01371153

Component

https://doi.org/10.3141/2308-15

http://scholar.google.com...aval&publication_year=2012

Gaps in real-time and archived traffic data are common and can be attributed to several factors, such as sensor failures and data communications interruptions. Regardless of the cause of the missing data, these gaps often must be filled with reliable and accurate estimates before the data can be used for planning, operations, or congestion-mitigation purposes. This research compared different methods for imputing missing values in video detection system data, including historical averages, simple linear regression, multiple linear regression, spatial averages, and Newell’s simplified kinematic wave model. The study used the fundamental relationship between speed and flow in filtering the data for quality control. A sensitivity analysis tested the response of different methods to factors such as the size of training data set and time-of-day adjustments to the algorithms. The results indicated that the time of day and volume adjustment factors had a nontrivial impact on the accuracy of the outputs. Despite significant errors in the base data set, the Newell algorithm performed on a par with the other methods in terms of bias and mean absolute percentage, but the more simple factoring methods provided comparable results and were easier to implement.

01472278

12-4590

English

pp 138–147

2012

Transportation Research Record: Journal of the Transportation Research Board

9780309263061

Print

Figures; References; Tables

Algorithms; Data quality; Intelligent transportation systems; Traffic data; Video imaging detectors

Data filtering; Data imputation; Missing data

Data and Information Technology; Highways; Operations and Traffic Management; I73: Traffic Control

TRIS, TRB, ATRI

Feb 8 2012 5:25PM

• Advances in Uncertainty Treatment in FHWA Procedure for Estimating Annual Average Daily Traffic Volume
• Analysis of Travel Time Reliability in New York City Based on Day-of-Week and Time-of-Day Periods
• Average Travel Time Estimations for Urban Routes That Consider Exit Turning Movements
• Bluetooth Sensor Data and Ground Truth Testing of Reported Travel Times
• Comparative Evaluation of Reported Speeds from Corresponding Fixed-Point and Probe-Based Detection Systems
• Development of Hypothesis Test for Travel Time Data Quality
• Estimating Traffic Speed with Single Inductive Loop Event Data
• Estimation of Multiclass and Multilane Counts from Aggregate Loop Detector Data
• Evaluation of Thermal Image Video Sensors for Stop Bar Detection at Signalized Intersections
• Exploring Link Covering and Node Covering Formulations of Detection Layout Problem
• Generating Route-Specific Origin–Destination Tables Using Bluetooth Technology
• Household-Level Global Positioning System Travel Data to Measure Regional Traffic Congestion
• Multisensor Data Integration and Fusion in Traffic Operations and Management
• Probabilistic Fusion of Vehicle Features for Reidentification and Travel Time Estimation Using Video Image Data
• Sensitivity of Commuters’ Demographic Characteristics to License Plate Data Collection Specifications: Case Study of I-85 High-Occupancy Vehicle to High-Occupancy Toll Lanes Conversion in Atlanta, Georgia
• Short-Term Travel Time Prediction Considering the Effects of Weather
• Side-Fire Lidar-Based Vehicle Classification
• Studying Patterns of Use of Transport Modes Through Data Mining: Application to U.S. National Household Travel Survey Data Set
• Traffic Ground Truth Estimation Using Multisensor Consensus Filter