3x the traffic with self-driving cars, according to Ark Analyst. NCHRP studying issues too.

Above: Ark Analyst Tasha Keeney explains why autonomous vehicles could increase traffic threefold by 2030 (Youtube: Ark Invest)

Tesla’s true ace in the hole is its Autopilot hardware, now included on all its new vehicles. Tesla will be able to implement ever-more-capable self-driving capabilities as they become available — something no other automaker can do. Furthermore, its fleet of vehicles is generating an enormous trove of data. This data is not only valuable to Tesla as it improves its Autopilot system, but it could also have a substantial cash value — one that ARK believes has not been considered by stock analysts.

Above: Autonomous taxis could add roughly $2.3 trillion to annual US economic output by 2035 (Image: Huffington Post via Ark Invest)

Meanwhile, the National Academies’ Transportation Research Board has the following underway:

NCHRP 20-102(13)

Planning Data Needs and Collection Techniques for CV/AV Applications
NCHRP 20-102 (Impacts of Connected Vehicles and Automated Vehicles on State and Local Transportation Agencies–Task-Order Support) ]

  Project Data
Funds: $250,000
Staff Responsibility: TBD
Fiscal Year: 2017
BACKGROUND

Note: While similar to Task 14, this problem statement focuses on data for planning purposes.

As owners and operators of transportation infrastructure, state and local agencies maintain databases of relevant information. Currently, this includes travel survey records, traffic counts, crash records, design “as built” plan sets, construction schedules, and many more. CV/AV applications need certain information about the environment and infrastructure in a variety of time scales, and information about the potential impact of CV/AV on future transportation. Household travel surveys are administered on a regular basis in many MPOs, and in several states. However, no information about the potential impact that CV/AV would have on future travel is available from these surveys, and no consensus exists about standards to collect information in this area. Similarly, some AV developers are currently storing detailed digital 3-D maps for reference during automated driving. Perhaps such an asset of a public agency could be valuable to many applications, but this requires maintenance. Some agencies provide access to various sets of information electronically, others are available through records requests, and yet others are not available at all. Agencies vary widely in their ability to provide access to certain information now and in the future. There is a need to identify the information that is necessary for agencies to maintain to plan, enable, and enhance CV/AV applications; develop standard formats and standard systems where they would be helpful and do not already exist; and provide guidance for agencies on how to implement strategies for collecting, updating, maintaining, and disseminating the information.

Similarly, a variety of information about travel conditions can be collected by CV/AV enabled vehicles and can be shared with agencies to enhance their operations. Agencies currently struggle to collect good information about origin-destination flows, traffic volumes, travel delays, pavement surface quality, crash and anomaly location, and location of work zones, among others. There is a need to identify standards for collection of this information, how it is communicated to agencies, stored, maintained, updated, and eventually used to enhance transportation planning, operations, and maintenance.

The Safety Pilot Model Deployment and the upcoming additional CV pilot deployments will continue to contribute valuable information on the design and implementation of management systems for dissemination of agency-owned data and ingestion of CV/AV generated information for agency operations. The scalability of these systems needs to be estimated in this research as the penetration level of CV/AV technology advances from several thousand vehicles to several millions. Similarly, each CV pilot deployment will only deploy a small subset of the 50+ envisioned applications. Scalability of the back-end system to eventually accommodate up to 50 applications will also need to be explored in this research.  The objective of this research is to develop guidance on data collection and management strategies for the planning needs of typical agencies. This research will be coordinated with Task 14 that is looking at the operational realm.

