Travel Modeling in an Era of Connected and Automated Transportation Systems: An Investigation in the Dallas-Fort Worth Area

The North Central Texas Council of Governments (NCTCOG) engaged the Data-Supported Transportation Operations and Planning Center (D-STOP) to conduct a planned four-year study to analyze the status and progress of connected/autonomous vehicle (CAV) development, determine what the wide-ranging effects of the technology’s adoption in North Central Texas, and, ultimately, begin constructing scenarios and methods to account for these effects in long range planning. Part I begins by examining the state of technology for both AVs and CVs and provides evidence that the discrete technologies to enable both vehicle capabilities are nearing market readiness. The paper also draws a contrast between the two technologies as they are each being developed in response to distinct factors. Finally, Part I examines certain policy, privacy, and security questions. Part II looks at CAV adoption and finds that there will likely be decades of mixed use between AVs and human-driven vehicles. In addition, this section discusses existing adoption predictions from private consultants and academics, provides adoption estimates of CAVs based on adoption rates of similar technologies in the past, and proposes assumptions for three planning scenarios. Although the implementation timeline is highly uncertain, the market is susceptible to certain disruptors (such as ridesharing) that could significantly affect AV adoption. Finally, Part III describes the approach followed in order to propose 112 potential planning scenarios to reflect the wide range of potential CAV impacts. The proposed scenarios are built based on the analysis of possible adoption timelines for vehicle automation and connectivity, and consider the impact of additional behavioral and technological factors, using existent regional planning methodologies. The limitations of traditional modeling tools may limit the observed impacts of CAVs, which can motivate the exploration of more advanced tools such as activity-based models and dynamic traffic assignment.

• Record URL:
  http://ctr.utexas.edu/wp-content/uploads/122.pdf
• 
• Summary URL:
  https://library.ctr.utexas.edu/Presto/catalogid=34882
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/32602
• Supplemental Notes:
  • This research was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Data-Supported Transportation Operations and Planning Center

  University of Texas at Austin
  Austin, TX  United States  78701

  Research and Innovative Technology Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Kuhr, James
  • Juri, Natalia Ruiz
  • Bhat, Chandra R
  • 0000-0002-0715-8121
  • Archer, Jackson
  • Duthie, Jennifer
  • Varela, Edgar
  • Zalawadia, Maitri
  • Bamonte, Thomas
  • Mirzaei, Arash
  • Zheng, Hong
• Publication Date: 2017-2

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Technical Report
• Features: Appendices; Figures; Maps; Photos; References; Tables;
• Pagination: 61p

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Computer models; Connected vehicles; Forecasting; Impact studies; Intelligent vehicles; Long range planning; Market assessment
• Geographic Terms: Dallas-Fort Worth Metropolitan Area
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01643860
• Record Type: Publication
• Report/Paper Numbers: D-STOP/2017/122, Report 122
• Contract Numbers: DTRT13-G-UTC58
• Files: UTC, NTL, TRIS, RITA, ATRI, USDOT
• Created Date: Aug 21 2017 11:15AM