arXiv subjects

Riki Kawase

Publications and source records attributed to Riki Kawase.

3 recordsLinked to original sources

arXiv2026

Flexible and Reliable Network Design for Emerging Transportation Services: Multi-stage Stochastic Programming Approach

This paper proposes a general framework for flexible and reliable network design problems (FR-NDPs). The framework enables planners to change infrastructure investments in response to realized uncertainties, while ensuring desired levels of reliability. Motivated by emerging transportation services such as shared autonomous vehicle (SAV) systems, where historical data are scarce and technological developments uncertain, FR-NDPs integrate strategic investment decisions with operational control. We formulate the FR-NDPs as risk-averse multi-stage stochastic problems to be solvable by stochastic dual dynamic programming (SDDP) and establish sufficient conditions under which strategic and operational subproblems converge to the global optimum. We illustrate applications to SAV capacity expansion and integrated SAV-BRT (Bus Rapid Transit) route design, and numerical experiments on a Midtown Manhattan network highlight three key findings: (i) flexibility and reliability act complementarily to hedge against severe scenarios while mitigating the loss of expected performance; (ii) flexibility in investment planning allows dynamic risk hedging, with risk-averse planners reducing early-stage investments to preserve adaptability; and (iii) differences in operational flexibility between SAV and BRT systems are reflected in strategic decisions, with risk-averse planners tending to refrain investment in transport modes with lower operational flexibility.

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arXiv2026

Coarse Preference Reporting in the Bottleneck Model: Approximate Strategyproofness and Efficiency

A central operator schedules each vehicle's passage time through a bottleneck to achieve a dynamic system optimum (DSO). The assignment depends on each vehicle's preferred arrival time, which is private and must be elicited from each vehicle. Mechanisms that elicit exact preferences, such as the Vickrey-Clarke-Groves (VCG) mechanism, can achieve strategyproofness but involve relatively complex rules and a computational burden on the operator. We focus instead on coarse reporting, in which each vehicle selects from a finite menu of time slots of a common width. This discrete interface already structures reservation and appointment systems in practice, including managed lanes for automated vehicles, airport slot allocation, and delivery appointment windows. We design a slot-based DSO mechanism on this coarse interface, in which the operator implements DSO assignment based on the reported slots and charges a capacity shadow price as a toll, and evaluate its performance. We prove that both the worst-case misreporting gain and the expected efficiency loss decrease quadratically in the slot width. The efficiency loss decays in this way under binding capacity, while the worst-case misreporting gain requires an additional condition on the preferred arrival time distribution and the schedule cost function. Analyzing the no-toll case, we find that the misreporting incentive persists, however finely the slots are refined, indicating that the toll also serves to elicit truthful reports. Numerical experiments support these theoretical results and show that they continue to hold in parameter regions outside the sufficient conditions.

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arXiv2024

Multi-stage stochastic linear programming for shared autonomous vehicle system operation and design with on-demand and pre-booked requests

This study presents optimization problems to jointly determine long-term network design, mid-term fleet sizing strategy, and short-term routing and ridesharing matching in shared autonomous vehicle (SAV) systems with pre-booked and on-demand trip requests. Based on the dynamic traffic assignment framework, multi-stage stochastic linear programming is formulated for joint optimization of SAV system design and operations. Leveraging the linearity of the proposed problem, we can tackle the computational complexity due to multiple objectives and dynamic stochasticity through the weighted sum method and stochastic dual dynamic programming (SDDP). Our numerical examples verify that the solution to the proposed problem obtained through SDDP is close enough to the optimal solution. We also demonstrate the effect of introducing pre-booking options on optimized infrastructure planning and fleet sizing strategies. Furthermore, dedicated vehicles to pick-up and drop-off only pre-booked travelers can lead to incentives to reserve in advance instead of on-demand requests with little reduction in system performance.

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