Assessing spatial characteristics to predict DRT demand in rural Switzerland

Abstract

The niche market segment of demand responsive transport (DRT) services is meant to overcome structural economic problems of currently cost ineffective public transport (PT) services in rural areas. Simulation studies for mainly urban DRT services showed that demand for DRT trips is correlated with spatial characteristics. More knowledge of spatial characteristics of rural settings and their influence on DRT trips is necessary.

In this study, trip data of a rural DRT service called mybuxi is used. Machine learning is applied for a better understanding of spatial characteristics of DRT demand in two different rural settings of the mybuxi service. Here in, the transferability from one mybuxi setting to the other is then tested.

Results show that the number of inhabitants is the most important spatial characteristic for the prediction of DRT demand, followed by the distance to a train station and the presence of a restaurant in a given zone. The quality indicator of PT had low or no predictive power. The study shows that both DRT service areas experienced an increase in accessibility. For future transport planning, the increase in accessibility by DRT services in different rural areas must be taken as a legitimation for these services to be implemented instead of line-bound PT services.