(Autonomous vehicles: Use cases)
(Autonomous vehicles: Use cases)
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==Autonomous vehicles: Use cases==
 
==Autonomous vehicles: Use cases==
 
See [[Autonomous vehicles: use cases|Use cases]] for more information.
 
See [[Autonomous vehicles: use cases|Use cases]] for more information.
 
 
[[Content Charrette: Autonomous vehicles]]
 
  
 
==Autonomous vehicles: Early applications==
 
==Autonomous vehicles: Early applications==

Revision as of 21:05, 8 November 2017


Introduction

Connected and autonomous vehicles (CAV) are a rapidly evolving mode of travel, and one of several emerging megatrends in transport planning that will profoundly change travel behavior and patterns over the coming decades. There are several CAV definitions in the literature and press. We follow the SAE definitions, which include six levels of automation ranging from none at all to fully connected and autonomous vehicles, with the latter including a high level of system-level control of the transport network. The discussion that follows assumes a fully automated future (SAE level 5), which we make explicit with the acronym CAV5. Some of the concepts discussed may be applicable to lower levels of automation, but the focus here is upon CAV5 futures.

CAVs will influence all types of travel, from local commuting to long-distance travel. The discussion that follows applies to personal travel, primarily in the metropolitan settings. The parallel advances in freight and commercial CAVs are not addressed here, nor are related technologies such as delivery drones or hyperloops. The design of specific models is not addressed in this discussion. Rather, the context, behavioral foundations, modeling mindsets and methods, success stories from elsewhere are profiled.

Simulating CAV5 effects will require fundamentally rethinking how most transport planning models are structured and applied. There are some effects that can be represented using traditional models, but the majority of likely behavioral changes will affect all traveler preferences, choices, and costs. Many of these will be second or higher-order effects that will be realized through feedback loops or replanning. Discussions about the policy and planning context, major behavioral changes, and modeling frameworks are described in the sections linked to below. Case studies of several evolving models are also presented. The reader should be able to grasp the appropriate modeling approaches in light of their requirements.

Autonomous vehicles: Planning and policy context

A wide variety of questions about CAV futures are being posed by policymakers. A number of policy papers have been published on the topic, but they describe the problem space much better than specifying forecasting approaches. A number of policy issues and strategies identified in NCHRP Report 845 are summarized on the planning and policy context page. Other issues identified in other reports have been added to the discussion.

Autonomous vehicles: Behavioral considerations

The availability of AVs, either as privately-owned, shared, or hired vehicles, will open a range of travel possibilities not available today, and change many aspects of current modes. Time spent traveling in CAVs will be spent on other activities, reducing the disutility and cost of travel. Sharing an CAV or forgoing private auto ownership will likely become commonplace, further changing mobility patterns and creating new travel opportunities for young, elderly, and disabled persons. These and other behavioral considerations will form the basis for model specifications that include the full range of CAV5 impacts on travel behavior and network performance.

Autonomous vehicles: Modeling frameworks

The likely effects of CAVs will influence all aspects of travel behavior encompassed by travel forecasting models. This will necessitate a revolution in modeling frameworks suitable for measuring their extent and impacts. While the range of policy issues and strategies is long and varied there are few AV futures that are unlikely to influence all aspects of modeled behavior, to include long-term location choices, short-term travel choices (to include daily activity patterns and activity scheduling), and network assignment. While some can be accommodated within the traditional four-step modeling paradigm most will be better addressed using visioning and activity-based travel modeling frameworks.

Autonomous vehicles: Forecasting

see Forecasting for more information

Autonomous vehicles: Use cases

See Use cases for more information.

Autonomous vehicles: Early applications

A number of agencies have already incorporated CAV5 effects into their transport planning models and forecasts. They have been explicit from the outset in most visioning models. However, several agencies have begun explicitly representing them in travel forecasts for their region. None approach the ideal frameworks considered in our [[Autonomous vehicles:Forecasting|forecasting] section, but many have or are working towards some of them. The early applications profiled include recent innovations from the Puget Sound region, the San Francisco Bay Area, Atlanta, and the Canadian province of Ontario.

Further reading

A number of policymakers and planners have written white papers and reports describing how AVs might affect land use and travel behavior over the coming decades. Many of these were reviewed in the preparation of this and related pages in TFResource. Some cover the policy, planning, and legal aspects of CAVs, to include:

There is considerably less definitive guidance on how to include AVs in travel forecasting models. One emerging resource is the on-going NCHRP Project 20-102(09), which seeks to provide guidance on how trip-based, activity-based, and visioning models might evolve in order to incorporate travel changes due to CAVs. A large number of academic papers and research reports have been recently published or in progress, but most of them cannot be quickly implemented and capable of addressing the issues described in the pages linked above.

References

Content Charrette: Autonomous Vehicles

Subcategories

This category has only the following subcategory.