Exploring Demand Patterns of a Ride-Sourcing Service using Spatial and Temporal Clustering

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.