01020273

Component

https://doi.org/10.3141/1968-11

http://scholar.google.com...vsar&publication_year=2006

Decision support for real-time traffic management is a critical component for the success of intelligent transportation systems. Theoretically, microscopic simulation models can be used to evaluate traffic management strategies in real time before a course of action is recommended. However, the problem is that the strategies would have to be evaluated in real time; this might not be computationally feasible for large-scale networks and complex simulation models. To address this problem, two artificial intelligence (AI) paradigms—support vector regression (SVR) and case-based reasoning (CBR)—are presented as alternatives to the simulation models as a decision support tool. Specifically, prototype SVR and CBR decision support tools are developed and used to evaluate the likely impacts of implementing diversion strategies in response to incidents on a highway network in Anderson, South Carolina. The performances of the two prototypes are then evaluated by a comparison of their predictions of traffic conditions with those obtained from VISSIM, a microscopic simulation model. Although the prototype systems’ predictions were comparable to those obtained by simulation, their run times were only fractions of the time required by the simulation model. Moreover, SVR performance is superior to that of CBR for most cases considered. The study results provide motivation for consideration of the proposed AI paradigms as potential decision support tools for real-time transportation management applications.

01037954

English

pp 92-98

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099773

Print

Figures (5) ; References (20) ; Tables (1)

Artificial intelligence; Case based reasoning; Decision support systems; Intelligent transportation systems; Microsimulation; Prototypes; Real time information; Traffic congestion; Traffic control; Traffic diversion

VISSIM (Computer model)

Support vector regression

Anderson (South Carolina)

Bridges and other structures; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Mar 3 2006 11:07AM

• Application of Genetic Neural Networks to Real-Time Intersection Accident Detection Using Acoustic Signals
• Forecasting Shared-Use Vehicle Trips with Neural Networks and Support Vector Machines
• Frameworks to Represent Uncertainty When Level of Service Is Determined
• Improving the Prediction of Annual Average Daily Traffic for Nonfreeway Facilities by Applying a Spatial Statistical Method
• Incorporating Wavelet Decomposition Technique to Compress TransGuide Intelligent Transportation System Data
• Kalman Filter-Based Tracking System for Automated Inventory of Roadway Signs
• Moments Analysis for Improving Decision Reliability Based on Travel Time
• Performance Evaluation of Sensor- and Image-Based Technologies for Automated Pavement Condition Surveys
• Predicting Directional Design Hourly Volume from Statutory Holiday Traffic
• Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters
• Real-Time Detection of Crash-Prone Conditions at Freeway High-Crash Locations
• Travel Time Estimation with Correlation Analysis of Single-Loop Detector Data
• Unscented Kalman Filter Method for Speed Estimation Using Single Loop Detector Data