Big data fusion to estimate driving adoption behavior and urban fuel consumption
Name
1065524328-MIT.pdf
Description
Full printable version
Size
8.08 MB
Format
Adobe PDF
Checksum (MD5)
6c7bbad43c5d5081eb8cb68a4c4abb0b
Author(s)
Kalila, Adham
Advisor(s)
Marta C. González.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Data from mobile phones is constantly increasing in accuracy, quantity, and ubiquity. Methods that utilize such data in the field of transportation demand forecasting have been proposed and represent a welcome addition. We propose a framework that uses the resulting travel demand and computes fuel consumption. The model is calibrated for application on any range of car fuel efficiency and combined with other sources of data to produce urban fuel consumption estimates for the city of Riyadh as an application. Targeted traffic congestion reduction strategies are compared to random traffic reduction and the results indicate a factor of 2 improvement on fuel savings. Moreover, an agent-based innovation adoption model is used with a network of women from Call Detail Records to simulate the time at which women may adopt driving after the ban on females driving is lifted in Saudi Arabia. The resulting adoption rates are combined with fuel costs from simulating empty driver trips to forecast the fuel savings potential of such a historic policy change.
Description
Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 63-68).
Subjects
Civil and Environmental Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Persistent DSpace Link
