Route Choice Characteristics for Truckers
Bibliographic record
Abstract
This research studies the decision-making process and factors that affect truck routing. The data collection involved intercept interviews with truck drivers at three rest area and truck stop locations along major highways in Texas and Indiana in the United States and Ontario, Canada. The computerized survey solicited information on truck-routing decisions, identity of the decision makers, factors that affect routing, and sources of information consulted in making these decisions. In addition, stated preferences (SP) experiments were conducted, in which drivers were asked to choose between two route alternatives. A total of 252 drivers completed the survey, yielding 1,121 valid SP observations. These data were used to study the identity of routing decision makers for various driver segments and the sources of information used in pretrip planning and en route. A random-effects logit model was estimated with the SP data. Results show that there are significant differences in the route choice decision-making process in the various driver segments, and that these decisions are affected by multiple factors beyond travel time and cost. These factors include shipping and driver employment terms, such as the method of calculating pay and the bearing of fuel costs and tolls.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".