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Traffic Database System for Performance Tuning and Data Visualization

This project focuses on developing a traffic database system to reduce query response time and visualize traffic data for performance tuning and analysis. The system aims to improve transportation management and planning by providing efficient query results and intuitive data visualization.

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Traffic Database System for Performance Tuning and Data Visualization

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  1. Mapcube Shashi Shekhar Computer Science Department, AHPCRC University of Minnesota shekhar@cs.umn.edu (612) 624-8307 http://www.cs.umn.edu/~shekhar http://www.cs.umn.edu/research/shashi-group/

  2. Biography Highlights • 7/01-now : Professor, Dept. of CS, U. of MN • 12/89-6/01 : Asst./Asso. Prof. of CS, U of MN • Ph.D. (CS), M.B.A., U of California, Berkeley (1989) • Member: CTS(since 1990),Army Center, CURA • Author: “A Tour of Spatial Database” (Prentice Hall, 2002) and 100+ papers in Journals, Conferences • Editor: Geo-Information(2002-onwards), IEEE Transactions on Knowledge and Data Eng.(96-00) • Program chair: ACM Intl Conf. on GIS (1996) • Tech. Advisor: UNDP(1997-98), ESRI(1995), MNDOT GuideStar(1993-95 on Genesis Travlink) • Grants: FHWA, MNDOT, NASA, ARMY, NSF, ... • Supervised 7+ Ph.D Thesis (placed at Oracle, IBM TJ Watson Research Center etc.), 30+ MS. Thesis

  3. Research Interests • Knowledge and Data Engineering • Spatial Database Management • Spatial Data Mining(SDM) and Visualization • Geographic Information System • Application Domains : Transportation, Climatology, Defence Computations

  4. Spatial Data Mining, SDBMS • Historical Examples • London Cholera (1854) • Dental health in Colorado • Current Examples • Environmental justice • Crime mapping - hot spots (NIJ) • Cancer clusters (CDC) • Habitat location prediction (Ecology) • Site selection, assest tracking, spatial outliers

  5. Project: Traffic Database System Sponsor and time-period: MNDOT, 1998-1999 Students: Xinhong Tan, Anuradha Thota Contributions to Transportation Domain Reduce response of queries from hours to minutes Performance tuning (table design, index selection) Contributions to Computer Science GUI design for extracting relevant summaries Evaluate technologies with large dataset

  6. Map of Station in Mpls

  7. Gui Design • http://www.cs.umn.edu/research/shashi-group/TMC/html/gui.html

  8. Existing Table Fivemin Detector ReadDate Time Dayofweek Volume Occupancy Validity Speed

  9. Benchmark Queries 1. Get 5-min Volume, occupancy for detector ID = 10 on Oct. 1st, 1997 from 7am to 8am 2. Get 5-min volume, Occupancy for detector ‘5’ on Aug1 1997. 3. Get 5-min volume, Occupancy for detector ‘5’ on Aug1 1997 from 6.30am to 7.30am. 4. Get average 5-min volume, occupancy, for Monday in Aug1997 between 8.00 - 8.05,8.05-8.10 …… 9.00 5. Get maximum volume, Occupancy for detector ‘5’ on Aug1 1997 from 6am to 7am 6. Get the average of AM rushhour hourly volume for a set of stations on highway I35W-NB with milepoint between 0.0 and 4.0 from Oct. 1st, 1997 to Oct. 5th , 1997 Conclusion

  10. Examples of the Query • Example1: • Query description: • Get 5-min Volume, occupancy for detector ID = 10 on Oct. 1st, 1997 from 7am to 8am • SQL statement: • SELECT readdate, time, xtan.fivemin.detector, occupancy, volume • FROM xtan.fivemin, xtan.datetime • WHERE ReadDate = to_date('01-OCT-97', 'DD-MON-YYYY') • AND time BETWEEN '0705' AND '0800' • AND xtan.fivemin.Detector = '10' • AND xtan.fivemin.

  11. Examples of the Query • Query result 1:

  12. Examples of the Query • Example2: • Query description: • Get the average of AM rushhour hourly volume for a set of stations on highway I35W-NB with milepoint between 0.0 and 4.0 from Oct. 1st, 1997 to Oct. 5th , 1997 • SQL statement: • SELECT hour, xtan.v_stat_hour.station, avg(volume) • FROM tan.v_stat_hour, xtan.statrdwy • WHERE ReadDate BETWEEN to_date('01-OCT-97','DD-MON-YYYY') AND to_date('05-OCT-97','DD-MON-YYYY') • AND hour BETWEEN '06' AND '09' • AND statrdwy.route = 'I35W-I' • AND statrdwy.mp >= 0.0 • AND statrdwy.mp <= 4.0 • AND xtan.v_stat_hour.station = statrdwy.station • GROUP BY xtan.v_stat_hour.station, hour

