DATA QUALITY VISUALIZATION TOOLS ON ARCHIVED HISTORICAL FREEWAY TRAFFIC DATA

Transcription

1 DATA QUALITY VISUALIZATION TOOLS ON ARCHIVED HISTORICAL FREEWAY TRAFFIC DATA Jothan P. Samuelson, Maricopa Association of Governments TRB NATMEC Conference June 6, , All Rights Reserved 1

2 Project Extents 286 detectors: 66% loops, 34% PADs (228 detectors in 2009: 58% loops, 42% PADS) 2012, All Rights Reserved 2

3 How accurate is your traffic data? Pop Survey a. 95% b. 90% c. 80% d. 70% e. don t know 2012, All Rights Reserved 3

4 How accurate is your traffic data? How accurate does your data need to be? Sensor Type Counts Speed Loops 6% mean error 7% mean error PADs 34% mean error 26% mean error sample count 17 freeway stations 4 freeway stations - MAG Accuracy Evaluation ( ) How can we look at the quality of our data without having to measure it explicitly? 2012, All Rights Reserved 4

5 Data Quality Visualization Tools on Archived Historical Freeway Traffic Data Jothan P. Samuelson Maricopa Association of Governments TRB NATMEC Conference, June 2012 Abstract: Concern regarding the quality of traffic data exists among engineers and planners tasked with obtaining and using the data for various transportation applications. While data quality issues are often understood by analysts doing the hands on work, rarely are the quality characteristics of the data effectively communicated beyond the analyst. This analysis is an exercise in measuring and reporting data quality using ten visualization charts associated and stored with the processed traffic data. This exercise was conducted to support the performance measurement program at the Maricopa Association of Governments in Phoenix, Arizona, and investigates the traffic data from 228 continuous monitoring freeway detectors in the metropolitan region. The visualization charts produced communicate important information about the completeness and even accuracy of the traffic data, and can be used qualitatively in evaluating the validity of the traffic data beyond pass/fail criteria commonly used. These visualization charts also serve to educate an intuitive sense or understanding of the underlying characteristics of the data considered and used as valid. This presentation describes the method by which the traffic data has been processed and the creation and use of the visualization charts. Experience gained in the resulting data quality assessment would recommend wherever possible that visualizations tools be developed and used in the processing and quality control of all traffic data; and that these visualization tools, along with other information on the quality control effort, be stored as metadata with the processed traffic data. Data Analysis Steps: Sort raw lane specific freeway sensor data to a consistently formatted annual text data file for each detector station process sorted data through filtering and aggregation and cleaning template copy processed and aggregated results to receptacle template with data quality visualization charts and performance measure summaries. aggregate select point level charts and data to corridor level files. manually evaluate data quality visualization charts at the point level in the corridor aggregation file. Result: Processed and cleaned point level data in accessible format Visualization charts characterizing the quality of the data Summary point level performance measures Corridor level summary files for evaluation of point level data in relation to adjacent locations and corridor as a whole Utilization of processed data possible for a variety of applications where data can be rejected/accepted based on the application needs, the condition of adjacent detector data, and availability of the data as a whole Conclusions: Pass/fail quality control filters are not enough. Visualization of the data is needed in qualitatively filtering bad data. Visualization of the data is needed in better utilizing valid data by communicating the confidence that can be had in this data. Analysts stand to benefit most from tools that educate an intuitive sense of data quality. Needed are experience and guidelines for utilizing visualization tools for corridor level validity checks. 2012, All Rights Reserved 5

6 Data Processing and QC Steps raw data sorting copy/paste data to Excel template with formulas for sorting save data from template to text file combine data to single annual text file for each station station level copy data to template with aggregation and QC formulas copy cleaned and aggregated data to receptacle template with QC panel and summary performance statistics corridor level combine point level Visualization charts and performance measure statistics to tabs in corridor level file manually QC point level data with respect to corridor data 2012, All Rights Reserved 6

7 Data Processing and QC Steps Point Level Result File Example Corridor Level Result File Example Please contact presenter for more information and external example files referenced in this slide. 2012, All Rights Reserved 7

