DS595/CS525: Urban Network Analysis --Urban Mobility Prof. Yanhua Li

Transcription

1 Welcome to DS595/CS525: Urban Network Analysis --Urban Mobility Prof. Yanhua Li Time: 6:00pm 8:50pm Wednesday Location: Fuller 320 Spring 2017

2 2 Team assignment Finalized. (Great!) Guest Speaker 2/22 A few things Affect a bit of presentation schedule Merge two classes DS595 and CS525 in mywpi Project 1 Proposal due today A reminder of Project 1 follow-up. Class website - project page for the timeline

3 ? 3 How to write good paper reviews/critiques Summarize the work (80 points) What problem? Why the problem is important? Which method is proposed? How is the work evaluated? Correctly summarize all these in your own words, you get 80 points Critiques/comments (20 points) Quality Matters Some critiques, something in the paper is wrong? Some future work to make the work more solid? Some changes to the method to enable better performances?

4 ? 4 Weka Online Website 3 Volunteer Groups Class 1,2 - Getting started with Weka, Evaluation Class 3 - Simple classifiers WEEK 4 Class 4 - More classifiers WEEK 5 Class 5 - Putting it all together WEEK MINUTES EACH SESSION at the beginning of the class Briefly introduction of techniques you learned Post a question to the audience.

5 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

6 Big Trajectory Data in Urban Networks Taxi GPS Trajectory Mobile User Trajectory Urban roving sensors deliver big trajectory data. Reveal moving patterns and urban issues. Challenge How to manage and utilize the big trajectory data to improve people s life quality?

7 7 Reachability Query 10 mile 10 mile 15 min Free Space Road Network ST Reachability State of Art Our Proposed Query

8 8 Terminology Trajectory: a sequence of spatio-temporal points. (traj_id, latitude, longitude, timestamp, travel speed, direction, occupancy). Trajectory reachability: Given S, T, L, r, tell if r are reachable from S in [T,T+L] Reachable area: Given S, T, L, find all {r} that are reachable in [T,T+L] Prob-reachable area: Given S, T, L, find all {r} that are reachable in [T,T+L] at least prob% of days in the past

9 9 Spatio-Temporal Reachability Query Definition: To find the reachable area in a spatial network from a location in a given time period. Example: Start from my home at 8:00AM, where I can reach in 30 minutes with more than 80% confidence ( a ) ( b )

10 ? 10 Real-World Problems 1 pm 6 pm 1 pm 6 pm ( a ) Location-based Recommendation ( b ) Location-based Advertising 6 pm 1 pm 1 pm 6 pm ( c ) Business Coverage Analysis ( d ) Emergency Dispatching Analysis

11 11 Exhaustive Search Start from the querying location S and time T, to search the neighboring road segments throughout the whole road network. Long responding time for large trajectory dataset In Nov 2014, Shenzhen, China; Taxi GPS Bus GPS 1.58 TB 22,083 taxis 1.34 TB 8,427 buses Query: Reachable region from a user-specified location and time to travel 1 hour, with a confidence of 80% or more. 10 minutes to get the query answers!

12 12 Query Processing Framework 1. Preprocessing 2. Index Construction 3. Query Processing Input Road Networks Clean Road Segments Map- Matching ST-Index Lat Time Lng Time r 3 L r 3 r 3 r 2 r 2 r 2 r n r 1 r n r 1 r n r 1 Con-Index S, T, L, Prob Prob Trajectory Database Mapped Trajectory

13 13 Preprocessing Map Matching Map trajectory point to real road network Road Re-segmentation Partition the original road segments based on the given spatial granularity, e.g., <=500 meters

14 14 Service Providing Improve urban planning, Ease Traffic Congestion, Save Energy, Reduce Air Pollution,... The Environment Urban Data Analytics Win Data Mining, Machine Learning, Visualization Urban Computing Urban Data Management Spatio-temporal index, streaming, trajectory, and graph data management,... People Win Win Cities OS Human mobility Traffic Air Quality Meteorolo gy Social Media Energy Urban Sensing & Data Acquisition Road Networks POIs Participatory Sensing, Crowd Sensing, Mobile Sensing Tackle the Big challenges in Big cities using Big data! Urban Computing: concepts, methodologies, and applications. Zheng, Y., et al. ACM transactions on Intelligent Systems and Technology.

15 15 Indexing Structure ST-Index B-Tree R-Tree

16 16 Indexing Structure Connection Index

17 17 Query Processing Single Location Find maximum bounding region Trace back search r *

18 18 Query Processing Multiple Locations Find unified maximum bounding region Trace back search r 2 r 2 r 4 r 3 r 1 r 1

19 Evaluation Dataset: 60GB real taxi mobility data in Shenzhen Statistics City Size City Taxi Population 21,358 Baseline Algorithm Exhaustive search Evaluation metric 400 square miles Duration November 2014 # of spatio-temporal points 400 million (407, 040, 083) Running time (s) Total Length of Road Segments (km) Value

20 20 Evaluation 90% 50% Running time: 50-90% reduction over ES Road Segment Length: Increases as L increases

21 Evaluation (T) 21

22 Evaluation (Prob) 22

23 23 s-query vs m-query 3 times Running time: 3 times reduction over s-query Running time: Constant vs linear

24 Evaluation (m-query) 24

25 Summary Approximate query processing Single trajectory aggregate query via Random Index Sampling (RIS) Concurrent trajectory aggregate queries via Concurrent Random Index Sampling (CRIS)

26 26 Dimensions of Query Spatio-Temporal Reachability Queries have different types regarding different data inputs Data Static Streaming Mode Local Distributed Spatial Single location Multiple location Union Join Sequential

27 27 Dimensions of Query A B C Union Join Sequential

28 28 Dimensions of Query Streaming Data Real-Time Problem Distributed Mode Large-Scale Database Concurrent Queries 6 pm 1 pm Emergency Dispatching Analysis

29 Thank you!

30 30 Predictive Query Transitive Reachability AàB, BàC AàC Bayesian Network The probability that an object travel from segment A to segment B based on speed information

31 Imagine This You get an offer from company X and you need to find where to live House A, 8 miles to company, 15 min. House B,10 miles to company, 10 min. Which one?