The research team will define the data sensitive to the deployment of CVs and AVs that is needed by transportation planning organizations across the spectrum of planning applications. The team will describe promising approaches to forecasting that data and likely sources for the data underlying those forecasting approaches. Useful private sector sources of the underlying data will be described as well as obstacles to their use. A catalog of recommended stated preference questions and collection methods will be developed to allow market acceptance of these technologies to be charted over time and over different regions. Scenarios for typical agencies at state, regional, and local levels will be developed as examples for data management recommendations (including ingesting, storing, and using this data). The team will (a) review existing standards, formats, and commonly used technologies and (b) develop recommendations for harmonizing standards, developing dissemination and data collection systems or approaches, and ways of maintaining the information that is disseminated and using the data that is collected over time. Maintenance of the information over time is the critical component of the research and the recommendations. These tasks also should identify data availability policies and methods to address privacy and security concerns while not compromising the value of the information collected from CV/AV enabled vehicles.   To create a link to this page, use this URL: http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4257

NCHRP 20-102(14) [Anticipated]

Data Management Strategies for CV/AV Applications for Operations
NCHRP 20-102 (Impacts of Connected Vehicles and Automated Vehicles on State and Local Transportation Agencies–Task-Order Support) ]

  Project Data
Funds: $250,000
Staff Responsibility: B. Ray Derr
Fiscal Year: 2017
BACKGROUND

Note: While similar to Task 13, this problem statement focuses on data for operations purposes.

As owners and operators of transportation infrastructure, state and local agencies maintain databases of relevant information. Currently, this includes crash records, design “as built” plan sets, traffic signal timing parameters, construction schedules, and many more. CV/AV applications need certain information about the environment and infrastructure in a variety of time scales. Signal timing status is obviously needed in real time, traffic sign placements might be updated daily, and the next month’s construction projects might be updated weekly. Some AV developers are currently storing detailed digital 3-D maps for reference during automated driving. Perhaps such an asset of a public agency could be valuable to many applications, but this requires maintenance. Some agencies provide access to various sets of information electronically, others are available through records requests, and yet others are not available at all. Agencies vary widely in their ability to provide access to certain information now and in the future. There is a need to identify the information that is necessary for agencies to maintain to plan, enable, and enhance CV/AV applications; develop standard formats and standard systems where they would be helpful and do not already exist; and provide guidance for agencies on how to implement strategies for collecting, updating, maintaining, and disseminating the information.
Similarly, a variety of information about travel conditions can be collected by CV/AV enabled vehicles and can be shared with agencies to enhance their operations. Agencies currently struggle to collect good information about origin-destination flows, traffic volumes, travel delays, pavement surface quality, crash and anomaly location, and location of work zones, among others. There is a need to identify standards for collection of this information, how it is communicated to agencies, stored, maintained, updated, and eventually used to enhance transportation planning, operations, and maintenance.

The Safety Pilot Model Deployment and the upcoming additional CV pilot deployments will continue to contribute valuable information on the design and implementation of management systems for dissemination of agency-owned data and ingestion of CV/AV generated information for agency operations. The scalability of these systems needs to be estimated in this research as the penetration level of CV/AV technology advances from several thousand vehicles to several millions. Similarly, each CV pilot deployment will only deploy a small subset of the 50+ envisioned applications. Scalability of the back-end system to eventually accommodate up to 50 applications will also need to be explored in this research.

OBJECTIVE

The objective of this research is to develop guidance on operational data management strategies for typical agencies. This research will be coordinated with Task 13 that is looking at the planning realm.

The research team will summarize CV and AV applications that require information from public agencies at various time scales and develop recommended strategies for agencies to update, maintain, and make this information available to CV/AV applications. The research team will review previous work such as the CV Pooled Fund Study report on impacts of CV data on TMCs. Similarly, the research team will summarize CV and AV applications that can provide important information to public agencies at various time scales and develop recommended strategies for agencies to ingest, store, and use this data. Scenarios for typical agencies at state, regional, and local levels will be developed as examples for data management recommendations. A public sector task force will be established to provide feedback on project direction. The team will review existing standards, formats, and commonly used technologies and develop recommendations for harmonizing standards; developing dissemination and data collection systems or approaches; and approaches to maintaining the information that is disseminated and using the data that is collected over time. Maintenance of the information over time is the critical component of the research and the recommendations. These tasks should also identify data availability policies and methods to address privacy and security concerns while not compromising the value of the information collected from CV/AV enabled vehicles.

