Uncover Repeated Spatio-temporal Behavioral Patterns. Embedded in GPS-based Taxi Tracking Data

Transcription

1 Uncover Repeated Spatio-temporal Behavioral Patterns Embedded in GPS-based Taxi Tracking Data Yang Xu 1, Shih-Lung Shaw 1 2 *, Jiaoli Chen 1, Qingquan Li 2, Zhixiang Fang 2, Yuguang Li 2 1 Department of Geography, University of Tennessee, Knoxville, TN, USA {yxu30; sshaw; jchen42}@utk.edu 2 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China {qqli; zxfang}@whu.edu.cn; wd_liyuguang@yahoo.com.cn * Corresponding author: sshaw@utk.edu 1. Introduction Global Positioning System (GPS) based vehicle tracking data have been used to derive useful traffic data such as computing travel speed or congestion level (e.g., Herrera et al., 2010; Mohan, 2008) or measuring urban dynamics (e.g., Calabrese et al., 2011; Reades et al., 2007). Vehicle tracking data also have been used to analyze travel activities (e.g., Li et al., 2011; Liu et al., 2010). This study, on the other hand, focuses on identifying repeated spatio-temporal patterns embedded in a GPS-based taxi tracking dataset collected in Wuhan, China. Although taxi trajectories may appear to be chaotic at first glance, there could be important repeated spatio-temporal patterns embedded in taxi tracking data. For example, certain taxi drivers may have preferences of waiting for passengers at particular locations such as airports, train stations, etc. In many developing countries, it also is common to have two work shifts of drivers for one taxi. Identifying repeated spatio-temporal patterns embedded in vehicle tracking data thus can shed light on important travel and activity behavioral patterns. This study uses a taxi tracking dataset collected in Wuhan, China as a case study to identify repeated spatio-temporal behavioral patterns among the taxi drivers. The main objective of this study is to develop a systematic method that can facilitate uncovering repeated behavioral patterns in a large tracking dataset. This method can be adapted for studies using other types of tracking data such as cell phone tracking data of individual trajectories or online tracking data of individual web browsing histories. 2. Taxi tracking dataset used in this study Wuhan is a major city located in central China with about ten million people. We obtained a GPS tracking dataset of 10,801 taxis in Wuhan for the week of March 8-14, We first preprocessed the GPS tracking dataset to remove data noises and to match the GPS locations to the street network. Each taxi s GPS trajectories during the study week are broken into a series of individual trips based on the Service Status code of 0/1 indicating if a trip carries passengers or not. Each trip then is stored as one record with the following data: 1

2 - Taxi_ID: unique taxi ID - Date: date of the GPS tracking trip - Trip_ID: unique trip ID - O_Lat/ O_Lng/ O_Time: Latitude/ Longitude/ Time of the origin of a trip - O_RoadID: unique road segment ID on which the origin of a trip falls - D_Lat/D_Lng/D_Time: Latitude/Longitude/Time of the destination of a trip - D_RoadID: unique road segment ID on which the destination of a trip falls - Service_Status: binary value of 0/1 indicating if a trip carries passengers or not - Road_IDs: IDs of road segments that consist of a trip Figure 1 shows the organization of GPS tracking dataset used in this study. Each day in the study week includes data for all taxis. Each taxi in turn has records for individual trips, and each trip record is composed of the data fields described above. Date Day 1 Day 2 TaxiID:11201 Trip 1 Trip 2 Trip ID Day 3 TaxiID:11202 GPS dataset Day 4 Day 5 Day 6 ServiceStatus Road_IDs Day 7 TaxiID:25048 Figure 1.Organization of GPS tracking dataset used in this study. 3. Identification of repeated spatio-temporal behavioral patterns In order to identify occurrences of repeated behavioral patterns of the taxi drivers, we first divide each day into time segments and detect if any repeated behavior pattern occurs in particular time segments. Since each taxi trip lasts for a time duration, we implement a moving time window approach to capture varying trip durations. For instance, a taxi driver may have a habit of taking a break at a particular location between 13:45 and 14:15. If we simply divide a day into 24 one-hour time segments, the activity from 13:45 to 14:15 will cross the boundary between two time segments and split this activity into two parts. This creates a problem of identifying these repeated behavioral patterns. We develop a one-hour moving time window with a half-hour overlap between two consecutive time windows to generate a total of 47 time segments in a day (see Figure 2). It generates 47 rather than 48 time segments because we start at [0:00-1:00] and end at [23:00 24:00]. This moving time window method works well with our taxi tracking dataset because most taxi trips have a duration of less than one hour. 2

