Welcome to DS595/CS525: Urban Network Analysis -- Urban Mobility Prof. Yanhua Li Time: 6:00pm –8:50pm Wednesday Location: Fuller 320 Spring 2017
2 A few things • Team assignment • Finalized. (Great!) • Guest Speaker 2/22 • 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 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 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 6 10-15 MINUTES EACH SESSION at the beginning of the class Briefly introduction of techniques you learned Post a question to the audience.
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
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 Reachability Query 10 mile 15 min 10 mile Free Space Road Network ST Reachability Our Proposed Query State of Art
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 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 Real-World Problems 1 pm 1 pm 6 pm 6 pm ( a ) Location-based Recommendation ( b ) Location-based Advertising 1 pm 1 pm 6 pm 6 pm ( c ) Business Coverage Analysis ( d ) Emergency Dispatching Analysis
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 1.58 TB 22,083 taxis Bus GPS 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 Query Processing Framework 2. Index Construction 3. Query Processing 1. Preprocessing S T-Index Input S, T, 𝑀 , 𝑄𝑠𝑝𝑐 Clean r 2 r 1 r n r 3 Lat Lng Road r 2 r 1 Segments Road r n r 3 Networks Time r 2 r 1 r n r 3 Map- Con-Index 𝑀 Time Matching 𝑄𝑠𝑝𝑐 Trajectory Mapped Database Trajectory
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 Service Providing Improve urban planning, Ease Traffic Congestion, Save Energy, Reduce The Environment Air Pollution, ... Win Urban Data Analytics Data Mining, Machine Learning, Visualization Urban Computing Urban Data Management People Win Win Cities OS Spatio-temporal index, streaming, trajectory, and graph data management,... Human Meteorolo Road Air Social Energy Networks POIs Traffic mobility Quality gy Media Tackle the Big Urban Sensing & Data Acquisition challenges Participatory Sensing, Crowd Sensing, Mobile Sensing in Big cities using Big data! Urban Computing: concepts, methodologies, and applications . Zheng, Y., et al. ACM transactions on Intelligent Systems and Technology .
15 Indexing Structure • ST-Index B-Tree R-Tree
16 Indexing Structure • Connection Index
17 Query Processing • Single Location • Find maximum bounding region • Trace back search r *
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
Evaluation • Dataset: 60GB real taxi mobility data in Shenzhen Statistics Value 400 square miles City Size 21,358 City Taxi Population November 2014 Duration 400 million (407, 040, 083) # of spatio-temporal points • Baseline Algorithm – Exhaustive search • Evaluation metric – Running time (s) – Total Length of Road Segments (km)
20 Evaluation 90% 50% Running time: Road Segment Length: 50-90% reduction over ES Increases as L increases
21 Evaluation ( T )
22 Evaluation ( Prob )
23 s-query vs m-query 3 times Running time: Running time: 3 times reduction over s-query Constant vs linear
24 Evaluation ( m-query )
Summary • Approximate query processing – Single trajectory aggregate query • via Random Index Sampling (RIS) – Concurrent trajectory aggregate queries • via Concurrent Random Index Sampling (CRIS)
26 Dimensions of Query • Spatio-Temporal Reachability Queries have different types regarding different data inputs Data Static Streaming Mode Local Distributed Union Single Multiple Spatial Join location location Sequential
27 Dimensions of Query A B C Union Join Sequential
28 Dimensions of Query • Streaming Data • Real-Time Problem 1 pm 6 pm • Distributed Mode • Large-Scale Database • Concurrent Queries Emergency Dispatching Analysis
Thank you!
30 Predictive Query • Transitive Reachability • A à B, B à C A à C • Bayesian Network • The probability that an object travel from segment A to segment B based on speed information
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?
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