Abstract
Intelligent transportation systems (ITSs) became an essential tool for a broad range of transportation applications. Traffic flow visualization is an important problem in ITS. The visualized results can be used to support ITSs to plan operation and manage revenue. In this paper, we aim to visualize the daily floating taxis by presenting a novel figure using taxi trajectory data and weather information. Many visualization platforms feature a online-offline phase, in which taxi GPS trajectory data is processed by two phases. This approach incurs high costs though, since trajectory data is huge generated by taxis every second continually. To support the frequent trajectories, we present an analysis tool for mining frequent trajectories of taxis (FTMTool). It allows us to find the driver’s routes by collecting input on the most frequent roads, thereby achieving a set of high quality routes. The tool also supports the task statistic in selecting the specific roads. We demonstrate the usefulness of our tool using real data from New York city.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Castro, P.S., Zhang, D., Chen, C., Li, S., Pan, G.: From taxi gps traces to social and community dynamics: A survey. ACM Comput. Surv. (CSUR) 46(2), 17 (2013)
Chu, D., Sheets, D.A., Zhao, Y., Wu, Y., Yang, J., Zheng, M., Chen, G.: Visualizing hidden themes of taxi movement with semantic transformation. In: 2014 IEEE Pacific Visualization Symposium, pp. 137–144. IEEE (2014)
Commission, N.T.L.: TLC Trip Record Data. http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml
Ferreira, N., Poco, J., Vo, H.T., Freire, J., Silva, C.T.: Visual exploration of big spatio-temporal urban data: A study of new york city taxi trips. IEEE Trans. Vis. Comput. Graph. 19(12), 2149–2158 (2013)
Itoh, M., Yokoyama, D., Toyoda, M., Kitsuregawa, M.: Visual interface for exploring caution spots from vehicle recorder big data. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 776–784. IEEE (2015)
Wang, F., Chen, W., Wu, F., Zhao, Y., Hong, H., Gu, T., Wang, L., Liang, R., Bao, H.: A visual reasoning approach for data-driven transport assessment on urban roads. In: 2014 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 103–112. IEEE (2014)
Wang, Z., Ye, T., Lu, M., Yuan, X., Qu, H., Yuan, J., Wu, Q.: Visual exploration of sparse traffic trajectory data. IEEE Trans. Vis. Comput. Graph. 20(12), 1813–1822 (2014)
Wei, L.Y., Zheng, Y., Peng, W.C.: Constructing popular routes from uncertain trajectories. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 195–203. ACM (2012)
Acknowledgment
The authors thank the Taxi & Limousine Commission of New York City for providing the data used in this paper. This work was supported in part by the Natural Science Foundation of China under Grant 61502069, 61300087 by the Natural Science Foundation of Liaoning under Grant 2015020003, by the Fundamental Research Funds for the Central Universities under Grant DUT15QY40.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Xu, X., Xu, Z., Zhao, X. (2016). Traffic Flow Visualization Using Taxi GPS Data. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-49586-6_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49585-9
Online ISBN: 978-3-319-49586-6
eBook Packages: Computer ScienceComputer Science (R0)