01664259

Component

https://doi.org/10.1177/0361198118798243

Traffic volumes are a basic unit of measurement for understanding the transportation system. As investments in bicycle infrastructure are made, similar measures are necessary for understanding this non-motorized mode of travel. Methods for estimating annual average daily bicycle traffic (AADBT) are still developing, but generally employ techniques used in the motorized traffic monitoring field whereby data from permanent counters are used to construct expansion factors that are then applied to short-duration counts. This approach requires a network of permanent counters and knowledge about how to group factors into appropriate categories based on patterns observed in both the short-duration and permanent counter data. The methods presented in this paper advance a new approach to estimating AADBT solely using short-duration counts. The Seasonal Adjustment Regression Model uses statistical models that relate the daily bicycle volume to daily conditions and weather variables at a given count location. These models are then used to predict daily volumes for the remaining days of the year. To verify this approach and determine the resulting error, levels of available short-duration counts using varying amounts of permanent count data were simulated. This method was then applied to short-duration bicycle counts from Eugene, Oregon. With sufficient short-duration count data, this method can produce AADBT estimates with minimal error and without requiring a network of permanent counters. This approach also circumvents the need to determine which expansion factors should be applied to different short-term count locations by using statistical models in place of expansion factors.

18-05673

English

pp 145-153

2018-12

Transportation Research Record: Journal of the Transportation Research Board

Print

Figures (3) ; Maps; References (9) ; Tables (5)

Annual average daily traffic; Bicycle facilities; Bicycling; Traffic counts; Traffic volume

Eugene (Oregon)

Operations and Traffic Management; Pedestrians and Bicyclists; Planning and Forecasting

TRIS, TRB, ATRI

Jan 8 2018 11:27AM

• A Vibration-Based Vehicle Classification System using Distributed Optical Sensing Technology
• Annual Average Nonmotorized Traffic Estimates from Manual Counts: Quantifying Error
• Application of Trajectory Data for Investigating Vehicle Behavior in Mixed Traffic Environment
• Automated Validation and Interpolation of Long-Duration Bicycle Counting Data
• Challenges in Monitoring Regional Trail Traffic
• Comparison of Pedestrian Count Expansion Methods: Land Use Groups versus Empirical Clusters
• Coupled Approximation of U.S. Driving Speed and Volume Statistics using Spatial Conflation and Temporal Disaggregation
• Development of Statewide Annual Average Daily Traffic Estimation Model from Short-Term Counts: A Comparative Study for South Carolina
• Evaluating the Reliability, Coverage, and Added Value of Crowdsourced Traffic Incident Reports from Waze
• How Accurately Can We Measure from Video? Practical Considerations and Enhancements of the Camera Calibration Procedure
• Incorporating the Effect of Special Events into Continuous Count Site Selection for Pedestrian Traffic
• Method for Imputing Missing Data using Online Calibration for Urban Freeway Control
• Methods for Estimating Axle Factors and Axle Classes from Vehicle Length Data