8 Chart 1: Average Weekday Speed Valuable Information: 80 Annual Hourly Average Speeds - weekdays Overall speed trend Relationship between GP and hov speed Peak congestion period Average Speed (mph) All lanes GP lanes HOV lane 80 1 AM 2 AM 3 AM 4 AM 5 AM Annual Hourly Average Speeds - weekdays 6 AM 7 AM 8 AM 9 AM 10 AM 11 AM 12 PM 1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM 9 PM 10 PM 11 PM Average Speed (mph) AM 2 AM 3 AM 4 AM 5 AM 6 AM 7 AM 8 AM 9 AM 10 AM 11 AM All lanes GP lanes HOV lane 12 PM 1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM 9 PM 10 PM 11 PM above: detector 64 I-10 Broadway left: detector 59 I th Street ½ mile up stream of det , All Rights Reserved 8

9 Chart 2: Average Weekday Volume Valuable Information: Overall volume per lane trend Relationship between GP and hov volume Peak volume period Average Volume Per Lane AM Annual Hourly Average Throughput Per Lane - weekdays 2 AM 3 AM 4 AM 5 AM 6 AM 7 AM 8 AM 9 AM 10 AM 11 AM All lanes GP lanes HOV lane 12 PM Average Volume Per Lane 1 PM PM 3 PM 1 AM 2 AM 3 AM 4 AM 5 AM 6 AM 7 AM 8 AM 9 AM 10 AM 11 AM 12 PM 1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM 9 PM 10 PM 11 PM 4 PM 5 PM Annual Hourly Average Throughput Per Lane - weekdays 6 PM 7 PM 8 PM 9 PM 10 PM 11 PM All lanes GP lanes HOV lane above: detector 50 I-10 University Dr. left: detector 545 Loop 101 Elliot Rd 2012, All Rights Reserved 9

10 Chart 3: Average Weekday Occupancy Valuable Information: relationship between peak occupancy, volume, and speed Average Occupancy percent Annual Hourly Average Occupancy Percent - weekdays all occ gp occ hov occ 1 AM 2 AM 3 AM 4 AM 5 AM 6 AM 7 AM 8 AM 9 AM 10 AM 11 AM 12 PM 1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM 9 PM 10 PM 11 PM Average Occupancy percent Annual Hourly Average Occupancy Percent - weekdays all occ gp occ hov occ 1 AM 2 AM 3 AM 4 AM 5 AM 6 AM 7 AM 8 AM 9 AM 10 AM 11 AM 12 PM 1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM 9 PM 10 PM 11 PM above: detector 64 I-10 Broadway left: detector 393 I-10 Southern Ave ½ mile downstream of detector , All Rights Reserved 10

11 Chart 4: Average by-lane Distribution Valuable Information: Calibration error demonstrated by lanes irregularity lane specific data errors ADT 30,000 25,000 20,000 15,000 10,000 5,000 Annual Average by Lane - raw data with zero values and without ADT Speed Occupancy Speed and Occupancy ADT 30,000 25,000 20,000 15,000 10,000 5,000 - Annual Average by Lane - raw data with zero - values and without ADT Speed Occupancy lane h lane g lane f lane e lane d lane c lane b lane a lane h lane g lane f80 lane e lane d lane c lane b lane a Speed and Occupancy above: detector 30 I nd Ave. left: detector 263 Loop 101 SB, approaching Southern Ave , All Rights Reserved 11

12 Chart 5: Annual Validity Distribution Valuable Information: the amount and season of data missing missing vs. invalid data Percent of Data Rows Valid 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Distribution of Data Passing Quality Control Criteria by Date 0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Percent of Data Rows Valid 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Distribution of Data Passing Quality Control Criteria by Date 0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec above: detector 35 I th Ave. left: detector 85 I-10 Loop 101 SB 2012, All Rights Reserved 12

13 Chart 6: Weekly Validity Distribution Valuable Information: percent valid on weekdays vs. weekends overall percent of data valid Percent of Data Rows Valid 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 100% Distribution of Data Passing Quality Control Criteria by Weekday Percent of Data Rows Valid % 80% 70% 60% 50% 40% 30% 20% 10% 0% Distribution of Data Passing Quality Control Criteria by Weekday above: detector 35 I th Ave left: detector 396 I-10 Baseline Dr. 2012, All Rights Reserved 13

