Perceived accessibility: A literature review
Bibliographic record
Abstract
The integration of accessibility measures into transport planning has become prominent in many regions. However, accessibility evaluation is hampered by not having a comprehensive view on how accessibility is perceived by various population groups and how it impacts their choices given certain transport and land use configurations. Recently, studies have emerged attempting to measure perceived accessibility and understand its determinants and how it relates to various aspects such as travel behaviour and social inclusion using a variety of definitions and methods. In this paper, we review the empirical research on perceived accessibility, aiming to provide structure to future research on this topic. Based on 45 studies discussing perceived accessibility, we find that the concept is often ambiguously defined, and that measures lack robust validation regarding capturing the core aspects of accessibility and perception at the individual level. Moreover, results regarding the links between socioeconomic characteristics and perceived accessibility lack consistency and validity. The relationship between perceived accessibility and travel-related outcomes remains underexplored and requires further investigation, including indirect and bidirectional effects. Based on this literature review and earlier conceptualizations, we construct an empirical research framework that paves the way for future research by proposing relationships between perceived accessibility, calculated accessibility, travel behaviour, residential choice, as well as individual sociodemographic characteristics and attitudes. Understanding how various population groups perceive accessibility is essential for developing more accurate land use and transport measures that impact their behaviour and well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".