Demand-responsive transport (DRT) services (collective on-demand services, such as shared ridesourcing and microtransit) offer a collective flexible travel alternative that can potentially complement fixed transit (FT). The combination of an on-demand and line-based service holds the promise of improved mobility and increased service coverage. However, to date, it remains unknown whether DRT services deliver these much anticipated improvements. This study presents an assessment framework to evaluate the performance of DRT and related changes in accessibility, and performs an empirical analysis for a recently introduced DRT service in the Netherlands. The framework includes a performance benchmark between DRT and FT based on the computation of generalized journey times of the DRT rides and the FT alternatives, and can help identify whether DRT is used as a complement or a substitute for FT. The framework covers the spatial and temporal dimensions, and the explicit consideration of rejected trips is an integral part of the evaluation. Results suggest large accessibility improvements for DRT users, especially for some underserved origin–destination pairs.@en

On-demand transport has become a common mode of transport with ride-sourcing companies like Uber, Lyft and Didi transforming the mobility market. Recurrent patterns in prevailing demand patterns can be used by service providers to better anticipate future demand distribution and thus support demand-Anticipatory fleet management strategies. To this end, we propose three steps for extracting such demand patterns from travel requests: (1) constructing the origin-destination zones by spatial clustering, (2) composing the hourly and daily origin-destination matrix, and; (3) temporal clustering to extract the dynamic demand patterns. We demonstrate the three step approach on the open-source Didi ride-sourcing data. The data consists of travel requests data for November 2016 from Chengdu, China amounting to approximately 6 million rides. The analysis reveals pronounced and recurrent and thus predictable daily and weekly patterns with distinct spatial properties pertaining to ride-sourcing production and attraction characteristics.@en