The Downtown Seattle Bus Monitoring System A New Way of Collecting and Analyzing Transit Travel Time Data Owen Kehoe, PE, PTOE King County Metro Transit : Seattle, WA

2 Travel Time Measurement & Reporting  Project Background  Data Sources  Data Processing Tools  Report Format Development  Results Outline

3 Background  Alaskan Way Viaduct –Replacement –WSDOT projects –Long-term detours –Reduced lane capacity  Enhanced Transit Services Agreement –WSDOT provides funding for transit Added trips Added trips Schedule Maintenance Schedule Maintenance –Travel Time Monitoring Source: WSDOT

4 Objectives  Audience: Traffic Engineers & Auditors –Unfamiliar with transit network, scheduling –Concerned with corridors  Long Term Reporting –Construction detours –Transit network changes Rapid Ride C & D Line BRT service (2012) Rapid Ride C & D Line BRT service (2012)  Pinpoint Specific Construction Impacts –Peak spreading –Travel time variation and reliability

5 Construction Impact Area Alaskan Way Viaduct Complex Transit Routing 1 st Ave S WEST SEATTLE

6 Data Sources  AVL System [Automatic Vehicle Location] –Travel time between timepoints only! Route specific Route specific Off-route buses not tracked Off-route buses not tracked –Data available System-Wide  AVI System [Automatic Vehicle Identification] –Roadside RFID tag readers –CBD Only –WSDOT funding to Expand System

7 AVI Reader Locations Outer CBD Screenline Other Key Locations Original AVI System North CBD Screenline Central CBD Screenline South CBD Screenline Expanded AVI System Outer CBD Screenline W Seattle Screenline Other Key Locations

8 Data Processing  SQL Server and MS Access –Huge amount of data  Interpolate AVL data for locations between timepoints –Break up timepoint data into street segments –GIS and Bus Stop data  Estimate travel time share AD Travel Time = 5 min Length= 1000’ Speed= 20 mph Length= 2000’ Speed= 30 mph Length= 1000’ Speed= 25 mph C Dwell = 20 sec B Dwell = 30 sec Travel Time = 3 min AVL Data GIS Data Bus Stop Data Share 2.0 min1.5 min

9 Data Processing  Need to combine AVL & AVI Data –AVI  CBD and selected locations –AVL  Outlying points  Travel Times calculated by matching records –Coach ID, Route, Run, Date, (Time) Start Point End Point Coach: 2345 Route/Run: 5/13 Date: 3/14/2010 Timestamp: 10:34:33 Coach: 2345 Route/Run: 5/13 Date: 3/14/2010 Timestamp: 10:42:56 Travel Time=8:23 Match!

10 Report Development  Report 3x/Year  Specific Pathways Defined by corridor, start/end points Defined by corridor, start/end points Grouped by market coverage area Grouped by market coverage area

11 Report Development  Measures of Effectiveness –Slice data into hour intervals –Median Travel Times (Average) Less affected by extreme data Less affected by extreme data –25 th – 75 th Percentile (Reliability) Median Travel Time 25 th & 75 th Percentile One Bar represents one hour of data Compare scenarios side-by-side {

12 Report Development Two Directions, Two Charts Sample Size: Indication of Data Quality Pathway Map Scenario Info

13 System in Action  Ramp Closure –Multiple Routes –Lengthy Detour RR crossing RR crossing Drawbridge Drawbridge  Monitoring/Reporting system used to pinpoint impact Ramp Closed min. delay all day Reliability Problem PM Peak Inbound OK (no detour)

14 Highlights  New & Unique Approaches: –Combine different data streams –Define pathways by corridor, not route –Median rather than Average –Percentile as reliability measure –Slice data by hour for good level of detail  Useful Results: –Pinpoint effects of specific traffic events –Quantify impacts to schedule

The Downtown Seattle Bus Monitoring System A New Way of Collecting and Analyzing Transit Travel Time Data Owen Kehoe, PE, PTOE King County Metro Transit Seattle, WA