Paper Info
Title
A Unified Framework for Predicting KPIs of On-Demand Transport Services.
Abstract
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.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2846550
IEEE ACCESS
Keywords
Field
DocType
On-demand transport service,key performance indicator,cross-domain data fusion,feature selection
Data integration,Performance indicator,On demand,Service quality,Computer science,Operations research,Public transport,Feature extraction,Fuse (electrical),Traffic conditions,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Jihong Guan165781.13
Weili Wang2206.67
Wengen Li363.87
Shuigeng Zhou42089207.00