New and emerging data forms in transportation planning and policy: Opportunities and challenges for “Track and Trace” data

Harrison, Gillian; Grant-Muller, Susan; Hodgson, Frances

doi:10.1016/j.trc.2020.102672

Cited by 55 publications

(28 citation statements)

References 123 publications

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“…e spatial and temporal detail of information from emerging data sources can be used to enrich conventional transport data when modelling transport behaviour [4][5][6][7]. On the other hand, social media and web platforms might enhance traditional data collection methods [8].…”

Section: Literature Review: Benefits Of Mixing Sp-rp Datamentioning

confidence: 99%

Revisiting the Benefits of Combining Data of a Different Nature: Strategic Forecasting of New Mode Alternatives

Guzman

Arellana

Cantillo-García

et al. 2021

Journal of Advanced Transportation

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We revisit the practice of combining revealed (RP) and stated preference (SP) data (i.e., the data enrichment, DE, paradigm) in discrete choice models using secondary data obtained from emerging sources; these facilitate access to massive information about travel choices and can be used to improve transport models. Even though the benefits of the DE paradigm have been known for years, there is a large gap between the state of practice and the state of the art, particularly in Global South countries (but also in many industrialized nations). We use a SP dataset considering two new transport alternatives (train and metro) and a RP dataset based on a large mobility survey in Bogotá, Colombia, complemented with fairly precise level-of-service data obtained using GIS utilities and the Distance Matrix API by Google. Our results allow us to discuss good practice, identify barriers and challenges to the paradigm’s application, and draw recommendations for forecasting the demand for new alternatives using joint RP and SP data.

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Section: Literature Review: Benefits Of Mixing Sp-rp Datamentioning

confidence: 99%

Revisiting the Benefits of Combining Data of a Different Nature: Strategic Forecasting of New Mode Alternatives

Guzman

Arellana

Cantillo-García

et al. 2021

Journal of Advanced Transportation

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“…ey also present formal criteria and thresholds to characterize and segment passenger mobility [36]. Harrison et al pointed out in their paper that passively collected GPS-based "Track & Trace" datasets of individual mobility have great potential in enhancing transportation modeling and policy-making [37]. Zhang et al investigated the temporal variations of trip-destination distributions and their association with city spatial structure using four types of inhomogeneous Poisson point process models [38].…”

Section: Related Workmentioning

confidence: 99%

Mobile Phone Data in Urban Commuting: A Network Community Detection-Based Framework to Unveil the Spatial Structure of Commuting Demand

Liu

Yang

et al. 2020

Journal of Advanced Transportation

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As the outcomes of rapid urbanization, the spatial separation of homes and workplaces extends the commuting distance and complicates the commuting demand of residents. To promote urban livability and sustainability, it becomes crucially important to understand the commuting patterns by decomposing and simplifying the diverse commuting demand. In this paper, a methodology framework is proposed to describe the spatial structure of commuting demand in a city using mobile phone data. Four steps are mainly included in the proposed methodology: the preprocessing of mobile phone data, the labeling of individuals and their activity points, the construction of the jobs-housing relationship network, and the network decomposition based on the community detection algorithm. To demonstrate the practical use of the proposed methodologies, a case study is carried out in Shanghai to explore the commuting patterns of Shanghai residents. The result indicates the regions with dense jobs-housing connections and cross-regional commuting demand. The result also finds that the administrative boundaries show a significant effect on the residential commuting behavior and the metro lines on the cross-regional commuting behavior. The results generated by the methodology proposed can be referenced by policymakers to support urban transportation planning and promote urban livability and sustainability.

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“…The proposed computational framework could be useful in the process of policy and planning decisions across spatial and temporal scales, from macroscopic to microscopic [54]. In particular, numerical estimations and visual representations of ODs, travel time, and mode choice percentages provided are essential for initiating discussions across a range of stakeholders responsible for enabling transition to a sustainable mobility system.…”

Section: Usefulness Of Understanding Modal Shift Potentialmentioning

confidence: 99%

“…Further developments of automatic post-processing of the collected data [51,52] and open-source platforms [51,53] have contributed to a wider spread and customization of smartphone-based data collection in the recent years. Finally, the recent review explains integration of smartphone-based data into the transport planning and policy methods [54].…”

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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New and emerging data forms in transportation planning and policy: Opportunities and challenges for “Track and Trace” data

Abstract: This is a repository copy of New and emerging data forms in transportation planning and policy: Opportunities and challenges for "Track and Trace" data.

Cited by 55 publications

References 123 publications

Revisiting the Benefits of Combining Data of a Different Nature: Strategic Forecasting of New Mode Alternatives

Revisiting the Benefits of Combining Data of a Different Nature: Strategic Forecasting of New Mode Alternatives

Mobile Phone Data in Urban Commuting: A Network Community Detection-Based Framework to Unveil the Spatial Structure of Commuting Demand

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

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