3 Figure 2. Time segments derived from a moving time window method. We then examine how frequently a taxi visited the same road segment repeatedly in each of the 47 time segments during the study week. Based on the departure time (O_Time) and arrival time (D_Time) of each taxi trip and the road segments (Road_IDs) along a trip, we can estimate the time that a driver stayed on each road segment, so that trips that cross two time windows or trips running beyond one hour can be handled properly. For a given time segment, if a taxi passed a particular road segment during that time segment, this road segment receives a count of 1 regardless the number of times this road segment was traversed by the particular taxi during this time segment on the given day. All other road segments that were not used by the taxi would receive a count of 0. When we add up the counts by individual taxis and by individual road segments for all seven days in the study week, we can derive a table indicating the frequency that each taxi visited each road segment during each time segment. For example, Table 1 shows that taxi #11201 visited road segment # in time segment 33 (i.e., [16:00 17:00] ) on four days during the study week, while the same taxi visited road segment # in the same time segment all seven days in the study week. Table 1. An example of a table for a taxi # Taxi_ID Road_ID Time Segment Frequency(F) Figure 3 uses an example of taxi #11207 in time segment 14 to show how the spatial 5 3

4 distribution of road segments converges as the frequency increases from 2 to 7. This example illustrates the effectiveness of our method to identify repeated spatio-temporal patterns. (a)f>=2 (b)f>=3 (c)f>=4 (d)f>=5 (e)f>=6 (f)f=7 Figure 3. Selected road segments with different frequency. 4. Temporal and spatial distributions of repeated taxi behavioral patterns Figure 4 shows the analysis result of temporal distribution of repeated behaviors with two obvious peaks in a day. This temporal distribution suggests that a large proportion of taxi drivers visited the same road segments at least five days in the study week during the morning and the afternoon peak periods. In the meantime, it is common knowledge among the residents of Wuhan that it is very difficult to find available taxis during the morning and afternoon peak hours due to not only higher demands in those hours but also many taxis in Wuhan switch drivers for their respective 12-hour work shift during these peak hours. The findings from this case study shed additional light on the specific temporal distribution of this transportation issue in Wuhan. 4

5 Figure 4. Temporal distribution of taxi repeated behavioral patterns. We next examine the locations and spatial extents of those road segments that are visited by a taxi at least five days in the study week. A modified DBSCAN method is used to cluster the locations of start and end nodes of those repeatedly visited road segments based on a search distance of 1km.The analysis results indicate that 83% of the taxis have only one spatial cluster, 14% have two spatial clusters, and only 3% of the taxis have more than two spatial clusters. Figure 5 illustrates the cumulative frequency distribution of all spatial clusters based on the radius of these spatial clusters. Figure 6, on the other hand, displays the spatial distribution of those repeated visited road segments by frequency. The higher the frequency is, the more the road segment is visited repeatedly by the taxi drivers. This map helps us gain a better understanding of the spatial patterns of repeated behavior. Our GIS implementation also allows researchers to combine both spatial and temporal aspects in order to interactively explore the hidden repeated spatio-temporal behaviors among the taxi drivers. 5

6 Figure 5. Cumulative distribution of the cluster radius. Figure 6.Spatial distribution of repeatedly visited road segments by frequency. 6

7 5. Summary This study develops a novel approach to identifying repeated spatio-temporal behavioral patterns using a taxi tracking dataset. The analysis results are very encouraging and reveal some detailed spatio-temporal patterns that can help researchers gain better understanding of repeated behavioral patterns in a space-and-time context. References J. C. Herrera, D. B. Work, R. Herring, X. J. Ban, Q. Jacobson and A. M. Bayen, 2010, Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment. Transportation Research Part C: Emerging Technologies, 18(4): P. Mohan, V. N. Padmanabhan and R. Ramjee, 2008, Nericell: rich monitoring of road and traffic conditions using mobile smartphones. Proceedings of the 6th ACM conference on Embedded network sensor systems, ACM F. Calabrese, M. Colonna, P. Lovisolo, D. Parata and C. Ratti, 2011, Real-time urban monitoring using cell phones: A case study in Rome. Intelligent Transportation Systems, IEEE Transactions on(99): J. Reades, F. Calabrese, A. Sevtsuk and C. Ratti, 2007, Cellular census: Explorations in urban data collection. IEEE Pervasive Computing, 6: B. Li, D. Zhang, L. Sun, C. Chen, S. Li, G. Qi and Q. Yang, 2011, Hunting or waiting? discovering passenger-finding strategies from a large-scale real-world taxi dataset IEEE International Conference on Pervasive Computing and Communications Workshops, IEEE L. Liu, C. Andris and C. Ratti, 2010, Uncovering cabdrivers' behavior patterns from their digital traces. Computers, Environment and Urban Systems, 34(6):