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

Conference paper (2019)

Authors

T.L.K. Liu Transport and Planning - CEG
P.K. Krishnakumari Transport and Planning - CEG
O. Cats Transport and Planning - CEG
Research Group
Transport and Planning (CEG) (TU Delft)
Copyright: © 2019 T.L.K. Liu, P.K. Krishnakumari, O. Cats
DOI
help_outline
https://doi.org/10.1109/MTITS.2019.8883312
Ride-sourcing Temporal clustering Demand patterns Spatial clustering Taxi data
More Info
expand_more

Published Date

2019

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Civil Engineering & Geosciences

Department

Transport and Planning

Research Group

Transport and Planning

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.

Files

08883312.pdf
(.pdf | 2.56 Mb)