Adapting Land Use and Infrastructure for Automated Driving

Center for Connected and Automated Transportation/Research/Research from the University of Michigan/Adapting Land Use and Infrastructure for Automated Driving

Adapting Land Use and Infrastructure for Automated Driving

Complete
Headshot of Yafeng Yin
Yafeng Yin
Headshot of Srinivas Peeta. The link directs to their bio page.
Srinivas Peeta
The University of Michigan Logo. The link directs to the funded research led by this institution.
Purdue University Logo. The link directs to the funded research led by this institution.

Principal Investigator(s):

Yafeng Yin, Professor of Civil and Environmental Engineering – The University of Michigan
Professor of Industrial and Operations Engineering – The University of Michigan
Srinivas Peeta, Hockema Professor of Civil Engineering – Purdue University

Project Abstract:
This project is concerned with adapting land use and transportation infrastructure for automated driving. Autonomous vehicles will likely yield a transformation of urban form, its land use and mobility system. We propose to establish quantitative modeling frameworks to analyze these impacts and implications. The frameworks will provide a quantifiable understanding of the tradeoffs, and reveal the underlying mechanism and identify key parameters that could shape the future of mobility systems and urban land use. Moreover, the proposed modeling frameworks will aid planning agencies with infrastructure adaptation planning and optimize a roadmap for shaping highway infrastructure towards automated mobility.

Institution(s): University of Michigan – Ann Arbor
Purdue University

Award Year: 2018

Research Thrust(s): Policy & Planning

Project Form(s):

Project Information Form
Final Report Part A
Final Report Part B