01025655

Component

https://doi.org/10.3141/1968-05

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

Two trip-forecasting approaches—neural networks and support vector machines—are compared for a multiple-station shared-use vehicle system. The neural networks trained to perform trip forecasting belong to the multilayer perceptron model. Comparative evaluation was made with least-squares support vector machines with the radial basis kernel function. The forecasting models were trained or developed for 6 months, then validated with 1 month of actual trip data from the Honda Intelligent Community Vehicle System, currently in commercial operation in Singapore. Each model was designed to forecast the net flow of vehicles for a 3-h period on any day in a month at a particular shared-use vehicle port. The models were developed for and applied to forecasting the net flow at each port for each entire month from January 2004 to June 2004. Results indicate that the multilayer perceptron model has a slightly better forecast accuracy in terms of monthly average absolute error and monthly maximum absolute error.

01037954

English

pp 40-46

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099773

Print

Figures (3) ; References (22) ; Tables (2)

Accuracy; Forecasting; Neural networks; Validation

Honda Intelligent Community Vehicle System (Singapore)

Multilayer perceptron model; Shared use vehicle trips; Support vector machines

Singapore

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Mar 3 2006 10:41AM

• Application of Genetic Neural Networks to Real-Time Intersection Accident Detection Using Acoustic Signals
• Applications of Artificial Intelligence Paradigms to Decision Support in Real-Time Traffic Management
• 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