SemanticTraj : A New Approach to Interacting with Massive Taxi Trajectories

1. SemanticTraj:A New Approach to Interacting with Massive Taxi Trajectories Shamal AL-Dohuki, Farah Kamw, Ye Zhao, Kent State University, USA Chao Ma, Yingyu Wu, Xinyue Ye, Kent State University, USA Fei Wang, Wei Chen, Zhejiang University, China Jing Yang, UNC Charlotte, USA Xin Li, China Petroleum University

2. Trajectory Data • A variety of trajectory data of moving objects in urban spaces

3. Taxi Trajectory Data • Taxi trajectory data records real-time moving paths. • Sampled as a series of positions associated with vehicle attributes over urban road networks.

4. Interaction with Taxi Trajectory Data • Taxi trajectories are widely used in improving transportation, urban planning, etc. • A set of visual analytics systems have been successfully presented on exploiting the data. • We focus on how people directly interact with the data – store, query and visualize the results. Wang et al Ferreira et al

5. A Usage Scenario • A Museum is planning shuttle bus routes for their customers. The museum clerks want to investigate potential bus stops using historical taxi trajectories. • Where do the visitors usually take taxis to the museum at different times? • They may need to conduct the work over the map by: • select the museum area and show taxi drop-off locations; • show corresponding pick-up points on the map and find hot locations; • find the streets and POIs of these hot locations. • These map operations may not be very friendly for general users • Alternatively, is there an easy way for them to conduct the work?

6. Our Solution • We present SemanticTraj system for users to interact with the data. • Search by semantic keywords to dig up information. • To complete the task in a fast and easy way.

7. Video of used scenario

8. SemanticTraj • A new data query model. • Add full text search functionality to taxi trajectory data. • Use text search engines (e.g., Apache Lucene). • Search massive trajectory data by comprehensive search functions. • A visual analytics system. • Text Labels. • Meta-summary. • Many visual analytics functions.

9. SemanticTraj Framework SemanticTraj framework of processing, searching and visualizing taxi trajectory data

10. Textualization of Taxi Trajectories • We refer to the process of converting an attribute in the raw data to a text term as “textualization”. • Each geographical location of latitude and longitude is mapped to the street name it resides. • Numeric travel speed can also be converted to a descriptive term.

11. Taxi Documents (Trajectory & Trip) • After textualization, taxi documents is created from massive trajectories. • Efficient indexing schemes are created for • Trajectory document: full trajectories. • Trip document: passenger trips.

12. Using Text Search Engine • Use text search engines to manage and search taxi documents. • A specific type of database with benefits: • Flexible and comprehensive query functions. • Boolean, wildcard, fuzzy, proximity and range. • Automatic optimization in inverted indexing, compression, distributed operations for data management and query performance.

13. Example Query Syntax VeryFast Proximity Query: The search engine supports finding words are a within a specific distance away. Ex: Dspeed: “Slow VeryFast”~1 Slow

14. Flexible Search Functions • Users can utilize flexible search functions by combining street/POI names and speed descriptions. • Users can direct input query terms (as in Google), or use a given interface to generate the sentences search functions.

15. Data Exploration Tasks • SemanticTraj now allows users to retrieve taxi trajectories or trips. • passing one or multiple streets in a given time period; • passing one or multiple POIs; • with specific behaviors in travel speed; • More semantic information can be included in the exploration such as: • Human trajectories with human behavior information. • Geo-tagged social media information.

16. Example Data • Trajectory data of Hangzhou, China. • Population of about 2.5 million. • Taxi is one of its major transportation methods. • One month data (Dec. 1-31, 2011). • Acquired by 8,120 taxis. • The raw data size is around 2.5GB per day. • GPS sample points associated with some attributes (ID, speed, time, etc.).

17. Query Performance (Q1) Search trips passing Shangtanglu street; (Q2) Search trips passing Shangtanglu AND Zhonghegaojia streets; (Q3) Search trips passing Shangtanglu OR Zhonghegaojia streets. Query performance on trip documents

18. Visual System • SemanticTraj consists of widgets for query construction and showing a set of coordinated views. • Views are synchronized when users make selection or filtering on each of them. • Text labels and meta-summary are used to display query results with semantic information.

19. System Video

20. More Usage Scenarios • Search by speed description: VeryFast VeryFast in Dspeed • Find very fast traffic locations in Hangzhou, mostly major highways with intermittent slow sections.

21. More Usage Scenarios • Search by: Slow Slow Slow Slow Slow Slow in DSpeed • Find traffic jam locations in Hangzhou

22. More Usage Scenarios • Search by consecutive: Slow VeryFast in DSpeed • Identify taxi drivers’ abnormal behavior, quick acceleration from less than 20 km/h to more than 100 km/h which is not proper in the city.

23. Conclusion • We presented SemanticTraj, anew approach to interacting with massive taxi trajectory data sets. • The experience of searching by text is extended to taxi trajectory data. • The interactions is applied through semantic rich operations. • This scheme can be extended to more semantic-rich moving trajectories especially human trajectories. • Recorded by mobile devices. • Extracted from social media.

24. Thank You!