01366433

Component

https://doi.org/10.3141/2308-19

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

Vehicle classification data are used for numerous transportation applications. Most of the classification data come from permanent in-pavement sensors or temporary sensors mounted on the pavement. This study developed a lidar-based classification system that used data from sensors mounted in a side-fire configuration next to the road. The classification system first distinguished between vehicle returns and nonvehicle returns, and then clustered the vehicle returns into individual vehicles. An algorithm examined each vehicle cluster to check for any evidence of partial occlusion from another vehicle. Several measurements were taken from each nonoccluded cluster to classify the vehicle into one of six classes: motorcycle, passenger vehicle, passenger vehicle pulling a trailer, single-unit truck, single-unit truck pulling a trailer, and multiunit truck. The algorithm was evaluated at six locations under various traffic conditions. When compared with concurrent video ground truth data for more than 27,000 vehicles on a per vehicle basis, 11% of the vehicles were suspected of being partially occluded. The algorithm correctly classified more than 99.5% of the remaining nonoccluded vehicles. This research uncovered emerging challenges that likely applied to most classification systems: differentiating commuter cars from motorcycles. Occlusions were inevitable in this proof of concept study because the lidar sensors were mounted roughly 6 ft above the road, well below the tops of many vehicles. Ultimately a combination of a higher vantage point and shape information will greatly reduce the effects of occlusions.

01472278

12-2842

English

pp 173–183

2012

Transportation Research Record: Journal of the Transportation Research Board

9780309263061

Print

Figures; Photos; References; Tables

Algorithms; Automatic vehicle classification; Laser radar

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

TRIS, TRB, ATRI

Feb 8 2012 5:12PM

• 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
• Comparison of Modeling Approaches for Imputation of Video Detection Data in Intelligent Transportation 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
• 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