00966722

Component

https://doi.org/10.3141/1855-20

http://scholar.google.com...aiya&publication_year=2003

Single-loop detectors provide the most abundant source of traffic data in California, but loop data samples are often missing or invalid. A method is described that detects bad data samples and imputes missing or bad samples to form a complete grid of clean data, in real time. The diagnostics algorithm and the imputation algorithm that implement this method are operational on 14,871 loops in six districts of the California Department of Transportation. The diagnostics algorithm detects bad (malfunctioning) single-loop detectors from their volume and occupancy measurements. Its novelty is its use of time series of many samples, instead of basing decisions on single samples, as in previous approaches. The imputation algorithm models the relationship between neighboring loops as linear and uses linear regression to estimate the value of missing or bad samples. This gives a better estimate than previous methods because it uses historical data to learn how pairs of neighboring loops behave. Detection of bad loops and imputation of loop data are important because they allow algorithms that use loop data to perform analysis without requiring them to compensate for missing or incorrect data samples.

This paper appears in Transportation Research Record No. 1855, Transportation Data Research.

English

p. 160-167

2003

Transportation Research Record

0309085918

Figures (7) ; References (6) ; Tables (4)

Algorithms; Data quality; Detection and identification; Errors; Loop detectors; Real time data processing; Traffic data

California Department of Transportation

Diagnostics; Imputation; Missing data

California

Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Dec 30 2003 12:00AM

• ALGORITHM FOR DETECTING AND CORRECTING ERRORS IN ARCHIVED TRAFFIC DATA
• APPLICATION OF PROBE-VEHICLE DATA FOR REAL-TIME TRAFFIC-STATE ESTIMATION AND SHORT-TERM TRAVEL-TIME PREDICTION ON A FREEWAY
• ASSESSMENT OF DATA-COLLECTION TECHNIQUES FOR HIGHWAY AGENCIES
• BEHAVIORAL MECHANISMS OF NONRESPONSE IN MAIL-BACK TRAVEL SURVEYS
• COLLECTING VEHICLE-SPEED DATA BY USING TIME-LAPSE VIDEO RECORDING EQUIPMENT
• CORRELATION TECHNIQUE FOR ESTIMATING TRAFFIC SPEED FROM CAMERAS
• DEVELOPING A COMMODITY FLOW DATABASE FROM TRANSEARCH DATA
• DEVELOPMENT OF A HYBRID MODEL FOR DYNAMIC TRAVEL-TIME PREDICTION
• DEVELOPMENT OF A SYSTEM FOR COLLECTING LOOP-DETECTOR EVENT DATA FOR INDIVIDUAL VEHICLES
• ESTIMATING ANNUAL AVERAGE DAILY TRAFFIC FROM SATELLITE IMAGERY AND AIR PHOTOS: EMPIRICAL RESULTS
• ESTIMATING CORRIDOR TRAVEL TIME BY USING TRANSIT VEHICLES AS PROBES
• EVALUATION OF AN ARTIFICIAL NEURAL NETWORK TECHNIQUE APPLIED TO MULTIPLE-SENSOR WEIGH-IN-MOTION SYSTEMS
• GENERATING VEHICLE-SPECIFIC SPEED PROFILES FROM AUTOMATIC TRAFFIC RECORDERS
• IMPROVING A TRAVEL-TIME ESTIMATION ALGORITHM BY USING DUAL LOOP DETECTORS
• MICROSCOPIC TRAFFIC DATA COLLECTION BY REMOTE SENSING
• PREDICTING TRAVEL TIMES FOR THE SOUTH JERSEY REAL-TIME MOTORIST INFORMATION SYSTEM
• PRESERVATION OF INFRASTRUCTURE BY USING WEIGH-IN-MOTION COORDINATED WEIGHT ENFORCEMENT
• SIMPLEX-BASED CALIBRATION OF TRAFFIC MICROSIMULATION MODELS WITH INTELLIGENT TRANSPORTATION SYSTEMS DATA
• TECHNOLOGIES FOR TRUCK CLASSIFICATION AND METHODOLOGIES FOR ESTIMATING TRUCK VEHICLE MILES TRAVELED
• TEMPORAL VARIABILITY OF CAR USE AS AN INPUT TO DESIGN OF BEFORE AND AFTER SURVEYS
• TRAVEL-TIME RELIABILITY AS A MEASURE OF SERVICE
• USE OF LONG-TERM PAVEMENT PERFORMANCE DATA TO DEVELOP TRAFFIC DEFAULTS IN SUPPORT OF MECHANISTIC-EMPIRICAL PAVEMENT DESIGN PROCEDURES
• WEB-BASED RURAL ROAD ASSET-MANAGEMENT SYSTEM