Moving from Data Warehousing to Decision Support. Dr. Bruce Hellinga Feb. 28, 2013

Transcription

1 Moving from Data Warehousing to Decision Support Dr. Bruce Hellinga Feb. 28, 2013

2 Prior to Data Warehousing Problem definition Analysis Characteristics: Limited data spreadsheet tools applicable But small sample limits confidence in results Analysis difficult to reproduce Data not (systematically) archived Ad-hoc quality control

3 Data Warehousing Automated data collection systems AVL/APC Farecard Data Archive (Warehouse) Characteristics: Large quantity of data Increased confidence in results, but Quality control an issue Require more robust analysis tools Analyst needs to pull data/information from warehouse to identify problems and causes

4 Beyond Data Warehousing Wide variety of terms in use: Decision Support System (DSS) Business Intelligence (BI) Expert System Intelligent Agent (IA) All are about automating the transformation of data to information that is useful

5 The Four Pillars Quality Assurance (QA) Measures Of Performance (MOP) Assisted/ Automated Diagnosis Push and Pull

6 Distance (km) P1: Quality Assurance Data Errors (Reality different from recorded data) Route 51 - GRT Actual AINSLIE STREET TERMINAL SMART!CENTRES CAMBRIDGE HOLIDAY INN TERMINAL Scheduled CAMRIDGE CENTRE TERMINAL 1 Schedule deviation Time (minutes)

7 P1: Quality Assurance Data Anomalies Data are accurate but should not be used for calculating performance measures and/or for decision making

8 100% 85% Cumulative Relative Frequency All weather conditions Excluding snow storms Travel Time (minutes)

9 Distance (km) But it is not always easy to determine whether data are erroneous, anomalies, or valid! 40 ixpress 200; Departing 5:15pm 42 minutes CONESTOGA MALL 35 McCORMICK 30 UPTOWN WATERLOO 25 CHARLES TERMINAL 20 FAIRVIEW SMART!CENTRES CAMBRIDGE 5 0 AINSLIE STREET TERMINAL Time (minutes)

10 P2: Measures of Performance With rich, precise data, it is possible to compute many measures of performance, but significant care is required. Example: Schedule adherence MOP = % on-time on-time = does not depart early and does not arrive more than 3 minutes late.

11 Fraction of observations departing more than X seconds early 20% 18% 16% 14% 18.8% # observations (i.e. departures from time points) = 127, % 12% 10% 8% 6% 4% 2% 10.7% 7.7% 5.9% 4.6% 0% > 0s early > 15s early > 30s early > 45s early > 60s early > 75s early

12 P3: Automating Diagnosis Questions: 1. What are the problems? 2. What are the (likely) causes of these problems? 3. How do we solve the problem? Data Warehouse

13 P4: Push and Pull An Intelligent DSS should identify problems and notify the appropriate people at the appropriate time with the appropriate information so they can make the appropriate decision Relevant information (alerts) are pushed out from the system automatically Still retain the ability for an analyst to pull information for custom queries

14 Leveraging the data Transit data is not only valuable for the transit operator and/or transit user!

15 Signalized Intersections Transit Operator Delays at signalized intersections increase operating costs and decrease quality of service Can implement All of these TSP activities or queue require jump data lanes, about but where? existing conditions at the intersections AND Inadequate we LOS don t necessitates know ahead improved of time which signal timings intersections we need data for. Signals Manager Infrastructure Planning Calibrate planning models Identify corridors approaching capacity for which capital expansion will be required

16 Archived AVL/APC Data Stopped delay (d) Each point is from a particular bus on a given run (e.g. route, date, and time of day) Distance (X) Signalized intersection 1

17 d max Stopped delay (d) Can Compute: # obs Mean Stdev Percentiles X P1 X P2 Distance (X)

18 GIS Data Transit routes Signalized intersections

19 A

20 Stopped Delay (s) Stopped Delay (Seconds) Route 200 dn to Total stopped delay per service trip due to traffic signal 0 16:30 16:40 16:50 17:00 17:10 17:20 17:30 17:40 17:50 18:00 Time of Day % 20% 40% 60% Relative Frequency (%)

21 Relative Frequency (%) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% The stopped delays experienced by the transit vehicles are essentially the same as those experienced by general purpose vehicles! Stopped Delay per service trip (Seconds) TM # service trips % trips having to stop Mean of all trips (s) Mean of trips that stop (s) Std of all trips (s) COV 85 th %ile delay (s) Thru %

