Demand-adjusted Public Transit System based on a Scheduling Model that considers Repeated Behavior

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  • 繰り返し行動を考慮したスケジューリングモデルによる需要調整型公共交通システム

Abstract

<p>Demand Responsive Transport (DRT) is expected to be an efficient public transportation system in　depopulated and aging mountainous regions. DRT should match the low-frequency travel demand associated with atypical activities, but they are difficult to predict and accommodate. In this study, we　propose a recursive scheduling model that incorporates day-to-day changes in daily activity needs and an algorithm that adjusts reservations based on the model score instead of first in first serve principle. The algorithm was implemented using a path enumeration indexing technique with zero-suppressed binary decision graphs to accommodate the iterative computational costs associated with reservation adjustment.Empirical analysis shows that our proposal effectively predicts and matches infrequent demand and improves overall service levels.</p>

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