01020857

Component

https://doi.org/10.3141/1968-03

http://scholar.google.com...nger&publication_year=2006

Annual average daily traffic (AADT) data are important for various transportation research areas, including travel model calibration and validation, pavement design, roadway design, and air quality compliance. Specifically for model calibration and validation in long-range transportation planning, a base-year model requires numerous count locations across the study region. Sometimes count data for the lower classified roadways are not readily available. Detailed models require traffic counts for not only higher classifications of roadways such as freeways and arterials but also collector and, in some instances, local roadways. To predict AADT better for desired count locations on nonfreeway facilities, spatial dependency is considered. The theory behind the use of spatial dependency is that the traffic volume at one monitoring station is correlated with the volumes at neighboring stations. The spatial regression model takes into account both spatial trend (mean) and spatial correlation, which is modeled by a geostatistical approach called kriging. The spatial regression model is applied to AADT in Wake County, North Carolina. Results indicate that the overall predictive capability of the spatial regression model is much better than that of the ordinary regression model. In addition, the urban area has more reliable prediction than the rural area. Finally, the spatial regression model is expected to provide better predictions for desired count locations where no observed data currently exists due to budget limitations.

01037954

English

pp 20-29

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099773

Print

Figures (5) ; References (14) ; Tables (5)

Annual average daily traffic; Correlation analysis; Location; Regression analysis; Rural areas; Traffic counts; Traffic forecasting; Traffic volume; Trend (Statistics); Urban areas

Spatial regression models; Spatial statistics

Wake County (North Carolina)

Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB

Mar 3 2006 10:26AM

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• Applications of Artificial Intelligence Paradigms to Decision Support in Real-Time Traffic Management
• Forecasting Shared-Use Vehicle Trips with Neural Networks and Support Vector Machines
• Frameworks to Represent Uncertainty When Level of Service Is Determined
• Incorporating Wavelet Decomposition Technique to Compress TransGuide Intelligent Transportation System Data
• Kalman Filter-Based Tracking System for Automated Inventory of Roadway Signs
• Moments Analysis for Improving Decision Reliability Based on Travel Time
• Performance Evaluation of Sensor- and Image-Based Technologies for Automated Pavement Condition Surveys
• Predicting Directional Design Hourly Volume from Statutory Holiday Traffic
• Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters
• Real-Time Detection of Crash-Prone Conditions at Freeway High-Crash Locations
• Travel Time Estimation with Correlation Analysis of Single-Loop Detector Data
• Unscented Kalman Filter Method for Speed Estimation Using Single Loop Detector Data