Investigating Factors Affecting Request Matching in Demand-Responsive Transit Service with Different Fleet Sizes Using a Decision Tree Model

Abstract

Demand-responsive transit (DRT) is a flexible transportation service that adapts routes and schedules based on real-time passenger needs, offering greater convenience than traditional fixed-route systems. DRT systems are highly dynamic and complex. Customer requests are often rejected due to operational constraints. Therefore, it is essential to identify and rank the factors that determine request acceptance or rejection. This study develops a Decision Tree Model (DTM) for vehicle dispatching in DRT, using the Korea National University of Transportation (KNUT) Chungju Campus as the study area. Elecle bicycle origin–destination (OD) data were first used to simulate DRT operations, and the resulting outputs were employed to train the DTM to classify passenger requests as “assign” or “reject.” The model considers key factors such as vehicle capacity, access time, Estimated Time of Arrival (ETA), waiting time, detour factor, and egress time. Based on 5-fold cross-validation, the detour factor was identified as the most influential variable across all fleet configurations, with mean importance values of 0.582 ± 0.055, 0.550 ± 0.047, and 0.447 ± 0.073 for the 1-, 2-, and 3-vehicle scenarios, respectively. The model achieved accuracies of 0.73 ± 0.02, 0.82 ± 0.04, and 0.83 ± 0.07, indicating improved performance with increasing fleet size. Error analysis revealed conservative behavior for one vehicle, balanced performance for two, and liberal over-assignment for three vehicles.