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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Afian, A., Odoni, A., Rus, D.: Inferring unmet demand from taxi probe data. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 861–868, September 2015
Anwar, A., Volkov, M., Rus, D.: Changinow: a mobile application for efficient taxi allocation at airports. In: 2013 IEEE 16th International Conference on Intelligent Transportation Systems, pp. 694–701, October 2013
Chow, V.T., Maidment, D.R., Mays, L.W.: Applied Hydrology (1988)
Freire, J., Bessa, A., Chirigati, F., Vo, H.T., Zhao, K.: Exploring what not to clean in urban data: a study using new york city taxi trips. IEEE Data Eng. Bull. 39(2), 63–77 (2016)
Miranda, F., Doraiswamy, H., Lage, M., Zhao, K., Gonçalves, B., Wilson, L., Hsieh, M., Silva, C.T.: Urban pulse: capturing the rhythm of cities. IEEE Trans. Vis. Comput. Graph. 23(1), 791–800 (2017)
New York Open Data Set. http://www1.nyc.gov/site/planning/data-maps/open-data.page
New York Taxi Data Set. http://www.nyc.gov/html/tlc
Rao, W., Zhao, K., Zhans, Y., Hui, P., Tarkoma, S.: Maximizing timely content advertising in DTNs. In: 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012, Seoul, Korea (South), 18–21 June 2012, pp. 254–262 (2012)
Zhang, D., Zhao, J., Zhang, F., He, T.: comobile: real-time human mobility modeling at urban scale using multi-view learning. In: SIGSPATIAL, Bellevue, WA, USA, 3–6 November, pp. 40:1–40:10 (2015)
Zhao, K., Khryashchev, D., Freire, J., Silva, C.T., Vo, H.T.: Predicting taxi demand at high spatial resolution: approaching the limit of predictability. In: 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, 5–8 December 2016, pp. 833–842 (2016)
Zhao, K., Musolesi, M., Hui, P., Rao, W., Tarkoma, S.: Explaining the power-law distribution of human mobility through transportation modality decomposition. Nat. Sci. Rep. 5(9136) (2015)
Zhao, K., Tarkoma, S., Liu, S., Vo, H.T.: Urban human mobility data mining: an overview. In: 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, 5–8 December 2016, pp. 1911–1920 (2016)
Acknowledgment
The authors thank: the New York City TLC for providing the data used in this paper. This work was supported in part by a CUNY IRG Award and the NYU Center for Urban Science and Progress.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhao, K., Zheng, X., Vo, H. (2017). Inferring Unmet Human Mobility Demand with Multi-source Urban Data. In: Song, S., Renz, M., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10612. Springer, Cham. https://doi.org/10.1007/978-3-319-69781-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-69781-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69780-2
Online ISBN: 978-3-319-69781-9
eBook Packages: Computer ScienceComputer Science (R0)