Monitoring the Use of HOV and HOT Lanes

Holec, Eric; Somasundaram, Guruprasad; Papanikolopoulos, Nikolaos; Morellas, Vassilios; University of Minnesota. Department of Mechanical Engineering; University of Minnesota. Digital Technology Center

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Monitoring the Use of HOV and HOT Lanes

2013-01-01
By Holec, Eric ; Somasundaram, Guruprasad ; Papanikolopoulos, Nikolaos ; ...

[PDF-631.85 KB]

English

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Creators:

Holec, Eric ; Somasundaram, Guruprasad ; Papanikolopoulos, Nikolaos ; Morellas, Vassilios

Holec, Eric ; Somasundaram, Guruprasad ; Papanikolopoulos, Nikolaos ; Morellas, Vassilios Less -
Corporate Creators:

University of Minnesota. Department of Mechanical Engineering ; University of Minnesota. Digital Technology Center

University of Minnesota. Department of Mechanical Engineering ; University of Minnesota. Digital Technology Center Less -
Corporate Contributors:

University of Minnesota. Center for Transportation Studies. Intelligent Transportation Systems Institute ; United States. Department of Transportation. University Transportation Centers (UTC) Program

University of Minnesota. Center for Transportation Studies. Intelligent Transportation Systems Institute ; United States. Department of Transportation. University Transportation Centers (UTC) Program Less -
Subject/TRT Terms:

[+]

High Occupancy Toll Lanes High Occupancy Vehicle Lanes High Occupancy Vehicles Infrared Detectors Monitoring Passenger Vehicles Traffic Counting Vehicle Occupants
Publication/ Report Number:

CTS 13-07
Resource Type:

Research Paper
Geographical Coverage:

United States
Corporate Publisher:

University of Minnesota. Center for Transportation Studies. Intelligent Transportation Systems Institute
Abstract:

This report presents the formulation and implementation of an automated computer vision and machine learning based system for estimation of the occupancy of passenger vehicles in high-occupancy vehicles and high-occupancy toll (HOV/HOT) lanes. The authors employ a multi-modal approach involving near-infrared images and high-resolution color video images in conjunction with strong maximum margin based classifiers such as support vector machines. The authors attempt to maximize the information that can be extracted from these two types of images by computing different features. Then, the authors build classifiers for each type of feature which are compared to determine the best feature for each imaging method. Based on the performance of the classifiers the authors critique the efficacy of the individual approaches as the costs involved are significantly different.
Format:

PDF
Funding:

CTS Project #2011079
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:1c508e5fcc8789b3c193931cad4aeb88ac528c219833683ae71a38d1992685ee
Download URL:

https://rosap.ntl.bts.gov/view/dot/61406/dot_61406_DS1.pdf
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Monitoring the Use of HOV and HOT Lanes

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