*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
School of Civil and Environmental Engineering
Ph.D. Thesis Defense Announcement
Modeling and Understanding the Implications of Future Truck Technology Scenarios for
Performance-Based Freight Corridor Planning
By
Denise A. Smith
Advisors:
Dr. Frank Southworth (CEE) and Dr. Adjo Amekudzi-Kennedy (CEE)
Committee Members:
Dr. Ram Pendyala (CEE), Dr. Catherine Ross (COA/CEE), Dr. Michael Meyer (Parsons Brinckerhoff)
Date & Time: Tuesday, August 2, 2016, 1:30 PM
Location: Sustainable Education Building, 122
Autonomous highway vehicles are coming. Many advocates predict that autonomous trucks, in particular, will
be commercially available within the next decade. This includes autonomous and connected multi-vehicle truck
platoons. Unfortunately, this technology is developing more rapidly than the public sector is preparing for it: a
situation exacerbated by the fact that the expected arrival of the platoons is within the current planning horizon of
transportation planning agencies. Thus, there is a need to explore the implications of the technology for planning
purposes, which will require the development of tools to quantify potential costs and benefits. With these needs in
mind, the objectives of this thesis were to (1) develop a simulation modeling and performance measurement tool
which incorporates truck platooning technology, (2) demonstrate how this tool can be applied to the I-85 and I-285
corridor in Georgia, and (3) develop a scenario planning framework that uses the results from the tool to guide policy
development. The modeling tool consists of an iteratively linked, multi-commodity and multi-vehicle class truck trip
distribution and a traffic assignment model, requiring changes to the typical travel demand modeling process to
capture the characteristics of platooning technology. The results from an empirical application of this model were
used to assess the safety-, economic-, congestion-, and emissions-related impacts of platooning technology.
The model allowed for variations in platooning details through a multi-variable sensitivity analysis. This
analysis showed a range of costs and benefits of the technology, with the greatest benefits seen when labor costs were
cut by allowing some of the trucks to be driverless. Allowing the autonomous trucks to operate on a dedicated lane
was found to tremendously reduce travel time and congestion for those trucks. In some scenarios, these congestion
benefits came at the expense of the convenience of other vehicles, while in other scenarios, these vehicles experienced
modest congestion-reduction benefits. The emissions impacts varied; the benefits for fuel consumption and emissions
were as much as 9% at optimal speeds. While these findings are insightful, it is important to note that they are based
on a specific set of assumptions. Changing the assumptions in some cases could significantly change the results.
This research is one of the first efforts to modify a traditional travel demand model to simulate autonomous
truck platoons. One of the key components of this contribution is the use of an origin-user equilibrium traffic
assignment, a relatively new path-based assignment which allows the user to specify vehicle class and origin specific
traffic flows, and assign them to the network simultaneously, and which has yet to be explored in depth with respect
to multiple truck class-based, notably platoon-inclusive, freight movements. Additionally, the research presents a new
application of the Freight Analysis Framework, a widely used freight database within the United States. Given the
uncertainty associated with platooning technology, there are various limitations of this research. As the details of
platooning technology become clearer, tools such as the one developed here can help transportation planners better
incorporate such technological advances into their planning process.
