Synthesizing neighborhood preferences for automated vehicles

Automated Vehicles (AVs) have gained substantial attention in recent years as the technology has matured. Researchers and policymakers envision that AV deployment will change transportation, development patterns, and other urban systems. Researchers have examined AVs and their potential impacts with two methods: (1) survey-based studies of AV preferences and (2) simulation-based estimation of secondary impacts of varied AV deployment strategies, such as Shared AVs (SAVs) and Privately-owned AVs (PAVs). While the preference survey literature can inform AV simulation studies, preference study results have so far not been integrated into simulation-based research. This lack of integration stems from the absence of data that measure preferences towards PAVs and SAVs at the neighborhood level. Existing preference studies usually investigate adoption likelihood without collecting appropriate information to link preferences to precise locations or neighborhoods. This study develops a microsimulation approach, incorporating machine learning and population synthesizing, to fill this data gap, leveraging a national AV perception survey (NAVPS) and the latest National Household Travel Survey (NHTS) data. The model is applied to San Francisco, CA, and Austin, TX, to test the concept. The authors validate the proposed model by comparing the spatial distributions of synthesized ride-hailing users and observed ride-hailing trips. High correlations between the authors' synthesized user density and empirical trip distributions in two study areas, to some extent, verify their proposed modeling approach.

• Record URL:
  https://doi.org/10.1016/j.trc.2020.102774
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X20306847
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2020 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Wenwen
  • Wang, Kaidi
  • Wang, Sicheng
  • Jiang, Zhiqiu
  • Mondschein, Andrew
  • Noland, Robert B
• Publication Date: 2020-11

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 120
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Attitudes; Autonomous vehicles; Machine learning; Microsimulation; Neighborhoods; Population; Ridesharing; Ridesourcing; Stated preferences; Travel surveys; Vehicle sharing
• Geographic Terms: Austin (Texas); San Francisco (California)
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01754552
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 13 2020 9:17AM