Urban mobility under armed conflict: shifts in mode preferences and public transport fare behaviors
Bibliographic record
Abstract
Abstract This study delves into the profound transformations in mobility patterns resulting from armed conflict in Ukraine. Kharkiv City, the second-largest Ukrainian city, is considered, where residents continue to utilize public transport to fulfill critical daily functions, including commuting to workplaces and procuring essential goods. Despite the ongoing conflict, public transport remained a vital resource for maintaining socio-economic stability and ensuring personal well-being. This paper explores two main aspects: changes in the frequency of mode usage and fare-related aspects in multimodal networks. By utilizing the random utility maximization theory, the research identifies key factors driving shifts in mobility behaviors amidst the chaos of conflict. Behavioral data was collected via an online survey, yielding a final sample of 213 respondents. The analysis covers a multimodal transportation system that comprises metro, bus, trolleybus, tram, private car, bicycle, and walking modes. First, a list of ordered logit models for mode frequency usage was estimated to explore the changes in travel behaviors comparing peaceful and armed conflict times. Second, a mixed logit model was developed to examine the heterogeneity in individuals’ willingness to adopt various public transport fare plans. The study reveals striking insights: many individuals have significantly declined usage of metro and bus services, while private car utilization remained unchanged during armed conflict. Moreover, this research underscores the importance of fare-related aspects to be deployed in post-armed conflicted times. The findings emphasize the crucial role of multimodal transport plans, facilitating a shift from traditional single-trip tickets to integrated digital solutions within the public transport framework.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".