We describe analysis of flows in the CDC2013 bicycles data set

Andrienko, G.

Andrienko, N.

English

City Research Online

Andrienko, G. & Andrienko, N. (2013). Spatio-temporal analysis of flows in CDC 2013 data. Paper presented at the Understanding Urban Cycling: A Data Challenge, 14 Nov 2013, Leuven, Belgium.City Research OnlineOriginal citation: Andrienko, G. & Andrienko, N. (2013). Spatio-temporal analysis of flows in CDC 2013 data. Paper presented at the Understanding Urban Cycling: A Data Challenge, 14 Nov 2013, Leuven, Belgium.Permanent City Research Online URL: http://openaccess.city.ac.uk/2905/ Copyright & reuseCity  University  London has developed City  Research Online  so that  its  users  may access the research outputs of City University London's staff. Copyright © and Moral Rights for this paper are retained by the individual author(s) and/ or other copyright holders.  All material in City Research Online is checked for eligibility for copyright before being made available in the live archive. URLs from City Research Online may be freely distributed and linked to from other web pages. Versions of researchThe version in City Research Online may differ from the final published version. Users are advised to check the Permanent City Research Online URL above for the status of the paper.EnquiriesIf you have any enquiries about any aspect of City Research Online, or if you wish to make contact  with the author(s) of this paper, please email the team at publications@city.ac.uk.1 Data preprocessing In this study, we analyzed the CDC2013 bicycles data set1 aiming at characterizing streets according to their suitability for bicycle traffic. The available data included 2,403,904 time-stamped GPS positions of 78 distinct travellers recorded from the 28th of June, 2011 till the 1st of December, 2011. Only two positions have been recorded before the 7th of October, 2011; they have been discarded.  We performed pre-processing consisting of (1) eliminating duplicates, that is, records having the same person ID and time stamp, (2) eliminating stationary points characterized by speed lower than 2km/h, (3) dividing the tracks into daily trajectories by breaking them at 3AM, (4) dividing the daily trajectories by stops for at least 30 minutes or spatial gaps of more than 1km between positions, (5) eliminating short mostly stationary trajectories consisting of less than 5 points or having duration below 5 minutes or localized within an area with less than 50 metres radius. As a result, we obtained 1,584 trajectories representing trips.  Figure 1: Systematically wrong positions in the data.                                                                    1 https://sites.google.com/site/cdc2013workshop/ By visual inspection of maps (Figure 1), we found that many trajectories consist of systematically wrong positions. We partly improved the data by removing trajectory segments characterized by very high speed, tortuosity, or sinuosity, as well as stops expressed in the data as random changes of positions around one place. Despite our efforts, the data remained far from perfect (probably, due to misconfiguration of hardware and/or software during the data collection campaign), prohibiting many kinds of potentially interesting analyses. Moreover, we found that even major streets were used by quite small number of bicyclists, and that observations of most individuals were shorter than one week. These facts further restrict possible types of analysis [1].  2 Data processing For data aggregation, we produced a Voronoi tessellation of the territory reflecting the spatial distribution of the recorded positions [2]. The method extracted characteristic points of trajectories and clustered them in space, using a chosen radius of 100m for splitting dense clusters covering large areas.  Figure 2: Voronoi tessellation of the territory.   Spatio-temporal analysis of flows in CDC 2013 data  Gennady Andrienko Fraunhofer Institute IAIS Schloss Birlinghoven 53757 Sankt Augustin Germany gennady.andrienko@  iais.fraunhofer.de Natalia Andrienko Fraunhofer Institute IAIS Schloss Birlinghoven 53757 Sankt Augustin Germany natalia.andrienko@  iais.fraunhofer.de   Abstract We describe analysis of flows in the CDC2013 bicycles data set. Keywords: movement data, flows, clustering. AGILE 2013 – Leuven, May 14-17, 2013  This procedure resulted in 14,033 polygons (Figure 2). It can be observed that the tessellation correctly reflects the street network.  3 Data analysis Based on the computed tessellation, we calculated flows between the polygons (Figure 3) and hourly time series (Figure 4) of the transitions. Aggregated time series plot shows statistical distributions (deciles) of flow magnitude values over time (Figure 4 bottom).  Figure 3: Map fragment presents a part of 26,094 connections; the maximal flow magnitude is 127.   Figure 4: Hourly time series of flow magnitudes (top) and their scalable representation (bottom).   Time series have been used for clustering the flows by similarity by kMeans. Spatial footprints and temporal profiles of 5 major clusters are shown in Figure 5. Figure 5: Spatial and temporal profiles of 5 clusters.     Figure 6: Cluster 5: mostly mid-day flows   AGILE 2013 – Leuven, May 14-17, 2013  Figure 7: Cluster 3: morning activities.     Figure 8: Cluster 1: evening activities.   Figure 9: Cluster 2: late afternoon activities.     Figure 10: Cluster 4: late evening activities.   AGILE 2013 – Leuven, May 14-17, 2013  One can observe that different clusters are characterized by different temporal dynamics, see Figures 6-10. Hence, different roads have different temporal signatures, and, respectively, are used for different purposes.  4 Conclusions Despite the large amount of the collected data, the high uncertainty of positions and small amount of data corresponding to street segments and individuals makes many types of potentially interesting analyses impossible. References [1] G.Andrienko, N.Andrienko, P.Bar, D.Keim, S.Wrobel. V Visual Analytics of Movement. Springer, 2013.  [2] N.Andrienko, G.Andrienko. Spatial Generalization and Aggregation of Massive Movement Data. IEEE Transactions on Visualization and Computer Graphics, 2011, v.17 (2), pp.205-219.     

Spatio-temporal analysis of flows in CDC 2013 data

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