Home → News → Filling in the Gaps of Connected Car Data Helps Transportation... → Full Text

Filling in the Gaps of Connected Car Data Helps Transportation Planners

By Michigan Tech News

April 29, 2019

[article image]

A researcher at Michigan Technological University has developed a computational model that provides a more accurate representation of traffic in cities, which transportation planners can use for more efficient and cost-effective urban planning.

Kuilin Zhang, an assistant professor of civil and environmental engineering, and affiliated assistant professor of computer science, used two months of connected vehicle data from 2,800 cars to create a data-driven optimization approach to reconstruct the location-duration-path choices those cars make.

The reconstructed choices can be used to improve the validation and calibration of the models, which will in turn produce better estimates of travel demand, reduce congestion, decrease emissions, and save energy.

From Michigan Tech News
View Full Article


Abstracts Copyright © 2019 SmithBucklin, Washington, DC, USA


No entries found