1. Mining Spatio-Temporal Reachable Regions over Massive Trajectory Data
Guojun Wu#, Yichen Ding#, Yanhua Li#, Jie Bao†, Yu Zheng†, Jun Luo*,$
#Worcester Polytechnic Institute (WPI) †Microsoft Research
*Shenzhen Institutes of Advanced Technologies $Lenovo Research at Hong Kong 1

2. Imagine This… Location-based Advertising
Aim to arrange special activities for business and need to decide spatial regions to distribute coupons
The reachable region is time-varying
6 p.m.
1 p.m.
In today’s talk, I am focusing on managing and analyzing big trajectory data from urban networks.
In an urban network, vehicles, mobile phone users serve as urban roving sensors delivering a big trajectory data.
These trajectories characterize versatile moving patterns, and reflect the urban issues, such as traffic jam, malicious events, etc.
location-based advertising, where some business owner finds out the potential spatial regions to arrange special activities, such as distributing coupons and sales discount
Orange Road means traffic jam, while green road means clear. 2

3. Real-world Problems ( a ) Location-based Recommendation
( b ) Location-based Advertising
(a) A shopping mall distributes its special activities within reachable area. (b) Friends arrange their entertainment activities as much as possible. (c) Two delivery stations manage their affiliated delivery sites and dispatch
( c ) Business Coverage Analysis
( d ) Emergency Response Analysis

4. State-of-the-art works
Related Work
10 miles
15 min
10 miles
ST Reachability
Our work
Free space: circle 10 mile radius
Road Network: road length 10 mile in all directions, with different time periods
ST-Method: spatio-temporal reachable region, consider time aspect, with a given time period
Free Space
Road Network
State-of-the-art works

5. Spatio-Temporal Reachability Query
Definition:
Find the reachable area in a spatial network from a location in a given time period.
Example:
Start from my home at 8:00 AM, where I can reach in 30 minutes
One starting location
Multiple starting locations

6. Naïve Method: Exhaustive Search
Start from the querying location S and time T, then search the neighboring road segments throughout the whole road network.

7. Query Processing Framework

8. Stage 1: Preprocessing Road Re-segmentation Map Matching
Partition the original road segments based on the given spatial granularity
Map Matching
Map trajectory point to real road network
Chop based on intersection
Road segment length differs a lot, 1mile to several feet.
Map points to where it should be, based on distance and direction.

9. Stage 2: Indexing Structure
ST-Index
B-tree temporal
R-tree spatial
Nodetime list (different dates) item in time listdisk page

10. Stage 2: Indexing Structure
Connection Index
Based on speed information from trajectory data, we have two list for one segment ,at least where you can get starting from this segment and at most where you can get.

11. Stage 3: Query Processing
Single Location
Maximum bounding region search
Trace back search
With the ST-Index and Connection Index, now we are able to process queries from the user. In this processing, we employ 2 main techniques. One is the maximum/minimum bounding region search which use our index structures to generate a rough estimation of the upper bound of the reachable region based on the query parameters. The other is the Trace Back Search which uses connection index to refine the region from the rough bounding region. For the single location spatio-temporal reachability queries, we first estimate the maximum bounding region and then trace back search starts from the maximum bounding region to minimum one until satisfy the probability requirement. In this figure, the 2 solid circles indicate the maximum and minimum bounding region, respectively. And the dashed circle represents the prob-reachable region with respect to Probability requirement. As the result, all road segments within this dashed circle is to answer the single location query.
maximum bounding region

12. Stage 3: Query Processing
Multiple Locations
Unified maximum bounding region search
Trace back search r1 r2 r2 r4 r3
Compared with single location, multiple locations query has multiple starting locations. The difference in query processing is to estimate the unified maximum bounding region to skip the overlapping region search. As shown in left figure, road segment r1 and r2 are starting locations. Solid line indicates the outer boundary of the unified maximum bounding region of both r1 and r2 while dashed line indicates the boundary of overlapping region. And r3 is on the boundary of r2 while r4 on the r1. However, r3 is not on the outer-most boundary while r4 is.
The right figure indicates the trace back search which is similar to single location starts from maximum bounding region to inner minimum one. r1
Unified bounding region
Unified maximum bounding region

13. Evaluation
Dataset: 194 GB real human mobility data in a large city in eastern China
Baseline Algorithm
Exhaustive search
Evaluation metric
Processing Time & Reachable Road Length
Statistics
Value
City Size
400 square miles
City Population Size
three million people
Duration
30 days in November, 2014
Number of taxis
21,385 unique taxis
Number of trajectories
400 million (407, 040, 083)

14. Evaluation Effects on query probability Prob
Exhaustive search and duration L = {10,15} min
Processing Time vs. Reachable Road Length
Decrease
50% Time

15. Visualization (a) Prob = 20% (b) Prob = 60% (c) Prob = 80%
(d) Prob = 100%

16. Evaluation Effects on Start Time T Duration L = {5,10} min
Processing Time vs. Reachable Road Length
Same trend => consistent with each other

17. Visualization

18. Evaluation Comparison of s-query and m-query
Duration L = {5,10,…,35}min
Number of locations # = {1,2,…,9}
Up to
70% Time
Up to
90% Time

19. Visualization

20. Summary Spatio-Temporal Reachability Query Index Construction
Spatio-Temporal Index
Connection Index
Query Processing
Single-location ST Reachability Query
via SQMB algorithm + TBS algorithm
Multi-location ST Reachability Query
via MQMB algorithm + TBS algorithm

21. Thank you! Contact: Yanhua Li (yli15@wpi.edu)
Website: