QZTool—Automatically Generated Origin-Destination Matrices from Cell Phone Trajectories
Models describing human travel patterns are indispensable to plan and operate road, rail and public transportation networks. For most kind of analyses in the field of transportation planning, there is a need for origin-destination (OD) matrices, which specify the travel demands between the origin and destination zones in the network. The preparation of OD matrices is traditionally a time consuming and cumbersome task. The presented system, QZTool, reduces the necessary effort as it is capable of generating OD matrices automatically. These matrices are produced starting from floating phone data (FPD) as raw input. This raw input is processed by a Hadoop-based big data system. A graphical user interface allows for an easy usage and hides the complexity from the operator. For evaluation, we compare a FDP-based OD matrix to an OD matrix created by a traffic demand model. Results show that both matrices agree to a high degree, indicating that FPD-based OD matrices can be used to create new, or to validate or amend existing OD matrices.
KeywordsTransportation planning Travel demand Origin-destination (OD) matrices Floating phone data (FPD) Big data Hadoop
The Know-Center is funded within the Austrian COMET Program—Competence Centers for Excellent Technologies—under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry of Economy, Family and Youth and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG.
- 1.Ben-Akiva, M., Lerman, S.: Discrete Choice Analysis. MIT Press, Cambridge (1989)Google Scholar
- 2.Bera, S., Rao, K.V.K.: Estimation of origin-destination matrix from traffic counts: the state of the art. Eur. Transp./Trasporti Europei. 49, 2–23 (2011)Google Scholar
- 5.Aydos, C., Hengst, B., Uther, W.: Kalman Filter Process Models for Urban Vehicle Tracking. In: 12th International IEEE Conference on Intelligent Transportation Systems (ITSC ‘09), pp. 1–8. IEEE Press, New York (2009)Google Scholar
- 8.Janecek, A., Hummel, K.A., Valerio, D., Ricciato, F., Hlavacs, H.: Cellular data meet vehicular traffic theory: location area updates and cell transitions for travel time estimation. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 361–370. ACM, New York (2012)Google Scholar
- 14.Horn, C., Klampfl, S., Cik., M., Reiter, T.: Detecting outliers in cell phone data: correcting trajectories to improve traffic modelling. Transp Res Rec.: J. Transp. Res. Board (2014)Google Scholar
- 15.Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th International Conference on Advances in Geographic Information Systems (SIGSPATIAL), pp. 352–361. ACM, New York (2009)Google Scholar
- 16.Schulze, G., Horn, C., Kern, R.: Map-matching cell phone trajectories of low spatial and temporal accuracy. In: IEEE 18th International Conference on Intelligent Transportation Systems, pp. 2707–2714. IEEE Press, New York (2015)Google Scholar
- 17.Stenneth, L., Wolfson, O., Yu, P.S., Xu B.: Transportation mode detection using mobile phones and GIS information. In: Proceedings of the 19th ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL), pp. 54–63. ACM, New York (2011)Google Scholar
- 18.Wang, H., Calabrese, F., Di Lorenzo, G., Ratti C.: Transportation Mode Inference from Anonymized and Aggregated Mobile Phone Call Detail Records. In: 13th International IEEE Conference on Intelligent Transportation Systems, pp. 318–323. IEEE Press, New York (2010)Google Scholar