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PhD Defense by Wenwen Zhang

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THE SCHOOL OF CITY AND REGIONAL PLANNING


GEORGIA INSTITUTE OF TECHNOLOGY

Under the provisions of the regulations for the degree

DOCTOR OF PHILOSOPHY

on Tuesday, June 29, 2016
9:30 – 11:30 AM
in CGIS Conference Room

will be held the

DISSERTATION PROPOSAL DEFENSE
 
for
 
Wenwen Zhang

"Interactions between Land Use and Transportation in the Era of Shared Autonomous Vehicles: a Discrete Event Simulation Model"
 
The Examiners Are:

Dr. Subhrjia Guhathakurta, Chairperson
Dr. Steven French
Dr. Ram Pendyala, School of Civil Engineering
Dr. Richard Fujimoto, School of Computational Science & Engineering
Dr. Bistra Dilkina, School of Computational Science & Engineering
 
Faculty and students are invited to attend this examination.


Abstract:

We are on the cusp of a new era in mobility given that the enabling technologies for autonomous vehicles (AVs) are almost ready for deployment and testing. This promising technology, once married with the sharing economy is set to give birth to a new travel mode – Shared Autonomous Vehicles (SAVs), a taxi service without drivers. Recent studies have explored the feasibility, affordability, environmental benefits, and parking demand of the system in hypothetical grid-base cities. Despite these rapidly proliferating studies, to date, it remains unclear how this affordable and environmentally friendly travel mode will interact with existing land uses in the city and vice versa. This work attempts to fill in these gaps by answering the following research questions:

·             How much parking land use will we need and where will it need to be located after the introduction of SAV system?

·             How will an SAV system influence residential location choices?

·             How will the performance of the system vary given different land use patterns?

This dissertation addresses the above research questions by simulating the operation of SAVs in the City of Atlanta, using the real transportation network with calibrated link-level travel speeds, travel demand origin-destination matrix, and synthesized household profiles. This real world data-driven SAV model will be used to determine spatial distributions of parking demand under different scenarios, including different parking price policies, average waiting time minimization and energy consumption optimization. The results can provide information to support amendments to existing parking land use policies in the future.

The study will explore the second question by monitoring the user experiences, such as average waiting time, trip matching experience, and average travel cost, at different parts of the city, such as downtown and fringe areas. The results can provide implications for future residential location choices, which may induce changes in land use development pattern. The third question will be evaluated by comparing the performance of the SAV system, including the operation costs and service quality, across different urban layout scenarios to provide suggestions for potential land use adaptations to embrace the coming of SAVs. 

Status

  • Workflow Status:Published
  • Created By:Tatianna Richardson
  • Created:06/10/2016
  • Modified By:Fletcher Moore
  • Modified:10/07/2016

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