evacuation route could prove unique to a single vehicle fuel type, while being infeasible to the others. Finally, we observe that the driving range constraints could force evacuee vehicles to detour to meet their refueling needs before reaching safety and increase the total evacuation time by 7.32 % in one of the evaluated scenarios.

math.OC↗

arXiv2021

Associating Ridesourcing with Road Safety Outcomes: Insights from Austin Texas

Improving road safety and setting targets for reducing traffic-related crashes and deaths are highlighted as part of the United Nation's sustainable development goals and vision zero efforts around the globe. The advent of transportation network companies, such as ridesourcing, expands mobility options in cities and may impact road safety outcomes. In this study, we analyze the effects of ridesourcing use on road crashes, injuries, fatalities, and driving while intoxicated (DWI) offenses in Travis County Texas. Our approach leverages real-time ridesourcing volume to explain variation in road safety outcomes. Spatial panel data models with fixed effects are deployed to examine whether the use of ridesourcing is significantly associated with road crashes and other safety metrics. Our results suggest that for a 10% increase in ridesourcing trips, we expect a 0.12% decrease in road crashes (p<0.05), a 0.25% decrease in road injuries (p<0.001), and a 0.36% decrease in DWI offenses (p<0.0001) in Travis County. Ridesourcing use is not associated with road fatalities at a 0.05 significance level. This study augments existing work because it moves beyond binary indicators of ridesourcing presence or absence and analyzes patterns within an urbanized area rather than metropolitan-level variation. Contributions include developing a data-rich approach for assessing the impacts of ridesourcing use on our transportation system's safety, which may serve as a template for future analyses of other US cities. Our findings provide feedback to policymakers by clarifying associations between ridesourcing use and traffic safety, while helping identify sets of actions to achieve safer and more efficient shared mobility systems.

econ.GN↗