Open AccessA Unified Framework for Predicting KPIs of On-Demand Transport Services
Author(s) -
Jihong Guan,
Weili Wang,
Wengen Li,
Shuigeng Zhou
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2846550
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Having a better understanding of the key performance indicators (KPIs, e.g., demand and unmet demand) in the next time slot (e.g., next hour) is important for on-demand transport services, such as Uber and DiDi, to improve the service quality. In addition to the spatio-temporal dynamics, KPIs of on-demand transport services are also affected by many exogenous factors from different domains, e.g., the traffic condition from transportation domain and the weather condition from meteorology domain. Therefore, this paper proposes a unified framework to fuse the data collected from different domains to predict multiple KPIs for on-demand transport services. As demonstrated by the experiments, the proposed framework can capture both long-term regularity and short-term dynamics, thus achieving a better performance than the existing solutions in predicting KPIs.
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