Smart mobility : behavioral data collection and simulation
Author(s)
Padmanabha, Akshay
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Alternative title
Behavioral data collection and simulation
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Moshe Ben-Akiva.
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Metadata
Show full item recordAbstract
On-demand ridesharing services, such as Uber and Lyft, and autonomous vehicles are significantly changing the landscape of transportation and mobility. In light of these disruptions, we aim to determine consumer preferences with regards to transportation and use this data to simulate and analyze the urban effects of smart mobility solutions. We collect behavioral data using Future Mobility Sensing (FMS), a smartphone and prompted-recall-based integrated activity-travel survey, and create simulations using the data with SimMobility, a simulation platform that integrates various mobility-sensitive behavioral models with state-of-the-art scalable simulators to predict the impact of mobility demands on transportation networks, intelligent transportation services, and vehicular emissions. Enhancing these projects with on-demand preferences, individual patterns, and incentives as inputs, we aim to simulate and analyze a wide range of viable smart mobility solutions.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (page 45).
Date issued
2017Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.