14 Chart 7: Count of Error Flags by Hour Valuable Information: which type of errors exist uniform error vs. time specific error 3500 Count of Annual Quality Control Flags by Hour of Day - weekdays Count of 5-min Error Flags per Hour Count of Annual Quality Control Flags by Hour of Day - weekdays speed volume occupancy difference missing all rows 1 AM 2 AM 3 AM 4 AM 5 AM 6 AM 7 AM 8 AM 9 AM 10 AM 11 AM 12 PM 1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM 9 PM 10 PM 11 PM Count of 5-min Error Flags per Hour AM 2 AM 3 AM 4 AM 5 AM 6 AM 7 AM 8 AM 9 AM 10 AM 11 AM 12 PM 1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM speed volume occupancy difference missing all rows 9 PM 10 PM 11 PM above: detector 141 I-10 Loop 202 Interchange left: detector 66 I-10 WB, South of Broadway Rd. 2012, All Rights Reserved 14

15 Chart 8: Flow/Density Distribution Valuable Information: extent of peak period congestion deviations from expected flow/density relationship above: detector 50 I th St. left: detector 93 I-10 Buckeye Rd. 2012, All Rights Reserved 15

16 Chart 9: Speed/Density Distribution Valuable Information: extent of peak period congestion deviations from expected speed/density relationship above: detector 261 Loop 101 Broadway Rd left: detector 255 Loop 101 Loop 202 Fwy 2012, All Rights Reserved 16

17 Chart 10: Speed/Flow Distribution Valuable Information: extent of peak period congestion deviations from expected speed/flow relationship above: detector 50 I th St. left: detector 264 Loop 101 Southern Ave. 2012, All Rights Reserved 17

18 Result Summary Processed and cleaned point level data in accessible format Visualization charts characterizing the quality of the data Summary point level performance measures Corridor level summary files for evaluation of point level data in relation to adjacent locations and corridor as a whole Utilization of processed data possible for a variety of applications where data can be rejected/accepted based on the application needs, the condition of adjacent detector data, and availability of the data as a whole 2012, All Rights Reserved 18

19 Conclusions 1. Pass/fail quality control filters are not enough. Visualization of the data is needed in qualitatively filtering bad data. 2. Visualization of the data is needed in better utilizing valid data by communicating the confidence that can be had in this data. 3. Analysts stand to benefit most from tools that educate an intuitive sense of the data quality. 4. Needed are more experience and guidelines for utilizing visualization tools for corridor level validity checks. 2012, All Rights Reserved 19

20 Data Quality Visualization Tools on Archived Historical Freeway Traffic Data Jothan P. Samuelson Maricopa Association of Governments TRB NATMEC Conference, June 2012 Abstract: Concern regarding the quality of traffic data exists among engineers and planners tasked with obtaining and using the data for various transportation applications. While data quality issues are often understood by analysts doing the hands on work, rarely are the quality characteristics of the data effectively communicated beyond the analyst. This analysis is an exercise in measuring and reporting data quality using ten visualization charts associated and stored with the processed traffic data. This exercise was conducted to support the performance measurement program at the Maricopa Association of Governments in Phoenix, Arizona, and investigates the traffic data from 228 continuous monitoring freeway detectors in the metropolitan region. The visualization charts produced communicate important information about the completeness and even accuracy of the traffic data, and can be used qualitatively in evaluating the validity of the traffic data beyond pass/fail criteria commonly used. These visualization charts also serve to educate an intuitive sense or understanding of the underlying characteristics of the data considered and used as valid. This presentation describes the method by which the traffic data has been processed and the creation and use of the visualization charts. Experience gained in the resulting data quality assessment would recommend wherever possible that visualizations tools be developed and used in the processing and quality control of all traffic data; and that these visualization tools, along with other information on the quality control effort, be stored as metadata with the processed traffic data. Data Analysis Steps: Sort raw lane specific freeway sensor data to a consistently formatted annual text data file for each detector station process sorted data through filtering and aggregation and cleaning template copy processed and aggregated results to receptacle template with data quality visualization charts and performance measure summaries. aggregate select point level charts and data to corridor level files. manually evaluate data quality visualization charts at the point level in the corridor aggregation file. Result: Processed and cleaned point level data in accessible format Visualization charts characterizing the quality of the data Summary point level performance measures Corridor level summary files for evaluation of point level data in relation to adjacent locations and corridor as a whole Utilization of processed data possible for a variety of applications where data can be rejected/accepted based on the application needs, the condition of adjacent detector data, and availability of the data as a whole Conclusions: Pass/fail quality control filters are not enough. Visualization of the data is needed in qualitatively filtering bad data. Visualization of the data is needed in better utilizing valid data by communicating the confidence that can be had in this data. Analysts stand to benefit most from tools that educate an intuitive sense of data quality. Needed are experience and guidelines for utilizing visualization tools for corridor level validity checks. 2012, All Rights Reserved 20