01476727

Component

https://doi.org/10.3141/2380-08

http://scholar.google.com...ndez&publication_year=2013

In this study, a new vehicle classification algorithm was developed with inductive loop signature technology. There were two steps to the proposed algorithm. The first step was to use the Haar wavelet to transform and reconstruct inductive vehicle signatures, and the second step was to group vehicles into FHWA vehicle types through the use of the k nearest neighbor (KNN) approach with a Euclidean distance classifier. To determine the proper proportion of the wavelet to apply for reconstruction and feature extraction, transformed signatures were examined with percentages of large components of their corresponding wavelets. To implement the KNN approach, a library of vehicle signature templates for each FHWA vehicle class was composed. The proposed vehicle classification algorithm demonstrated promising classification results, with a 92.4% overall accuracy. The algorithm can be applied to the real world without the concerns about recalibration and transferability that arise with the use of signature data from single loops. Two additional vehicle classification schemes were applied for performance evaluation. For the inductive signature performance evaluation classification scheme, which aimed to facilitate emission analysis and easy interpretation, the overall accuracy was 94.1%. For the axle-based vehicle classification scheme proposed in this project, which aimed to group vehicles by use and the number of axles, the overall accuracy was 93.8%. Future research will focus on refinement of the signature template library for each FHWA vehicle type to further improve the performance of the proposed vehicle classification algorithm. The selection of the value of k for the KNN approach will be investigated also.

01490620

13-3182

English

pp 72–80

2013

Transportation Research Record: Journal of the Transportation Research Board

9780309263351

Print

Figures (4) ; Photos; References (30) ; Tables (5)

Algorithms; Axles; Intelligent transportation systems; Loop detectors; Vehicle classification; Wavelets

K-Nearest Neighbor Models

Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment; I73: Traffic Control

TRIS, TRB, ATRI

Feb 5 2013 12:38PM

• Assessment of Dynamic Speed Limits on Freeway A20 near Rotterdam, Netherlands
• Congestion Relief Based on Intelligent Transportation Systems in Florida: Analysis of Triple Bottom Line Sustainability Impact
• Evaluation of Wide-Area Traffic Monitoring Technologies for Travel Time Studies
• Hybrid Work Zone Information System with Portable Changeable Message Signs and Dedicated Short-Range Communication
• Improving Accuracy and Precision of Travel Time Samples Collected at Signalized Arterial Roads with Bluetooth Sensors
• Performance Characterization of Arterial Traffic Flow with Probe Vehicle Data
• Real-Time Detection and Tracking of Traffic Shock Waves by Conjugated Low-Angle Cameras
• Relationship Between Application Scale and Maximum Time Latency in Intelligent Transport Solutions
• State of the Practice on Use of Intelligent Transportation Systems at U.S.–Mexican Land Border Crossings
• Traffic Management Effects of Variable Speed Limit System on a German Autobahn: Empirical Assessment Before and After System Implementation
• U.S. Integrated Transportation Systems in the Future, 2030 to 2050: Application of a Scenario Planning Tool