01020417

Component

https://doi.org/10.3141/1945-12

http://scholar.google.com...+Wei&publication_year=2006

Classified vehicle volumes are important inputs for traffic operation, pavement design, and transportation planning. However, such data are not available from single-loop detectors, the most widely deployed type of traffic sensor in the existing roadway infrastructure. Several attempts have been made to extract classified vehicle volume data from single-loop measurements in recent years. These studies used estimated speed for length calculation and classified vehicles into bins based on the calculated vehicle lengths. However, because of the stochastic features of traffic flow, deterministic mathematical equations based on certain assumptions for speed calculation typically do not work well for all situations and may result in significant speed estimation errors under certain traffic conditions. Such errors accumulate when estimated speeds are used in vehicle-length calculations and degrade the accuracy of vehicle classification. To solve this problem, an artificial neural network method was developed to estimate classified vehicle volume data directly from single-loop measurements. The proposed neural network is three-layered with a back-propagation structure. This method was tested with data collected from several loop stations on I-5 over a long duration. The proposed artificial neural network model produced reliable estimates of volumes of classified vehicles under various traffic conditions.

01031684

English

0000-0002-4180-5628

pp 100-108

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099536

Print

Figures (5) ; References (24) ; Tables (4)

Accuracy; Errors; Loop detectors; Neural networks; Reliability; Sensors; Traffic flow; Traffic speed; Vehicle classification; Vehicle length

Single loop detectors

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

TRIS, TRB, ATRI

Mar 3 2006 11:02AM

• Accuracy Assessment of Traffic Monitoring Devices Vehicle by Vehicle
• Calculation of Travel Time Variability from Loop Detector Data
• Cellular-Based Data-Extracting Method for Trip Distribution
• Characterization of Truck Traffic in California for Mechanistic-Empirical Design
• Detection of Invalid Loop Detector Data in Urban Areas
• Effect of Sensor Spacing on Performance Measure Calculations
• Evaluation of Quartz Piezoelectric Weigh-in-Motion Sensors
• Identification and Correction of Dual-Loop Sensitivity Problems
• Identifying Lane-Mapping Errors at Freeway Detector Stations
• Sensitivity of NCHRP 1-37A Pavement Design to Traffic Input
• Statistical Investigations of Statutory Holiday Effects on Traffic Volumes
• Using Automatic Vehicle Identification Data to Gain Insight into Travel Time Variability and Its Causes