Simple Mixed Reality Infrastructure for Experimental Analysis of Route Choice Behavior
Bibliographic record
Abstract
Microscopic traffic simulation models offer virtual reproduction of full-scale traffic networks with individual vehicle and driver resolution. However, relevant behavioral aspects, such as route-choice behavior, are based on some theoretical assumptions. Alternatively, driving simulators allow for direct testing of real subjects. However, the virtual driving environment is typically a fairly rudimentary representation of the road network and traffic conditions, focusing on the immediate surroundings of the test vehicle. As such, both tools were integrated to create a mixed reality traffic analysis environment. The objective is to enhance the credibility of in-lab-simulated route-choice experiments under various intelligent transportation system applications. The mixed reality system allows a human subject to “drive” a vehicle in a microscopic traffic simulation model of an actual physical network. An externally controlled driving capability was integrated into the widely used Quadstone Paramics microscopic traffic simulator. The developed mixed reality platform was used at the Toronto (Canada) Intelligent Transportation System Centre to test subjects while driving in a simulated model of a main downtown Toronto corridor. Drivers were given descriptive and prescriptive traffic information while their reactions were monitored. For comparative purposes, they were also tested using a more classical map-based point-and-click route-choice procedure. Results of the analysis highlight the potential of the developed mixed reality platform to enhance the realism of in-lab-simulated route-choice experiments and hence improve the credibility of collected data. Both the virtual reproduction of the choice environment and the tangible consequences of choice decisions contribute to an enhanced experimental environment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".