Exploring demand patterns of a ride-sourcing service using spatial and temporal clustering

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Abstract

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.

Original languageEnglish
Title of host publicationMT-ITS 2019 - 6th International Conference on Models and Technologies for Intelligent Transportation Systems
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)9781538694848
DOIs
Publication statusPublished - 2019
Event6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019 - Krakow, Poland
Duration: 5 Jun 20197 Jun 2019

Conference

Conference6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019
CountryPoland
CityKrakow
Period5/06/197/06/19

Keywords

  • demand patterns
  • ride-sourcing
  • spatial clustering
  • taxi data
  • temporal clustering

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    Liu, T. L. K., Krishnakumari, P., & Cats, O. (2019). Exploring demand patterns of a ride-sourcing service using spatial and temporal clustering. In MT-ITS 2019 - 6th International Conference on Models and Technologies for Intelligent Transportation Systems [8883312] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/MTITS.2019.8883312