Transportation mobility during COVID-19: a systematic review and bibliometric analysis
Bibliographic record
Abstract
The COVID-19 pandemic has significantly affected global transportation mobility, presenting unprecedented challenges to transportation management. Public transit and ride-hailing services saw a drastic reduction in ridership, leading to an increased inclination towards private vehicles. The pandemic also altered travel patterns and individual mobility due to various COVID-19 protocols. This study conducted a comprehensive review of 96 academic papers spanning from January 1, 2020, to December 31, 2022, focusing on transportation and mobility using the Scopus database. Three major themes were identified: 'Impact on Ride-Hailing Services', 'Impact on Mode Preference', and 'Impact on Trip Purpose', with subdivisions based on keywords and key findings extracted using VOSviewer. The pandemic significantly impacted ride-hailing services, altering demand, usage, and safety measures. Mode preference shifted towards private vehicles due to safety concerns. The present study underscores the long-term implications of the pandemic, emphasizing recovery strategies for ride-hailing services and mode preferences post-pandemic. It highlights the need for sustainable transportation policies, advocating for enhanced public transportation systems, promoting active travel modes, and addressing socioeconomic disparities in mobility patterns. The findings emphasize the need for resilient transportation strategies in the face of future disruptions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.076 | 0.082 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".