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Quality Control Procedures for Archived Operations Traffic Data: Synthesis of Practice and Recommendations

Executive Summary

Introduction

The quality of traffic data archived from some traffic operations centers has been identified as a concern. Using quality control procedures to monitor and identify traffic data quality problems is an essential component of improving data quality. This report summarizes quality control procedures suggested in the literature as well as those that are used in numerous archived data management systems (ADMS) implementations. This report provides recommendations for a basic set of quality control procedures that can be adopted, as well as a process to customize quality control procedures for system-specific data quality issues.

Review of the Literature and Current Practice

The literature review found identified validity criteria that could be classified into the following general categories:

  • Univariate and multivariate range checks - These criteria typically correspond to the minimum, maximum, or range of expected values for a single variable or combination of variables.
  • Spatial and temporal consistency - These criteria evaluate the consistency of traffic data as compared to nearby locations (either across lanes, or upstream and downstream monitoring locations) or previous time periods.
  • Detailed diagnostics - These criteria require detailed diagnostic data from traffic detectors and typically can not be performed with the original source data that is collected and archived by traffic operations centers.

A review of current practice identified the following findings:

  • The validity criteria are similar among the nine data archives that were reviewed.
  • The validity criteria are less complex than those described in the literature.
  • Nearly all of the validity criteria are programmed on a simple pass/fail basis.
  • Most of the validity criteria do not have a specified order or sequence.
  • In most cases, all criteria are applied even if previous criteria indicate invalid data.

Recommendations

The following recommendations were provided for quality control procedures as they relate to archived operations data:

  • Recognize that validity criteria (i.e., quality control) are only one part of a comprehensive quality assurance process that does more than just discard suspect data that have already been collected.
  • Provide metadata to document quality control procedures and results.
  • Provide metadata to document historical traffic sensor status and configuration.
  • Use database flags or codes to indicate failed validity criteria.
  • At a minimum, implement basic foundation for data validity criteria (Table ES-1).
  • Further develop other spatial and temporal consistency criteria for ADMS.
  • When feasible, use visual review to supplement the automated validity criteria.
Table ES-1. Recommended Validity Criteria for Freeway Traffic Detector Data
Validity Criteria Default Parameter
Prescreening Criteria  
Controller error codes (e.g., -1, 255, etc.) n.a.
Check consistency of elapsed time and poll cycles n.a.
Check for duplicate records (location ID, date, time identical) n.a.
If VOL=OCC=SPD=0, then set SPD=missing/null (no vehicles present) n.a.
Univariate Range Criteria  
Minimum volume 0 vehicles
Maximum volume 3000 vphpl (adjust for appropriate time interval)
Minimum occupancy 0%
Maximum occupancy 100%
Minimum speed 0 mph
Maximum speed 100 mph
Multivariate Logical Consistency  
Maximum consecutive identical volume & occupancy & speed values (including VOL=OCC=SPD=0) Number of reporting intervals that corresponds to 30 consecutive minutes (max.) with no vehicles detected
If volume>0 & speed=0 then invalid n.a.
If volume=0 & speed>0 then invalid n.a.
If volume=speed=0 & occupancy>0 then invalid n.a.
If occupancy=0 and volume>volumemax (based on maximum possible volume when occupancy value is truncated to 0) VOLmax = [(2.932×SPEED×ELAPSED_TIME)/600
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Updated: 04/05/2011
 

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