Latest revision as of 10:48, 15 February 2021

Abstract

Evaluating potential of shifting to low-carbon transport modes requires considering limited travel-time budget of travelers. Despite previous studies focusing on time-relevant modal shift, there is a lack of integrated and transferable computational frameworks, which would use emerging smartphone-based high-resolution longitudinal travel datasets. This research explains and illustrates a computational framework for this purpose. The proposed framework compares observed trips with computed alternative trips and estimates the extent to which alternatives could reduce carbon emission without a significant increase in travel time.. The framework estimates potential of substituting observed car and public-transport trips with lower-carbon modes, evaluating parameters per individual traveler as well as for the whole city, from a set of temporal and spatial viewpoints. The illustrated parameters include the size and distribution of modal shifts, emission savings, and increased active-travel growth, as clustered by target mode, departure time, trip distance, and spatial coverage throughout the city. Parameters are also evaluated based on the frequently repeated trips. We evaluate usefulness of the method by analyzing door-to-door trips of a few hundred travelers, collected from smartphone traces in the Helsinki metropolitan area, Finland, during several months. The experiment’s preliminary results show that, for instance, on average, 20% of frequent car trips of each traveler have a low-carbon alternative, and if the preferred alternatives are chosen, about 8% of the carbon emissions could be saved. In addition, it is seen that the spatial potential of bike as an alternative is much more sporadic throughout the city compared to that of bus, which has relatively more trips from/to city center. With few changes, the method would be applicable to other cities, bringing possibly different quantitative results. In particular, having more thorough data from large number of participants could provide implications for transportation researchers and planners to identify groups or areas for promoting mode shift. Finally, we discuss the limitations and lessons learned, highlighting future research directions.

Document type: Article

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The different versions of the original document can be found in:

• http://downloads.hindawi.com/journals/jat/2020/4693750.pdf under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.1155/2020/4693750 under the license cc-by

• http://hdl.handle.net/10138/317160

• https://aaltodoc.aalto.fi/handle/123456789/43637

• http://downloads.hindawi.com/journals/jat/2020/4693750.pdf,

http://downloads.hindawi.com/journals/jat/2020/4693750.xml,

http://dx.doi.org/10.1155/2020/4693750

• http://dx.doi.org/10.1155/2020/4693750,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.hindawi.com/journals/jat/2020/4693750,

http://downloads.hindawi.com/journals/jat/2020/4693750.pdf,

https://researchportal.helsinki.fi/en/publications/a-computational-framework-for-revealing-competitive-travel-times-,

https://research.aalto.fi/files/41707578/4693750.pdf,

https://research.aalto.fi/en/publications/a-computational-framework-for-revealing-competitive-travel-times-,

https://academic.microsoft.com/#/detail/3006289770