22 Ranking the Route Segments on the basis of signal delays 52: to 52: to 11: to Watson 51: Depot to And Pinebush 5: And Gateview to 16: HOMER South Rd And Monarch Tr to HOMER 10: DOON to HOMER Village 201: to FISCHER And 10: to 8: Terminal to Park Mall 15: to 3: to Watson 9: to 53: to 23: N/A to Park Mall 110: HOMER South Rd And Monarch Tr to HOMER 64: to 9: N/A to 61: Terminal to HOMER College 21: Mall to Mean Stopped Delay (s)

23 Route + Section Name Mean Delay 90th Percentile Delay (s) Queue (m) Proportion of Trips Experiencing Signal Delay (%) 52: DUNDAS@Easton to HESPELER/WATER@Coronation/Dundas % : KING@River to FAIRWAY@King % : OTTAWA@Alpine to OTTAWA@Homer Watson % : PINEBUSH@Walmart&Home Depot to HESPELER@Eagle And Pinebush % 280 5: ERB@Beechwood And Gateview to FISCHER-HALLMAN@Thorndale % : HOMER WATSON@Doon South Rd And Monarch Tr to HOMER WATSON@Conestoga College % : DOON VILLAGE@Pioneer to HOMER WATSON@ManitouAndDoon Village % : FISCHER-HALLMAN@McGarry to FISCHER HALLMAN@Greenbrook And Hwy 7And8 WB Rmp % : WILSON@Kingsway to FAIRWAY@Wilson % 258 8: Terminal to FAIRWAY@Fairview Park Mall % : LACKNER@Keewatin to VICTORIA@Natchez % 234 3: OTTAWA@Alpine to OTTAWA@Homer Watson % 181 9: NORTHFIELD@Highpoint to NORTHFIELD@Skylark % : FRANKLIN@Clyde to FRANKLIN@Savage % : N/A to FAIRWAY@Fairview Park Mall % : HOMER WATSON@Doon South Rd And Monarch Tr to HOMER WATSON@Conestoga College % : CONCESSION@Bishop to HESPELER@Dunbar % 283 9: N/A to NORTHFIELD@Kraus % : Terminal to HOMER WATSON@Conestoga College % : KING@Conestoga Mall to KING@Northfield % 122 Num Obs

24 R52Up; to

25 Route + Section Name Mean Delay 90th Percentile Delay (s) Queue (m) Proportion of Trips Experiencing Signal Delay (%) 52: DUNDAS@Easton to HESPELER/WATER@Coronation/Dundas % : KING@River to FAIRWAY@King % : OTTAWA@Alpine to OTTAWA@Homer Watson % : PINEBUSH@Walmart&Home Depot to HESPELER@Eagle And Pinebush % 280 5: ERB@Beechwood And Gateview to FISCHER-HALLMAN@Thorndale % : HOMER WATSON@Doon South Rd And Monarch Tr to HOMER WATSON@Conestoga College % : DOON VILLAGE@Pioneer to HOMER WATSON@ManitouAndDoon Village % : FISCHER-HALLMAN@McGarry to FISCHER HALLMAN@Greenbrook And Hwy 7And8 WB Rmp % : WILSON@Kingsway to FAIRWAY@Wilson % 258 8: Terminal to FAIRWAY@Fairview Park Mall % : LACKNER@Keewatin to VICTORIA@Natchez % 234 3: OTTAWA@Alpine to OTTAWA@Homer Watson % 181 9: NORTHFIELD@Highpoint to NORTHFIELD@Skylark % : FRANKLIN@Clyde to FRANKLIN@Savage % : N/A to FAIRWAY@Fairview Park Mall % : HOMER WATSON@Doon South Rd And Monarch Tr to HOMER WATSON@Conestoga College % : CONCESSION@Bishop to HESPELER@Dunbar % 283 9: N/A to NORTHFIELD@Kraus % : Terminal to HOMER WATSON@Conestoga College % : KING@Conestoga Mall to KING@Northfield % 122 Num Obs

26 R11IB; to Watson 189m

27 Key Points Need to get the pillars right! QA; MOP; Diagnosis; Push & Pull Automating Diagnosis is not trivial but big payback Leverage the Archived AVL/APC data Distribute cost of DSS over more entities in organization.

28 Thank you! Web:

29 Photo Sources Slide 2: Slide 3: GRT buses: GPS: Passenger counter: Slide 6: detour sign: snow: Octoberfest parade: temporary bus stop: