arXiv subjects

Jaskaranveer Kaur

Publications and source records attributed to Jaskaranveer Kaur.

3 recordsLinked to original sources

arXiv2026

Mean-Field Control of Adherence in Participation-Coupled Vehicle Rebalancing Systems

Human driver participation is a critical source of uncertainty in Mobility-on-Demand (MoD) rebalancing. Drivers follow platform recommendations probabilistically, and their willingness to comply evolves with experienced outcomes. This creates a closed-loop feedback in which stronger recommendations increase participation, participation increases congestion, congestion lowers allocation success, and realized allocations update adherence beliefs. We propose a microscopic stochastic model that couples (i) belief-driven participation, (ii) Poisson demand, (iii) uniform matching, and (iv) Beta--Bernoulli belief updates. Under a large-population closure, we derive a deterministic mean-field recursion for the population adherence state under platform actuation. For i.i.d. Poisson demand and constant recommendation intensity, we prove global well-posedness and invariance of the recursion, establish equilibrium existence, provide uniqueness conditions, and show global convergence in the regime where platform recommendations are no weaker than baseline participation. We then define steady-state adherence and throughput, characterize the induced performance frontier, and show that adherence and throughput cannot, in general, be simultaneously maximized under uniform time-invariant actuation. This yields a throughput-maximization problem with an adherence floor. Exploiting the monotone frontier structure, we show the optimal uniform time-invariant policy is the maximal feasible recommendation intensity and provide an efficient bisection-based algorithm.

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arXiv2025

Maximal Compatibility Matching for Preference-Aware Ride-Hailing Systems

This paper presents the Maximal Compatibility Matching (MCM) framework, a novel assignment strategy for ride-hailing systems that explicitly incorporates passenger comfort into the matching process. Traditional assignment methods prioritize spatial efficiency, but often overlook behavioral alignment between passengers and drivers, which can significantly impact user satisfaction. MCM addresses this gap by learning personalized passenger comfort zones using gradient-boosted decision tree classifiers trained on labeled ride data, and by modeling driver behavior through empirical operating profiles constructed from time-series driving features. Compatibility between a passenger and a driver is computed as the closed-form volume of intersection between their respective feature-space regions. These compatibility scores are integrated into a utility-based matching algorithm that balances comfort and proximity through a tunable trade-off parameter. We validate the framework using a Unity-based driving simulator with real-time passenger feedback, demonstrating that MCM enables more personalized and socially acceptable matchings while maintaining high levels of operational performance.

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arXiv2024

Integrated User Matching and Pricing in Round-Trip Car-Sharing

Traditional round-trip car rental systems mandate users to return vehicles to their point of origin, limiting the system adaptability to meet diverse mobility demands. This constraint often leads to fleet under-utilization and incurs high parking costs for idle vehicles. To address this inefficiency, we propose a N-user matching algorithm which is designed to facilitate one-way trips within the round-trip rental framework. Our algorithm addresses the joint problem of optimal pricing and user matching through a Two-Stage Integer Linear Programming (ILP)-based formulation. In the first stage, optimal rental prices are determined by setting a risk factor that governs the likelihood of matching a set of N-user. The second stage involves maximizing expected profit through a novel ILP-based user-matching formulation. Testing our algorithm on real-world scenarios demonstrates an approximate 35\% increase in demand fulfillment. Additionally, we assess the model robustness under uncertainty by varying factors such as the risk factor (probability of user ride acceptance at the offered price), cost factor (rental cost-to-fare ratio), and maximum chain length.

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