To create a link to this page, use this URL: http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4258

NCHRP 20-102(15) [Anticipated]

Understanding the Impacts of the Physical Highway Infrastructure Caused By the Increased Prevalence of Advanced Vehicle Technologies
NCHRP 20-102 (Impacts of Connected Vehicles and Automated Vehicles on State and Local Transportation Agencies–Task-Order Support) ]

  Project Data
Source: AASHTO Highway Subcommittee on Traffic Engineering
Funds: $650,000
Staff Responsibility: B. Ray Derr
Fiscal Year: 2018
This project has been tentatively selected and a project statement (request for proposals) is expected in . The project statement will be available on this world wide web site. The problem statement below will be the starting point for a panel of experts to develop the project statement.
Vehicle technologies are advancing faster than ever and there is a growing need to better understand how and when the traditional infrastructure will be impacted. Some agencies are starting to question the value of maintaining signs, roadside hardware, and other key physical highway infrastructure (because they might not be needed in the future). While there may be a day in the distant future when vehicles can navigate without any physical guidance, until then, physical guidance has to serve both the human driver and the machine driver. This physical guidance is mostly provided by traffic control devices. Traffic control devices have a long history of research, development, and testing to ensure that human road users can navigate anywhere within the US without having to relearn traffic codes. The MUTCD has evolved over the last 80 years to define a system in which agencies uniformly apply traffic control devices. All of the research, testing, and application guidance provided through the MUTCD has focused exclusively on the human driver. There is a growing and urgent need to assess how traffic control device designs and applications can be modified to accommodate both the human driver and the machine driver.
For the past three years, the TRB Automated Vehicle Symposium has hosted breakout sessions focused on the highway infrastructure. These breakout sessions have been attended by a mix of representation from transportation agencies and the automotive industries. A recurring discussion at these meetings has been related to the need to reassess traffic control device design and application with machine driver concepts in mind. For the past two years, vehicle industry experts have reported examples from their real-world demonstrations of how the existing traffic control device designs and applications appear non-uniform from their perspective and could be improved. According to the available research, there are significant safety and mobility benefits to be gained from advanced vehicle technologies that are not likely to be achieved from other sources.
The overall objective of this research is to assess how elements of the physical highway infrastructure (with an emphasis on traffic control devices) can be designed, enhanced, and/or applied to meet the needs of both the human driver and the machine driver. The research team should work with AV system developers to understand their technologies and processes for handling traffic control devices, including traffic control devices within work zones. The research team should also engage vendors of traffic control device materials regarding potential enhancements and feasibility of new product development. Existing research and innovations in retroreflectivity and other characteristics in traffic control devices should be included. The deliverables should include a detailed assessment of the current challenges as seen from the AV system developers as well as the highway owners and operators, suggestions to overcome those challenges with specific examples, implementation suggestions, and a roadmap outlining additional research, analysis, and milestones.
 To create a link to this page, use this URL: http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4377

From Engadget 

Uber Movement’s traffic data is now available to the public

Back in January, Uber announced that it’s giving urban planners access to a website with traffic data of their cities. Now that website is out of beta, and anybody can access it anytime. The Uber Movement website can show you how long it takes to get from one part of a city to another based on the day of the week and the time of day. People like you and me can consult it for realistic travel times, since its data came from actual Uber trips. However, its real purpose is to help city officials and planners figure out how to improve their transit systems.

Despite its good intentions and the anonymized data, the project wasn’t met with the warm reception Uber expected. The company has a pretty bad track record when it comes to privacy, after all. If you’ll recall, the New York Attorney General’s office discovered a few years ago that Uber’s corporate employees could track passengers’ rides and logs of their trips through the “God View” app. Uber had to purge riders’ identifiable info from its system and limit the app’s access to settle the probe. More recently, the ride-hailing firm had to change an app setting that tracked customers five minutes after their rides end after pressure from privacy groups.

At this point in time, Uber Movement only has data on Boston, Washington DC, Manila and Sydney. If it doesn’t want to put the project in jeopardy — because it will add more cities in the future — it has to be very, very careful with the data it collects.

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