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.

In Korea, drivers should come to a complete halt and proceed cautiously when encountering a school bus displaying its red warning lights and other safety features, a requirement that is often disregarded in practice. The reason for this might stem from a lack of awareness about the law, and we set out to investigate whether an innovative lighting system employing road projections or VMS could encourage compliance. We found that while 63% of drivers in surveys indicated they would correctly stop when approaching a stopped school bus, in driving experiments, we found that only 18% of drivers did. Our study also uncovered a knowledge gap, with just 53% to 60% of respondents correctly answering basic about the purpose of existing lighting and laws related to school buses. With on-road experiments, when we introduced road projection systems for enhanced non-connected vehicle-to-vehicle (V2V) communication, we found that understanding would increase and compliance could increase by up to 77% (from surveys) and 93% (in road tests); these findings underscore the potential of road projections or potentially VMS as effective V2V tools for enhancing road safety in proximity to school buses.