GIS Toolkit Design and Application for Route Choice Analysis:A Comparison of Observed Routes and Shortest Paths
Bibliographic record
Abstract
Route choice decision making involves information processing, learning and personal preferences. Decisions regarding route choice are often difficult to observe using conventional data collection methods. Recently, the Global Positioning System (GPS) has been used to collect activity-travel data. Observed route choice can be reconstructed using GPS data as this technology provides second-by-second positional information along with travel speeds. In light of these detailed data sets, sophisticated analysis tools are needed to evaluate route choice data. This paper presents the development and application of a GIS-based toolkit for use in examining observed route choice and shortest paths based on distance and time. Specifically, the tool enables the automatic processing of individual routes to generate a series of route choice variables. Traditional variables such as route distance, travel time and trip speeds, are generated along with measures of route circuity/directness and trip percentages based on road type. The Route Choice Analysis (RCA) toolkit empowers transportation professionals with a tool to help them better evaluate the route choice decision-making process based on network variables. The RCA toolkit is applied to routes captured using person-based GPS data for a sample of households in Waterloo, Ontario, Canada. This sample is used to demonstrate the ability of the toolkit to generate variables to aid in our understanding of route choice decisions.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".