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Travel Effects and Associated Greenhouse Gas Emissions of Automated Vehicles
  • Published Date:
    2018-04-01
  • Language:
    English
Filetype[PDF-876.22 KB]


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  • Resource Type:
  • Abstract:
    In much the same way that the automobile disrupted horse and cart transportation in the 20th century, automated vehicles hold the potential to disrupt our current system of transportation and the fabric of our built environment in the 21st century. Experts predict that vehicles could be fully automated by as early as 2025 or as late as 2035 (Underwood, 2015). The public sector is just beginning to understand automated vehicle technology and to grapple with how to accommodate it in our current transportation system. Research on automated vehicles is extremely important because automated vehicles may significantly disrupt our transportation system with potentially profound effects, both positive and negative, on our society and our environment. However, this research is very hard to do because fully automated vehicles have yet to travel on our roads. As a result, automated vehicle research is largely conducted by extrapolating effects from current observed behavior and drawing on theory and models. Both the magnitude of the mechanism of change and secondary effects are often uncertain. Moreover, the potential for improved safety in automated vehicles drive the mechanisms by which vehicle miles traveled (VMT), energy, and greenhouse gas (GHG) emissions may change. We really don’t know whether automated vehicles will achieve the level of safety that will allow for completely driverless cars, very short headways, smaller vehicles, lower fuel use, and/or reduce insurance cost. We don’t know whether automated vehicle fleets will be harmonized to reduce energy and GHG emissions. In this white paper, the available evidence on the travel and environmental effects of automated vehicles is critically reviewed to understand the potential magnitude and likelihood of estimated effects. We outline the mechanisms by which automated vehicles may change travel demand and review the available evidence on their significance and size. These mechanisms include increased roadway capacity, reduced travel time burden, change in monetary costs, parking and relocation travel, induced travel demand, new traveler groups, and energy effects. We then describe the results of scenario modeling studies. Scenarios commonly include fleets of personal automated vehicles and automated taxis with and without sharing. Travel and/or land use models are used to simulate the cumulative effects of scenarios. These models typically use travel activity data and detailed transportation networks to replicate current and predict future land use, traffic behavior, and/or vehicle activity in a real or hypothetical city or region. The findings from this white paper are summarized in the text and in Table A below.
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