Possible futures for fully automated vehicles : using scenario planning and system dynamics to grapple with uncertainty

It is widely expected that fully automated vehicles (also commonly referred to as "driverless" or "self-driving" cars) will significantly change transportation systems in the United States and around the world. By reducing or eliminating many of the costs and disincentives of travel by automobile, these vehicles may have the potential to radically alter many of the inherent dynamics that have governed transportation systems since the advent of the automobile. To date, however, there has been very little structured analysis of these potential changes. Most of the existing literature addresses the technical challenges facing vehicle automation technology or considers immediate effects on the transportation system, usually analyzing single effects in isolation. Very little attention appears to have been paid to multiple simultaneous interactions that may occur across the transportation system and potential feedback effects that may arise among elements of the system. This thesis examines how the transportation system might react to the widespread introduction of fully automated vehicles (AVs), specifically considering how these reactions will affect total usage of automobiles, as measured by vehicle miles traveled (VMT). For the purpose of this thesis, the system boundary is broadly drawn-potential system responses are considered within the transportation system itself (consisting of existing users, vehicles, and infrastructure) and the "macro-system" (which includes broader economic, regulatory, social, and political dimensions). To address the wide range of uncertainties involved, scenario-planning techniques are used to develop and explore three scenarios that span a range of important variables. Within each scenario, system dynamics methodology is used to explore potential system reactions to the scenario assumptions and to consider the ultimate implications for VMT. The main insight from this analysis is that unstable responses (rapid movement to the extremes) appear more likely than steady transitions to "moderate" states. When the scenarios assume behavior can change substantially, the structure of the system suggests either that strong and growing forces will cause automobiles to become even more dominant over other modes than they are today (and VMT will rise dramatically), or public transit will become increasingly more appealing and assume a growing role (and VMT will drop substantially). The challenge of predicting the underlying behavioral changes is substantial: Who can say with any certainty how people will use a technology that provides point-to-point, self-directed, self-scheduled travel, with no requirement for attention or effort by a human occupant, potentially at higher speeds, in greater comfort, and with safer operation than today's automobiles? There are simply not enough existing data and no precedent for such analysis. Given the potential for unstable outcomes, depending on the desired outcome, it may be critical for policy-makers to consider the initial conditions of AV deployment, as these may have a substantial impact on the transportation system over the long term.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering Systems Division.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Stanford, Joseph, S.M. (Joseph Marsh) Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Patrick Hale.

• Engineering Systems Division.
• System Design and Management Program.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/105319

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/105319