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 language | English |
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Title of host publication | MT-ITS 2019 - 6th International Conference on Models and Technologies for Intelligent Transportation Systems |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Number of pages | 9 |
ISBN (Electronic) | 9781538694848 |
DOIs | |
Publication status | Published - 2019 |
Event | 6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019 - Krakow, Poland Duration: 5 Jun 2019 → 7 Jun 2019 |
Conference
Conference | 6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019 |
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Country/Territory | Poland |
City | Krakow |
Period | 5/06/19 → 7/06/19 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Keywords
- demand patterns
- ride-sourcing
- spatial clustering
- taxi data
- temporal clustering