Modeling and understanding the implications of future truck technology scenarios for performance-based freight corridor planning

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Smith, Denise A.
Southworth, Frank
Amekudzi-Kennedy, Adjo A.
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Autonomous highway vehicles are coming. The question regarding this technology has shifted from “if” to “when”. Many advocates predict that autonomous trucks, in particular, will be commercially available within the next decade, and perhaps even before autonomous passenger vehicles. This includes the emergence of autonomous and connected multi-vehicle truck platoons. Unfortunately, this technology is developing more rapidly than the public sector is preparing for it; the situation is exacerbated by the fact that the timeframe for which the technology is expected to make up a substantial portion of the motor vehicle fleet is within the current planning horizon of most transportation planning agencies. Thus, there is an immediate need to explore the implications of this technology for public agency planning purposes; exploring these implications will in turn require the development of tools to quantify the potential costs and benefits involved. With these needs in mind, the objectives of this dissertation were to (1) develop a simulation modeling and performance measurement tool that incorporates autonomous and connected truck platooning technology into the long-range planning process, (2) demonstrate how this tool can be applied to a selected interstate corridor in Georgia (I-85 and I-285), and (3) develop a scenario planning framework that uses the results from the tool to guide policy development. The model consists of an iteratively linked, supply-demand equilibrium based multi-commodity and multi-vehicle class truck trip distribution and a highway traffic assignment model, requiring changes be made to the typical travel demand modeling process to capture the characteristics of platooning technology. The results from an empirical application of this model were then used to assess the safety-, economic-, congestion-, and emissions-related impacts of platooning technology. The model developed is flexible enough to allow for a number of variations in platooning details, and was supported by a multi-variable sensitivity analysis of key input variables. This sensitivity 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 (which would also help to alleviate a currently significant shortage of experienced truck drivers). Allowing the autonomous trucks to operate on a dedicated lane was found to tremendously reduce travel time and congestion for those trucks. However, the magnitude of cost savings depends on a variety of factors, including the deployment of platoons of different sizes, the potential for platoon-supported fuel savings, and the level of corridor traffic congestion. 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 for platoons were as much as 9.6% at optimal speeds. While these findings are insightful, it is important to note that they are based on a specific set of assumptions and do not consider infrastructure costs related to the implementation of the technology. 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 (OUE) 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. The OUE assignment 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, which is a widely used freight database within the United States. Given the uncertainty associated with platooning technology, there are a number of limitations associated with this research, and the final chapter of this dissertation discusses such limitations and presents opportunities for future work. As the details of platooning technology become clearer, tools such as the one developed here can assist transportation planners with incorporating such technological advances into their planning processes.
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