Exploring Route Choice Decision-Making Process: Comparison of Preplanned and Observed Routes Obtained Using Person-Based GPS
Bibliographic record
Abstract
Trip decisions are complex and involve choosing the activity destination, the mode and subsequently the route for travel. This paper presents detailed information on the pre-planned and observed route choices for the home-to-work commute. Specifically, the study examines how people formulate their route plans and describe their attitudes and preferences for their selected route. A geographical information system (GIS) records the pre-planned route information with the route planning sequence. Observing route choice is a difficult procedure; however, through the use of the global positioning system (GPS), one can accurately record route choice. An automated activity-trip detection algorithm processes GPS data and displays results within an internet-based prompted recall diary. The diary is used to verify trip start and end times. This combination of GPS, GIS and diary responses provide great insight into the route choice decision-making process. Twenty-four individuals from Ontario, Canada participated in answering survey questions and the collection of person-based GPS data. Results indicate a preference to minimize travel time as stated by participants in deciding what route to travel. Participants also affirmed a desire to minimize the number of stop lights/signs, as well as, avoid congestion and maximize route directness. A comparison between pre-planned and observed routes, reveals about one-fifth of participants deviated from their pre-planned route. This study demonstrates the need for qualitative and quantitative survey methods for exploring pre-planned and observed route choice patterns.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".