Abstract
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.
Highlights
One of the essential measures for enabling transition to sustainable urban mobility systems is modal shift [1,2]
Previous research informs us that comparatively longer trip duration with public transport (PT) or cycling with reference to driving is a frequent barrier for modal shift [13,14,15,16]
We have to recognize the importance of perceived travel time, as it can often be underestimated for driving [19], or overestimated for using PT and cycling [20]
Summary
One of the essential measures for enabling transition to sustainable urban mobility systems is modal shift [1,2]. The use of smartphone data has been shown to provide a better estimation of observed walking for PT access/egress, leading to better estimations of potential changes in physical activity with mode shift [27,60,61] Despite these computational potentials, most of the previous literature has studied the reduced emissions and increased travel times separately [55,62], focusing on comparing alternatives such as bike, PT, or shared bike [18,63]. The above background implies two main gaps for developing a computational framework aiming at understanding potential for modal shift taking into account door-to-door travel times, carbon emissions, and physical activity with alternative travel modes Development of such a computational framework requires longitudinal data collection of revealed travel behavior of individuals.
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