Revealing Daily Mobility Pattern Disparities of Monomodal and Multimodal Travelers through a Multi-Layer Cluster Analysis: Insights from a Combined Big Dataset
Sustainability (Switzerland), ISSN: 2071-1050, Vol: 16, Issue: 9
2024
- 3Captures
- 1Mentions
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures3
- Readers3
- Mentions1
- Blog Mentions1
- Blog1
Article Description
More detailed and precise mobility patterns are needed for policies to reduce monomodal automotive dependency and promote multimodality in travel behaviors. Yet, empirical evidence from an integrated view of a complete door-to-door trip mode chain with daily mobility for pattern identification is still lacking. As an improvement and a solution on this issue, a multi-layer cluster model was designed and proposed for distinguishing 20 mobility pattern clusters, including six monomodal traveler groups, two non-transit multimodal traveler groups, and 12 transit multimodal based on big data mining. Statistical analysis with seven indicator measurements and a spatial distribution analysis with the Kernel density GIS maps of travelers’ residential location were carried out to reveal significant disparities across pattern clusters concerning spatial, social, and trip characteristics, based on which more precise and target policies for each group were discussed. This research may help provide more detailed information in establishing traveler mobility pattern profiles and solutions in filling the planning–implementation gap from the perspective of planners, policymakers, and travelers.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know