A Computational Framework for Revealing Competitive Travel Times with Low-Carbon Modes Based on Smartphone Data Collection

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.

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

“…The present paper extends the framework proposed by Bagheri et al [55], using longitudinal data collection of revealed travel behavior of individuals for exploring the potential for shifting to low-carbon travel alternatives in a target urban region. In comparison to previous development, this current, extended, computational framework analyzes changes in travel time together with emission reductions and physically active travel, by considering variable time-increase thresholds.…”

“…In addition, the filtering process discards non-stop circular trips, such as running exercises, where such trips start and end at the same geolocation. Additional details on data collection and filtering have been discussed in Bagheri et al [55] and Rinne et al [45]. Further details about accuracy and noise in the data collection, informed by the findings of the case study, are also discussed in the discussion section of this paper.…”

“…Given the need to understand the potential for modal shift at the level of individual travel times, emissions, and physical activity, there is a need for accurately computing such potential from disaggregated data collection [55][56][57]. Along with technological trends, development of such computational frameworks for understanding latent trips has previously relied on the use of traditional travel surveys [17,18,58,59].…”

“…In addition, 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].…”

“…Second, such a computational framework should provide useful and scalable analysis for exploring time-relevant low-carbon travel alternatives in the target region. To this end, this research presents further development in the formulation and implementation of such a computational framework, expanding from Bagheri et al [55]. The computational framework is based on high-resolution smartphone-based travel data and evaluated using a case in the Helsinki Metropolitan Region (HMR), Finland.…”