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 comparing observed with computed alternative trips in order to estimate the extent to which modal shifts could reduce carbon emissions without incurring a significant travel time penalty. 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. For instance, the framework illustrates the size and distribution of modal shift possibilities and emission-savings, as clustered by departure time, trip distance and spatial coverage throughout the city. The framework also identifies the trips frequently repeated by the same traveler. 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.
Bagheri Majdabadi, M., Mladenovic, M., Kosonen, I., Nurminen, J. K., Roncoli, C., & Ylä-Jääski, A. (2020). A computational framework for revealing competitive travel times with low carbon modes based on smartphone data collection. JOURNAL OF ADVANCED TRANSPORTATION, 2020, . https://doi.org/10.1155/2020/4693750