%0 Conference Paper %1 Zheng:2016:UTP:2983323.2983357 %A Zheng, Zimu %A Wang, Dan %A Pei, Jian %A Yuan, Yi %A Fan, Cheng %A Xiao, Fu %B Proceedings of the 25th ACM International on Conference on Information and Knowledge Management %C New York, NY, USA %D 2016 %I ACM %K mobility prediction traffic urban %P 1363--1372 %R 10.1145/2983323.2983357 %T Urban Traffic Prediction Through the Second Use of Inexpensive Big Data from Buildings %U http://doi.acm.org/10.1145/2983323.2983357 %X Traffic prediction, particularly in urban regions, is an important application of tremendous practical value. In this paper, we report a novel and interesting case study of urban traffic prediction in Central, Hong Kong, one of the densest urban areas in the world. The novelty of our study is that we make good second use of inexpensive big data collected from the Hong Kong International Commerce Centre (ICC), a 118-story building in Hong Kong where more than 10,000 people work. As building environment data are much cheaper to obtain than traffic data, we demonstrate that it is highly effective to estimate building occupancy information using building environment data, and then to further use the information on occupancy to provide traffic predictions in the proximate area. Scientifically, we investigate how and to what extent building data can complement traffic data in predicting traffic. In general, this study sheds new light on the development of accurate data mining applications through the second use of inexpensive big data. %@ 978-1-4503-4073-1

@inproceedings{Zheng:2016:UTP:2983323.2983357, abstract = {Traffic prediction, particularly in urban regions, is an important application of tremendous practical value. In this paper, we report a novel and interesting case study of urban traffic prediction in Central, Hong Kong, one of the densest urban areas in the world. The novelty of our study is that we make good second use of inexpensive big data collected from the Hong Kong International Commerce Centre (ICC), a 118-story building in Hong Kong where more than 10,000 people work. As building environment data are much cheaper to obtain than traffic data, we demonstrate that it is highly effective to estimate building occupancy information using building environment data, and then to further use the information on occupancy to provide traffic predictions in the proximate area. Scientifically, we investigate how and to what extent building data can complement traffic data in predicting traffic. In general, this study sheds new light on the development of accurate data mining applications through the second use of inexpensive big data.}, acmid = {2983357}, added-at = {2016-12-07T16:16:36.000+0100}, address = {New York, NY, USA}, author = {Zheng, Zimu and Wang, Dan and Pei, Jian and Yuan, Yi and Fan, Cheng and Xiao, Fu}, biburl = {https://www.bibsonomy.org/bibtex/231716324470272b9e516e56269713a89/ntempelmeier}, booktitle = {Proceedings of the 25th ACM International on Conference on Information and Knowledge Management}, description = {Urban Traffic Prediction through the Second Use of Inexpensive Big Data from Buildings}, doi = {10.1145/2983323.2983357}, interhash = {96490810870f9dc48ba6bdc7ee4628f2}, intrahash = {31716324470272b9e516e56269713a89}, isbn = {978-1-4503-4073-1}, keywords = {mobility prediction traffic urban}, location = {Indianapolis, Indiana, USA}, numpages = {10}, pages = {1363--1372}, publisher = {ACM}, series = {CIKM '16}, timestamp = {2016-12-07T16:16:36.000+0100}, title = {Urban Traffic Prediction Through the Second Use of Inexpensive Big Data from Buildings}, url = {http://doi.acm.org/10.1145/2983323.2983357}, year = 2016 }