Paper Info
Title
Inferring Unmet Human Mobility Demand With Multi-Source Urban Data
Abstract
As the sharing economy has been increasing dramatically in the world, the mobile-hailed ridesharing companies like Uber and Lyft in the US, Didi Chuxing in China has begun to challenge traditional public transportation providers such as bus, subway or taxis. Ridesharing companies have shown their ability to provide the mobility services where public transit has failed. The human mobility demand that cannot be satisfied by traditional transportation modes (unmet human mobility demand) can be served by the ridesharing companies. In this paper, we provide a 'hydrological' perspective for inferring unmet mobility demand patterns in cities with multi-source urban data. We observe that the unmet human mobility demand is proportional to the met mobility demand by examining the yellow taxi and the Uber data in New York City. Based on this observation, a Single Linear Reservoir (SLR) model has been proposed for modeling unmet human mobility demand from multi-source urban data.
Year
DOI
Venue
2017
10.1007/978-3-319-69781-9_12
WEB AND BIG DATA
Keywords
Field
DocType
Urban human mobility, Spatio-temporal data mining
Demand patterns,Computer science,China,Taxis,Public transport,Sharing economy,Artificial intelligence,Multi-source,Machine learning,Environmental economics
Conference
Volume
ISSN
Citations 
10612
0302-9743
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Kai Zhao1896.70
Xinshi Zheng200.68
Huy T. Vo3103561.10