arXiv subjects

Tina Radvand

Publications and source records attributed to Tina Radvand.

3 recordsLinked to original sources

arXiv2026

A Quantum Optimization Algorithm for Optimal Electric Vehicle Charging Station Placement for Intercity Trips

Electric vehicles (EVs) play a significant role in enhancing the sustainability of transportation systems. However, their widespread adoption is hindered by inadequate public charging infrastructure for long-distance travel. Identifying optimal charging station locations in large transportation networks is an NP-hard combinatorial optimization problem. This paper applies Grover Adaptive Search (GAS) to improve the efficiency of solving the Charging Station Location Problem (CSLP). The proposed method achieves a quadratic improvement in computational complexity over classical exact methods, such as branch and bound. This paper develops a quantum subroutine that encodes the CSLP constraints by marking feasible solutions with objective value below a given threshold, and integrates this subroutine within the GAS procedure. The approach is demonstrated on a 7-node transportation network in central Illinois, with an analysis of success probability and sensitivity to algorithm parameters.

quant-ph↗

arXiv2026

Hierarchical Pod Routing for Modular Transit Systems

Modular bus systems allow fixed-route transit services to better match vehicle capacity to passenger loads. Operating modular bus systems requires deciding how pods are assigned to scheduled services, repositioned for subsequent assignments, and parked when not in service. A time-space minimum-cost flow formulation provides an exact benchmark for this problem, but it does not scale well to large urban transit systems with many pod-level routing decisions. This paper proposes a Hierarchical Pod Routing (HPR) framework that solves the problem in two stages. The first stage uses a minimum path-cover formulation to assign service trips to pods while minimizing fleet size. The second stage determines how pods are repositioned and parked between consecutive assignments to minimize empty movement and parking costs. The proposed method is evaluated on benchmark instances constructed from New York City MTA GTFS data, which capture realistic bus schedules and passenger-demand patterns. The results show that HPR remains within $8\%$ of the exact minimum-cost flow objective on the largest instances, while achieving more than a four order of magnitude reduction in runtime and an approximately one order of magnitude reduction in memory use. The results also show that HPR distributes service trips more evenly across pods and reduces the frequency of long idle periods between assignments, while the exact minimum-cost flow formulation produces shorter distances between consecutive assignments. Sensitivity analyses further show that HPR maintains consistent performance across a range of operating-cost assumptions. These findings suggest that HPR can provide a scalable and near-optimal planning approach for modular transit systems.

math.OC↗

arXiv2026

Evolution of Lane-Changing Behavior in Mixed Traffic: A Quantum Game Theory Approach

As automated vehicles (AVs) enter mixed traffic, proactively anticipating the evolution of human driving behavior during critical interactions, such as lane changes, is essential. However, classical Evolutionary Game Theory (EGT) fails to capture the complexity of human decision-making during lane changes. Specifically, by strictly assuming independence between agents, classical models calibrated on empirical payoffs predict a convergence to unrealistic full cooperation, contradicting the stable 42% cooperation rate observed in real-world data. To resolve this discrepancy, this study introduces a Quantum Game Theory (QGT) framework. We analyze 7,636 lane-changing interactions from the Waymo Open Motion Dataset (WOMD) to derive empirical payoff matrices via a Quantal Response Equilibrium (QRE) model. Utilizing the Marinatto-Weber (MW) quantization scheme, we introduce an entanglement parameter to mathematically embed latent correlations directly into the payoff structure of a single interaction. Our results identify a human entanglement parameter of $|b|^2_{HDV} \approx 0.52$ that accurately reproduces the observed mixed equilibrium. Furthermore, simulations of three AV deployment strategies (classical, entangled, and inverted) reveal that human adaptation depends critically on the underlying AV algorithm: while cooperative classical AVs maximize system-wide cooperation at high market penetration rates, defective inverted AVs paradoxically yield higher overall cooperation at low penetration rates by prompting more cooperative behaviors from human drivers. Consequently, rather than waiting for large scale deployment to observe these effects, stakeholders can utilize this framework to simulate repeated interactions and proactively anticipate how human driver behavior will evolve in response to specific AV software designs.

cs.GT↗