Version 2004 of the Fixed-Guideway Transit Network is a network database of the nation's fixed-guideway transit systems. The data set covers systems in cities defined as FTA's universe of cities and includes heavy rail, light rail, monorail, cable car, inclined plane, and automated guideway. Source: Bureau of Transportation Statistics URL: http://www.bts.gov/publications/national_transportation_atlas_database/2006/

Context

This GPS trajectory dataset was collected in (Microsoft Research) Geolife project by 178 users in a period of over four years (from April 2007 to October 2011). A GPS trajectory of this dataset is represented by a sequence of time-stamped points, each of which contains the information of latitude, longitude and altitude. This dataset contains 17,621 trajectories with a total distance of 1,251,654 kilometers and a total duration of 48,203 hours. These trajectories were recorded by different GPS loggers and GPS-phones, and have a variety of sampling rates. 91 percent of the trajectories are logged in a dense representation, e.g. every 1~5 seconds or every 5~10 meters per point.

Content

This dataset recoded a broad range of users’ outdoor movements, including not only life routines like go home and go to work but also some entertainments and sports activities, such as shopping, sightseeing, dining, hiking, and cycling.

Data Format - Trajectory file Every single folder of this dataset stores a user’s GPS log files, which were converted to PLT format. Each PLT file contains a single trajectory and is named by its starting time. To avoid potential confusion of time zone, we use GMT in the date/time property of each point, which is different from our previous release. - PLT format: Line 1…6 are useless in this dataset, and can be ignored. Points are described in following lines, one for each line. Field 1: Latitude in decimal degrees. Field 2: Longitude in decimal degrees. Field 3: All set to 0 for this dataset. Field 4: Altitude in feet (-777 if not valid). Field 5: Date - number of days (with fractional part) that have passed since 12/30/1899. Field 6: Date as a string. Field 7: Time as a string. Note that field 5 and field 6&7 represent the same date/time in this dataset. You may use either of them. Example: 39.906631,116.385564,0,492,40097.5864583333,2009-10-11,14:04:30 39.906554,116.385625,0,492,40097.5865162037,2009-10-11,14:04:35 - Transportation mode labels Possible transportation modes are: walk, bike, bus, car, subway, train, airplane, boat, run and motorcycle. Again, we have converted the date/time of all labels to GMT, even though most of them were created in China. Example: Start Time End TimeTransportation Mode 2008/04/02 11:24:21 2008/04/02 11:50:45 bus 2008/04/03 01:07:03 2008/04/03 11:31:55 train 2008/04/03 11:32:24 2008/04/03 11:46:14 walk 2008/04/03 11:47:14 2008/04/03 11:55:07 car

First, you can regard the label of both taxi and car as driving although we set them with different labels for future usage. Second, a user could label the transportation mode of a light rail as train while others may use subway as the label. Actually, no trajectory can be recorded in an underground subway system since a GPS logger cannot receive any signal there. In Beijing, the light rails and subway systems are seamlessly connected, e.g., line 13 (a light rail) is connected with line 10 and line 2, which are subway systems. Sometimes, a line (like line 5) is comprised of partial subways and partial light rails. So, users may have a variety of understanding in their transportation modes. You can differentiate the real train trajectories (connecting two cities) from the light rail trajectory (generating in a city) according to their distances. Or, just treat them the same.

More: User Guide: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/User20Guide-1.2.pdf

Citation

Please cite the following papers when using this GPS dataset. [1] Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of International conference on World Wild Web (WWW 2009), Madrid Spain. ACM Press: 791-800.

[2] Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, Wei-Ying Ma. Understanding Mobility Based on GPS Data. In Proceedings of ACM conference on Ubiquitous Computing (UbiComp 2008), Seoul, Korea. ACM Press: 312-321. [3] Yu Zheng, Xing Xie, Wei-Ying Ma, GeoLife: A Collaborative Social Networking Service among User, location and trajectory. Invited paper, in IEEE Data Engineering Bulletin. 33, 2, 2010, pp. 32-40.

Inspiration

This trajectory dataset can be used in many research fields, such as mobility pattern mining, user activity recognition, location-based social networks, location privacy, and location recommendation.

Dataset 1: Beijing Subway Smart Card Data (April 2010) This dataset contains anonymized smart card transaction records from the Beijing subway system, collected during one week in April 2010. It consists of 239,728 records, each corresponding to a unique trip taken by passengers using a smart card. Each record includes a unique card identifier, check-in and check-out timestamps, and station locations. While these smart cards are purchased anonymously and do not store personal identity information, the presence of unique identifiers allows for the extraction of travel patterns, potentially leading to individual re-identification. This characteristic is particularly relevant to privacy considerations under the General Data Protection Regulation (GDPR), as pseudonymization alone is not sufficient for privacy protection. To mitigate re-identification risks, we applied detection k-anonymity, a privacy-preserving method that ensures individual trip data is generalized within groups of similar travel patterns. The accompanying Python implementation of detection k-anonymity demonstrates how this technique can be applied to large-scale mobility data, preserving both analytical utility and privacy. This dataset and method were used as part of our research on privacy-preserving mobility analytics. Dataset 2: Lelystad Public Transport Smart Card Data (January–April 2023) This dataset comprises 230,228 smart card travel records collected from the public bus network of Lelystad, the Netherlands, covering the period from 08 January 2023 to 01 April 2023. The Lelystad bus network consists of 98 transport nodes connected by 194 edges, primarily bidirectional routes. Each trip record contains information on the origin and destination bus stops, timestamps, bus lines used, and a uniquely encrypted smart card identifier per trip. Given the complexity of real-world public transportation networks, this dataset was used to evaluate advanced data processing techniques, particularly for handling complex queries related to passenger flows, route optimization, and privacy considerations. The dataset was analyzed using privacy-preserving methods to ensure compliance with data protection standards. The associated Python implementation demonstrates how detection k-anonymity can be applied to smart card data to prevent individual re-identification while maintaining the dataset's analytical utility. This dataset supports research on privacy-aware public transportation analytics and network modeling.