Measuring Accessibility to Urban Services Using Fuzzy Logic Within Transportation GIS
Bibliographic record
Abstract
This paper is an attempt to bridge the gap between, on the one hand, the mobility behaviour of households and their perception of accessibility to urban amenities and, on the other hand, house price dynamics as captured through the hedonic modelling of the Quebec City residential market. Part One focuses on designing a methodology to analyze mobility behaviour of people and to relate their sensitivity to travel time with service places location within the city so as to assess their perceived accessibility. A series of Ïsubjectiveó measures of accessibility based on actual trips made by individuals and households is built for Quebec City on the grounds of the 2001 origin-destination (O-D) survey. Part Two proposes an empirical test of the impact of accessibility on house prices. Applying hedonic modelling to some 952 single-family houses sold in Quebec City (Pop. 489 820 in 2001) between 1993 and 1996, two accessibility measures are compared (Section 4), the former being based on simulated travel times aggregated through factor analysis while the latter rests on perceived accessibility indices obtained via fuzzy logic on actual travel times. Findings firstly indicate that, while overall accessibility to jobs and services is quite homogeneous throughout the City thanks to a highly efficient highway network, there are nevertheless statistically significant differences in the way accessibility is structured depending on trip purposes and household profiles, thereby supporting the hypothesis that various types of persons experience different constraints and are not equally willing to travel in order to reach various kinds of activities. Findings also suggest that an objective measure of accessibility combining travel time indicators for the nearest service with factor analysis yields optimal results from a merely statistical point of view. Yet, resorting to subjective, and more comprehensive, accessibility indices derived from fuzzy logic, and accounting for number of opportunities, allows to investigate commuting patterns and travel behaviour with greater insight and to design trip purpose and household status-specific indicators of accessibility to urban amenities. This research leads to the conclusion that different people have a heterogeneous perception of space and thus, will adjust their willingness to pay for additional centrality/accessibility when choosing their home location depending on their needs and preferences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".