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原文传递 Travel Effects and Associated Greenhouse Gas Emissions of Automated Vehicles.

题名：	Travel Effects and Associated Greenhouse Gas Emissions of Automated Vehicles.
作者：	Rodier, C.
关键词：	Horse and cart transportation, Gas emission
摘要：	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. The manner in which automated vehicles are integrated into our regional transportation systems could have significant negative and positive effects on congestion, vehicle miles traveled (VMT), greenhouse gas emissions (GHGs), energy consumption, and land development patterns. For example, one study estimates that automated vehicles could double GHG emissions and energy consumption or reduce it by 50%, depending on the magnitude of different travel demand effects (Wadud et al., 2016). Understanding the potential impacts of automated vehicles is critical to guiding their adoption in ways that improve multi-modal accessibility for all citizens and minimize negative environmental effects. The challenge, of course, is that fully automated vehicles have not yet been introduced into the transportation system and thus observed data is not available on how travelers will adopt and respond. 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. In section II, 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. In section III, we describe the results of scenario modeling studies. Scenarios commonly include fleets of personal automated vehicles and automated taxis with and without sharing that are fully operational without a driver (i.e., level 5 automation). 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. In section IV, the results of the review are synthesized to identify the magnitude and strength of the evidence for the effects, lessons learned, and research gaps.
报告类型：	科技报告