Uncovering the spatiotemporal patterns of CO 2 emissions by taxis based on Individuals' daily travel (original) (raw)
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arXiv:1809.10834, 2018
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Given the necessity to understand the modal shift potentials at the level of individual travel times, emissions, and physically active travel distances, there is a need for accurately computing such potentials from disaggregated data collection. Despite significant development in data collection technology, especially by utilizing smartphones, there are limited efforts in developing useful computational frameworks for this purpose. First, development of a computational framework requires longitudinal data collection of revealed travel behavior of individuals. Second, such a computational framework should enable scalable analysis of time-relevant low-carbon travel alternatives in the target region. To this end, this research presents an open-source computational framework, developed to explore the potential for shifting from private car to lower-carbon travel alternatives. In comparison to previous development, our computational framework estimates and illustrates the changes in trav...
Analisys of taxi data for understanding urban dynamics
2016
This study is the result of a long and difficult journey spanning several years, and was not possible without the valuable contribution of various people and institutions, which I would like to thank. Firstly, I would like to express my gratitude to my supervisors: Prof. Carlos Bento and Prof. Santi Phithakkitnukoon. Their guidance, expertise, and motivation were fundamental throughout the entire project. They have spare considerable amounts of their personal time to discuss and review my work. They provided solutions and guidance when they were most needed, from setting the initial goals, all the way to the final validation. And above all, they have believed in me and my work, and have been at my side throughout this entire academic path. No contribution or finding produced from this study was possible without the richness of the data set and the knowledge it possesses. Therefore, data providers are a key player in this study-as they are on every research. Besides collecting and providing data, data providers were available to share their knowledge and assist during the initial interpretation of the data sets. I would like to thank all the data providers that made this study possible: Geotaxi, TMN (currently rebranded as MEO),