01014822

Component

https://doi.org/10.3141/1917-21

http://scholar.google.com...+Sun&publication_year=2005

Intelligent transportation systems can play a significant role in transportation security in addition to their traditional roles in transportation operations and management. A multidetector semiautomated vehicle surveillance framework is presented. The objective of the framework is to assist in the search for a vehicle of interest involved with security threats such as terrorism, abduction, or crime. When a vehicle of interest is wanted, this framework can be applied to reduce surveillance data sets and thus reduce time and labor. This system estimates the a posteriori probabilities that indicate the closeness of the match between a vehicle of interest and any vehicle in the search space. This paper explores the use of multidetector fusion of video and inductive loop data by means of a linear fusion model. This system classifies vehicle pairs into possible correct match or incorrect match classes and transforms the problem into the probabilistic domain by using Bayesian neural networks and probabilistic neural networks (PNNs). The use of Bayesian and PNN classifiers assumes equal losses. With Bayesian estimation and generalized regression neural networks, the a posteriori probability is used as a threshold representing unequal losses. A comparison between the traditional Bayesian approaches and the equivalent neural network methods is presented. The use of different feature combinations, methods to balance training data sets, forward sequential search, and combined and uncombined feature approaches is also investigated. Field arterial data from southern California show that, by retaining only 29% of the search space, the framework produces 92% accuracy, which is a promising result.

01014803

English

0000-0002-8857-9648

pp 190-198

2005

Transportation Research Record: Journal of the Transportation Research Board

0309093902

Print

Figures (4) ; References (11) ; Tables (2)

Crimes; Data fusion; Identification systems; Intelligent transportation systems; Loop detectors; Neural networks; Security; Surveillance; Terrorism; Video imaging detectors

Bayesian approach; Probabilistic neural networks; Semiautomated vehicle surveillance; Vehicles of interest

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

TRIS, TRB, ATRI

Dec 28 2005 8:44AM

• Assessing Issues, Technologies, and Data Needs to Meet Traffic Input Requirements by Mechanistic-Empirical Pavement Design Guide: Implementation Initiatives
• Assessing Robustness of Imputation Models Based on Data from Different Jurisdictions: Examples of Alberta and Saskatchewan, Canada
• Comparison of Trip Determination Methods in Household Travel Surveys Enhanced by a Global Positioning System
• Connectionist Approach to Improving Highway Vehicle Classification Schemes: The Florida Case
• Cost-Effective Reporting of Travel on Local Roads
• Customized Pretrip Prediction of Freeway Travel Times for Road Users
• Developing Custom Tools for Deriving Complex Data from Travel Survey Databases
• Enterprisewide Spatial Data Integration of Legacy Systems for Asset Management: The Case of the Illinois Department of Transportation
• Estimating Intersection Control Delay Using Large Data Sets of Travel Time from a Global Positioning System
• Evaluation of Microwave Radar Trailers for Nonintrusive Traffic Measurements
• Experience Implementing a User Service for Archived Intelligent Transportation Systems Data
• Exploiting Correlations Between Link Flows to Improve Estimation of Average Annual Daily Traffic on Coverage Count Segments: Methodology and Numerical Study
• Factors Affecting Minimum Number of Probes Required for Reliable Estimation of Travel Time
• Freeway Detector Assessment: Aggregate Data from Remote Traffic Microwave Sensor
• Improved Dual-Loop Detection System for Collecting Real-Time Truck Data
• Monitoring and Predicting Freeway Travel Time Reliability: Using Width and Skew of Day-to-Day Travel Time Distribution
• Reality-Based Approach to Stated Preference Surveys
• Traffic Measurement and Vehicle Classification with Single Magnetic Sensor
• Trip-Chaining Trends in the United States: Understanding Travel Behavior for Policy Making
• Two Minutes per Person per Day Each Year: Exploration of Growth in Travel Time Expenditures
• Vehicle Classification Model for Loop Detectors Using Neural Networks