Travel competence: Empowering travellers

Sharples, Rosemary

doi:10.1016/j.trf.2016.09.013

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Travelers can have an underestimated perception of the travel time with car [10], and conversely, an overestimated perception of travel time with PT and bike [11]. Studies show that car drivers who correct their inaccurate knowledge of travel time with PT and bike are more likely to use it in the future or at least consider PT in their choice set [2,11,12]. erefore, it is important to understand that using low-carbon modes can sometimes have comparable travel time to driving car, i.e., there is a potential for time-relevant low-carbon transportation.…”

Section: Introductionmentioning

confidence: 99%

“…Although often counterintuitive, previous studies have shown that a portion of current urban car trips could be potentially made with low-carbon (e.g., PT) and active (e.g., bike and walk) modes, without compromising much of travel time [8,9]. Travelers can have an underestimated perception of the travel time with car [10], and conversely, an overestimated perception of travel time with PT and bike [11]. Studies show that car drivers who correct their inaccurate knowledge of travel time with PT and bike are more likely to use it in the future or at least consider PT in their choice set [2,11,12].…”

Section: Introductionmentioning

confidence: 99%

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

Bagheri

Mladenović

Kosonen

et al. 2020

Journal of Advanced Transportation

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bagheri

Mladenović

Kosonen

et al. 2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

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“…In contrast, there is an objective potential that some urban driving trips could be replaced with low-carbon and active modes without compromising much travel time [17,18]. Moreover, 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]. Being able to accurately understand the potential for modal shift can also inform the development of strategies for improving users' travel competence, and thus influence the likelihood of considering alternative travel modes [14,20,21].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Identifying alternative stops for first and last-mile urban travel planning

Horstmannshoff,

Redmond

2024

Public Transp

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Urban travelers today are seeking increasingly more information to plan their optimal trip, based on additional factors other than scheduled departure times. Still, some route planning applications provide a simple approach with a few parameter settings (e.g. to minimize travel time between two specific places at a certain time) and without any multimodal solutions. Our approach provides travelers with a set of non-dominated nearby stops that presents a number of traveler preferences in an easily comprehensible and quickly calculable manner. We display first and last-mile stops that fall on a Pareto front based on multiple criteria such as travel time, number of transfers, and frequency of service. Our algorithm combines stop and route-based information to quickly present the traveler with numerous nearby quality options for their itinerary decision making. We expand this algorithm to include multimodal itineraries with the incorporation of free-floating scooters to investigate the change in stop and itinerary characteristics. We then analyze the results on the star-shaped public transportation network of Göttingen, Germany, to show what advantages stops on the Pareto front have as well as demonstrate the increased effect on frequency and service lines when incorporating a broadened multimodal approach.

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Travel competence: Empowering travellers

Cited by 5 publications

References 28 publications

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

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

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

Identifying alternative stops for first and last-mile urban travel planning

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