1 Bottleneck Identification and Forecasting in Traveler Information Systems Robert L. Bertini, Rafael Fernández-Moctezuma, Huan Li, Jerzy Wieczorek, Portland State University 2008 CITE District and Quad Regional Conference Victoria, B.C., Canada April 30, 2008

2 Bottleneck Identification and Forecasting in Traveler Information Systems AgendaAgenda  What is a bottleneck?  Objectives  Our data archive, PORTAL  Methodology  Comparison of algorithm predictions against ground truth  Historical data visualization  Conclusion and next steps  What is a bottleneck?  Objectives  Our data archive, PORTAL  Methodology  Comparison of algorithm predictions against ground truth  Historical data visualization  Conclusion and next steps

3 Bottleneck Identification and Forecasting in Traveler Information Systems What is a Bottleneck? Queueing upstream Freely-flowing downstream Temporally and spatially variable Queueing upstream Freely-flowing downstream Temporally and spatially variable Queued Unqueued Bottleneck Detectors

4 Bottleneck Identification and Forecasting in Traveler Information Systems Objectives  Create an algorithm to systematically detect freeway bottlenecks and quantify their impacts  Implement this tool in PORTAL, our continuously-updated transportation data archive  Create an algorithm to systematically detect freeway bottlenecks and quantify their impacts  Implement this tool in PORTAL, our continuously-updated transportation data archive

5 Bottleneck Identification and Forecasting in Traveler Information Systems PORTAL and Regional Facts  PORTAL  SQL relational database  200 MB/day, 75 GB per year  Regional Infrastructure  98 CCTV Cameras  138 Ramp Meters  TriMet Automatic Vehicle Location (AVL) System and Bus Dispatch System (BDS)  Extensive fiber optics network  Bi-state region  Regional ITS Committee: TransPort  Data sharing philosophy  Gigabit ethernet/private ITS network  PSU official data archive entity

6 Bottleneck Identification and Forecasting in Traveler Information Systems What’s in the PORTAL Database? Loop Detector Data 20 s count, lane occupancy, speed from 500 detectors (1.2 mi spacing) Incident Data 140,000 since 1999 Weather Data VMS Data 19 VMS since 1999 Data Archive Days Since July 2004 About 300 GB 4.2 Million Detector Intervals Bus Data 1 year stop level data 140,000,000 rows

7 Bottleneck Identification and Forecasting in Traveler Information Systems What’s Behind the Scenes? Database Server PostgreSQL Relational Database Management System (RDBMS) Storage 2 Terabyte Redundant Array of Independent Disks (RAID) Web Interface Development Server CentOS Linux distribution

8 Bottleneck Identification and Forecasting in Traveler Information Systems Contour Plots - Speed

9 Bottleneck Identification and Forecasting in Traveler Information Systems Real-time ?

10 Bottleneck Identification and Forecasting in Traveler Information Systems Our intended goal

11 Bottleneck Identification and Forecasting in Traveler Information Systems Data  I-5 Northbound corridor has best loop detector coverage: 23 detectors over 24 miles, giving 1.1 mi average detector spacing.  Chose 5 representative days for initial testing  Averaged data across all 3 lanes, removed bad detectors, and imputed values for 0 mph speeds  I-5 Northbound corridor has best loop detector coverage: 23 detectors over 24 miles, giving 1.1 mi average detector spacing.  Chose 5 representative days for initial testing  Averaged data across all 3 lanes, removed bad detectors, and imputed values for 0 mph speeds MP 308 MP 284

12 Bottleneck Identification and Forecasting in Traveler Information Systems Our starting point  Based on a California field experiment  Using 5-minute aggregated data, declare a bottleneck between two detectors in a given time period if:  Speed difference across bottleneck is > 20 mph, and  Upstream speed is < 40 mph  “Sustained bottlenecks” filter:  Remove outliers with too few “neighbors”  Fill in any small gaps within bottlenecks  Based on a California field experiment  Using 5-minute aggregated data, declare a bottleneck between two detectors in a given time period if:  Speed difference across bottleneck is > 20 mph, and  Upstream speed is < 40 mph  “Sustained bottlenecks” filter:  Remove outliers with too few “neighbors”  Fill in any small gaps within bottlenecks

13 Bottleneck Identification and Forecasting in Traveler Information Systems Success and false alarm rate tables

14 Bottleneck Identification and Forecasting in Traveler Information Systems Success rate over all 5 days (using sustained filter) Success rate

15 Bottleneck Identification and Forecasting in Traveler Information Systems False alarm rate over all 5 days (using sustained filter) False alarm rate

16 Bottleneck Identification and Forecasting in Traveler Information Systems Completed work  Validated this method on Oregon data  Compared to accepted ground truth  Found optimal values for parameters:  Data aggregation level  Max upstream speed  Min speed difference  Compared method with and without “sustained bottleneck” filter  Validated this method on Oregon data  Compared to accepted ground truth  Found optimal values for parameters:  Data aggregation level  Max upstream speed  Min speed difference  Compared method with and without “sustained bottleneck” filter

17 Bottleneck Identification and Forecasting in Traveler Information Systems Work in progress  Use the algorithm to process historical data and find prior probabilities to improve real-time detection  Find the entire congested area upstream of the bottleneck; use to find queue propagation speed  Use the algorithm to process historical data and find prior probabilities to improve real-time detection  Find the entire congested area upstream of the bottleneck; use to find queue propagation speed

18 Bottleneck Identification and Forecasting in Traveler Information Systems Historical bottleneck tracking

19 Bottleneck Identification and Forecasting in Traveler Information Systems Historical bottleneck tracking: 1

20 Bottleneck Identification and Forecasting in Traveler Information Systems Historical bottleneck tracking: 1

21 Bottleneck Identification and Forecasting in Traveler Information Systems Historical bottleneck tracking: 1

22 Bottleneck Identification and Forecasting in Traveler Information Systems Historical bottleneck tracking: 2

23 Bottleneck Identification and Forecasting in Traveler Information Systems Historical bottleneck tracking: 3

24 Bottleneck Identification and Forecasting in Traveler Information Systems Historical bottleneck tracking: 4

25 Bottleneck Identification and Forecasting in Traveler Information Systems Historical bottleneck tracking: 5

26 Bottleneck Identification and Forecasting in Traveler Information Systems Historical bottleneck tracking: sustained bottleneck filter

27 Bottleneck Identification and Forecasting in Traveler Information Systems Our intended goal

28 Bottleneck Identification and Forecasting in Traveler Information Systems Our intended goal

29 Bottleneck Identification and Forecasting in Traveler Information Systems Next steps  Implement into PORTAL and solicit user feedback  Run it for several months’ worth of data, and make use of historical knowledge to improve predictions  Test on additional corridors  Check for effect of weather conditions  Distinguish incidents from recurrent congestion  Implement into PORTAL and solicit user feedback  Run it for several months’ worth of data, and make use of historical knowledge to improve predictions  Test on additional corridors  Check for effect of weather conditions  Distinguish incidents from recurrent congestion

30 Bottleneck Identification and Forecasting in Traveler Information Systems AcknowledgmentsAcknowledgments  Oregon Department of Transportation  Federal Highway Administration  TriMet  The City of Portland, OR  National Science Foundation  CONACYT  TransPort ITS Committee  Oregon Department of Transportation  Federal Highway Administration  TriMet  The City of Portland, OR  National Science Foundation  CONACYT  TransPort ITS Committee Visit PORTAL Online:

31 Bottleneck Identification and Forecasting in Traveler Information Systems Thank You!