Tour-based mode choice modeling technique: US practices
Bibliographic record
Abstract
Achieving consistency between modeled modes for different trips on the same tour has been one of the major reasons for a wide acceptance of the tour-based modeling paradigm. Experience with the tour-based models developed and applied in such metropolitan regions as San-Francisco, New York, Columbus, Atlanta, Sacramento, and Montreal has shown that mode choice is one of the most complicated and least transferable models. This paper provides a systematic overview of the existing tour-based mode choice models and identifies directions for further improvements. There are two different levels at which the mode choice decision is modeled: tour mode (upper-level choice) and trip mode (lower-level choice conditional upon the upper-level choice). The tour level reflects the most important decisions that a traveler makes in terms of using private car versus public transit, non-motorized, or any other mode. Most of the models include at least two car modes (either driver vs. passenger or by single vs. high occupancy), at least two transit modes by access (car vs. walking), non-motorized mode, and some special modes (school bus and/or taxi). In the transit-oriented regions like New York and San-Francisco, additional stratification of transit modes is applied (rail vs. bus, etc). Trip-level decisions provide the details of exact modes for each trip. Trip modes in each region depend on the variety of transit sub-modes (intercity rail, commuter rail, light rail transit, bus rapid transit, express bus, local bus, etc) and variety of car occupancy (single, shared ride 2, shared ride 3, etc) and road pricing categories (toll vs. non-toll). The paper discusses details of choice set formation at both tour and trip levels as well as the linkage between them implemented through the matrix correspondence rules, by using trip mode choice logsums in the tour mode utilities, etc. The mode choice model is normally estimated and applied sequentially, taking into account stop frequency and stop location choices. The hierarchy of sub-models is described. Mode choice decisions are closely intertwined with destination choice and time-of-day (TOD) choice. Sequencing of these choices and linkage between them are still open questions with different approaches applied in different models. The experience with different structures where TOD choice was applied before mode choice (San Francisco, Columbus, Sacramento, Atlanta) versus alternative structures with mode choice applied before TOD choice (New York, Montreal) is described. Possible directions for further enhancement of the integrity and simultaneity in modeling these choices are outlined. For the covering abstract see ITRD E145999
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".