A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Pairwise classification and support vector machines
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Predicting haptic data with support vector regression for telepresence applications
Design and application of hybrid intelligent systems
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Context-Sensitive kernel functions: a distance function viewpoint
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Travel-time prediction with support vector regression
IEEE Transactions on Intelligent Transportation Systems
Pedestrian detection and tracking with night vision
IEEE Transactions on Intelligent Transportation Systems
Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
Combination of Feature Extraction Methods for SVM Pedestrian Detection
IEEE Transactions on Intelligent Transportation Systems
Ensemble of Multiple Pedestrian Representations
IEEE Transactions on Intelligent Transportation Systems
Connectivity statistics of store-and-forward intervehicle communication
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Computers and Electrical Engineering
| Hi-index | 0.00 |
This paper presents a framework for real-time highway traffic condition assessment using vehicle kinetic information, which is likely to be made available from vehicle-infrastructure integration (VII) systems, in which vehicle and infrastructure agents communicate to improve mobility and safety. In the proposed VII framework, the vehicle onboard equipment and roadside units (RSUs) collaboratively work, supported by an artificial intelligence (AI) paradigm, to determine the occurrence and characteristics of an incident. Two AI paradigms are examined: 1) support vector machines (SVMs) and 2) artificial neural networks (ANNs). Each RSU then assesses the traffic condition based on the information from multiple vehicles traveling on its supervised highway segment. As a case study, this paper developed a model of the VII-SVM framework and evaluated its performance in a microscopic traffic simulation environment for a highway network in Spartanburg, SC. The performance of the VII-SVM was compared with the performance of the corresponding VII-ANN framework, and both frameworks were found to be capable of classifying the travel experience using the kinetic data generated by each vehicle. The performance of the VII-SVM framework, in terms of its detection rate, false-alarm rate, and detection times, was also found to be superior to a baseline California-type incident-detection algorithm. Moreover, the framework provided additional information, including an estimate of the incident location and the likely number of lanes blocked, which will be helpful for implementing an appropriate response strategy. The proposed VII-AI framework thus provides a reliable alternative to traditional traffic sensors in assessing traffic conditions.