Road Weather Management Program
photos of lightning, trucks plowing snow, an empty road before a storm, and an evacuation
Office of Operations 21st century operations using 21st century technologies

Best Practices for Road Weather Management Version 2.0

Best Practices > Publications > Publication Titles

Title:

Travel Time Prediction in the Presence of Traffic Incidents using Different Types of Neural Networks

Abstract:

An Artificial Neural Network (ANN) is regarded as an excellent candidate to model complex traffic prediction problems. This research utilizes three different types of neural networks to model corridor travel time prediction in the presence of traffic incidents using data collected from a highway corridor in Northern Virginia. Traffic condition data, incident data, and weather data for the corridor were collected for consecutive twelve months. The weather information collected included visibility, precipitation, temperature, and weather type. Data fusion between the incident data set, traffic condition data set, and weather data set was performed. The performance of the three different neural networks are compared, and the results demonstrate that under some cases, one typology of neural network performs better, and under other cases, another typology is superior.

Source(s):

Transportation Research Board (TRB) Annual Meeting, University of Wisconsin at Madison and Environmental Systems Research Institute (ESRI). For an electronic copy of this resource, please direct your request to WeatherFeedback@dot.gov.

Date: 2006

Author:

Tao, Yang, Qiu, Ran

Keywords:


Mobility
Traffic modeling

PDF files can be viewed with the Acrobat® Reader®.
Office of Operations