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Article

Analysis of Potential Shift to Low-Carbon Urban Travel Modes: A Computational Framework Based on High-Resolution Smartphone Data

1
Department of Built Environment, Aalto University, 02150 Espoo, Finland
2
Department of Computer Science, Aalto University, 02150 Espoo, Finland
3
Department of Computer Science, University of Helsinki, 00560 Helsinki, Finland
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(15), 5901; https://doi.org/10.3390/su12155901
Received: 24 June 2020 / Revised: 10 July 2020 / Accepted: 13 July 2020 / Published: 22 July 2020
(This article belongs to the Section Sustainable Transportation)
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 travel time in relation to the potential reductions in emission and increases in physically active travel, as well as daily weather conditions. The potential usefulness of the framework was evaluated using long-term travel data of around a hundred travelers within the Helsinki Metropolitan Region, Finland. The case study outcomes also suggest that in several cases traveling by public transport or bike would not increase travel time compared to the observed car travel. Based on the case study results, we discuss potentially acceptable travel times for mode shift, and usefulness of the computational framework for decisions regarding transition to sustainable urban mobility systems. Finally, we discuss limitations and lessons learned for data collection and further development of similar computational frameworks. View Full-Text
Keywords: smartphone-based data collection; low-carbon transport; computational framework; travel times; decision support; active travel smartphone-based data collection; low-carbon transport; computational framework; travel times; decision support; active travel
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MDPI and ACS Style

Bagheri, M.; Mladenović, M.N.; Kosonen, I.; Nurminen, J.K. Analysis of Potential Shift to Low-Carbon Urban Travel Modes: A Computational Framework Based on High-Resolution Smartphone Data. Sustainability 2020, 12, 5901. https://doi.org/10.3390/su12155901

AMA Style

Bagheri M, Mladenović MN, Kosonen I, Nurminen JK. Analysis of Potential Shift to Low-Carbon Urban Travel Modes: A Computational Framework Based on High-Resolution Smartphone Data. Sustainability. 2020; 12(15):5901. https://doi.org/10.3390/su12155901

Chicago/Turabian Style

Bagheri, Mehrdad, Miloš N. Mladenović, Iisakki Kosonen, and Jukka K. Nurminen 2020. "Analysis of Potential Shift to Low-Carbon Urban Travel Modes: A Computational Framework Based on High-Resolution Smartphone Data" Sustainability 12, no. 15: 5901. https://doi.org/10.3390/su12155901

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