Energy and Environmental Impact of Truck-Only Lanes: A Case Study of Interstate 75 between Macon and McDonough, Georgia

Investigators: 

Dr. Angshuman Guin, Georgia Tech     

Dr. Michael O. Rodgers, Georgia Tech

Daejin Kim, Ph.D. Student, Georgia Tech

Project Overview:

Continuous increase in freight truck demand on urban highways has been a challenging issue for transportation planners. The heavy mixture of passenger cars and trucks has caused various serious problems such as traffic congestion, fatal accidents, and emissions. Some special treatments such as truck-only lanes have gained growing interest as one of the potential alternatives that can alleviate the problems. In this context, the Georgia Department of Transportation has recently announced its $2 billion project proposal of creating truck-only lanes along an at least 40-mile stretch of Interstate 75 (I-75) northbound. However, few in-depth studies on this project have been conducted. Thus, this research primarily aims to evaluate the effects of the proposed truck-only lanes on the I-75 corridor with a focus on their environmental impact. A state-of-the-art tool combining microscopic traffic simulation and emission modeling used in this research is expected to provide notable results that help transportation policy-makers make informed decisions on the truck-only lanes.

To this aim, the Georgia Tech Team has built a baseline network on VISSIM simulator that covers the I-75 corridor and its connectors. The team gathered traffic count data for each connecting link from Georgia traffic counts website (http://geocounts.com/gdot/). Based on the data, the team generated the vehicle inputs by time of day and type of vehicles that will be input in the VISSIM models. The team currently has validated the baseline VISSIM simulation model while building an alternative network for the truck-only lanes. Once completed, the team will integrate the VISSIM simulation models with MOVES-Matrix to calculate second-by-second emissions and fuel consumption of individual vehicles on this network for each scenario.