  13. Examples of the Query • Query result 2:

  14. Project: Traffic Data Visualization Sponsor and time-period: USDOT/ITS Inst., 2000-2001 Students: Alan Liu, CT Lu Contributions to Transportation Domain Allow intuitive browsing of loop detector data Highlight patterns in data for further study Contributions to Computer Science Mapcube - Organize visualization using a dimension lattice Visual data mining, e.g. for clustering

  15. Motivation for Traffic Visualization • Transportation Manager • How the freeway system performed yesterday? • Which locations are worst performers? • Traffic Engineering • Where are the congestion (in time and space)? • Which of these recurrent congestion? • Which loop detection are not working properly? • How congestion start and spread? • Traveler, Commuter • What is the travel time on a route? • Will I make to destination in time for a meeting? • Where are the incident and events? • Planner and Research • How much can information technique to reduce congestion? • What is an appropriate ramp meter strategy given specific evolution of congestion phenomenon?

  16. Dimensions • Available • TTD : Time of Day • TDW : Day of Week • TMY : Month of Year • S : Station, Highway, All Stations • Others • Scale, Weather, Seasons, Event types, …

  17. Comparison with IWEDA • Summary of IWEDA Weather Visualizations • Dimension = system of components, time slot, space • User chooses a system component and a timeslot • A cell in the matrix of systems x timeslots • Select a component from the system • User gets a weather map • It is querying a time slice • Possibilities with mapcube • Other visualizations are facilitated • Changes in weather for a day for a location • Changes in weather for a day for a given route • … • Possibilities with Spatial Data Mining • Co-location of micro phenomena with terrain types • Spatial outliers or discontinuities • Hotspots, e.g., tornado alley

  18. Mapcube : Which Subset of Dimensions ? TTDTDWTMYS TTDTDWS TTDTDW TDWS STTD S TTD TDW Next Project

  19. Data Fusion levels and Mapcube • Different Sub-cubes help with different data fusion levels • Level 0: Single Sensor • Local weather as a function of time • Level 1: Correlating Multiple Sensors • Map of spatial variation in weather • Space-time plot for a route for a day • Level 2: Interpret, Aggregate • Detect spatial discontinuities, spatial outliers • Group sensors with similar weather measurements • Group timeslots with similar weather measurements

  20. Singleton Subset : TTD Configuration: • X-axis: time of day; Y-axis: Volume • For station sid 138, sid 139, sid 140, on 1/12/1997 Trends: • Station sid 139: rush hour all day long • Station sid 139 is an S-outlier

  21. Singleton Subset: TDW • Configuration: • X axis: Day of week; Y axis: Avg. volume. • For stations 4, 8, 577 • Avg. volume for Jan 1997 • Friday is the busiest day of week • Tuesday is the second busiest day of week Trends:

  22. Singleton Subset: S Configuration: • X-axis: I-35W South; Y-axis: Avg. traffic volume • Avg. traffic volume for January 1997 Trends?: • High avg. traffic volume from Franklin Ave to Nicollet Ave • Two outliers: 35W/26S(sid 576) and 35W/TH55S(sid 585)

  23. Dimension Pair: TTD-TDW Configuration: • Evening rush hour broader than morning rush hour • Rush hour starts early on Friday. • Wednesday - narrower evening rush hour • X-axis: time of date; Y-axis: day of Week • f(x,y): Avg. volume over all stations for Jan 1997, except Jan 1, 1997 Trends:

  24. Dimension Pair: S-TTD Configuration: • X-axis: Time of Day • Y-axis: Route • f(x,y): Avg. volume over all stations for 1/15, 1997 Trends: • 3-Cluster • North section:Evening rush hour • Downtown area: All day rush hour • South section:Morning rush hour • S-Outliers • station ranked 9th • Time: 2:35pm • Missing Data

  25. Dimension Pair: TDW-S • X-axis: stations; Y-axis: day of week • f(x,y): Avg. volume over all stations for Jan-Mar 1997 • Busiest segment of I-35 SW is b/w Downtown MPLS & I-62 • Saturday has more traffic than Sunday • Outliers – Route branch Configuration: Trends:

  26. Post Processing of cluster patterns • Clustering Based Classification: • Class 1: Stations with Morning Rush Hour • Class 2: Stations Evening Rush Hour • Class 3: Stations with Morning + Evening Rush Hour

  27. Triplet: TTDTDWS: Compare Traffic Videos Configuration: Traffic volume on Jan 9 (Th) and 10 (F), 1997 • Evening rush hour starts earlier on Friday • Congested segments: I-35W (downtown Mpls – I-62); I-94 (Mpls – St. Paul); I-494 ( intersection I-35W) Trends:

  28. Size 4 Subset: TTDTDWTMYS(Album) • Outer: X-axis (month of year); Y-axis (route) • Inner: X-axis (time of day); Y-axis (day of week) Configuration: Trends: • Morning rush hour: I-94 East longer than I-35 W North • Evening rush hour: I-35W North longer than I-94 East • Evening rush hour on I-94 East: Jan longer than Feb

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