Development of a cumulative prospect theory-based departure time choice model for dynamic traffic microsimulation
Bibliographic record
Abstract
This study presents a comprehensive framework of dynamic traffic microsimulation modeling system that considers travelers’ departure time (DT) choices in response to sudden risk events in the transport network. The novelty of the model is that it captures the nonlinear responses of travelers to sudden risk events during DT choice-making by utilizing a Cumulative Prospect Theory (CPT)-based approach. For model testing, the study considers a case of transportation systems’ critical infrastructure (CI) renewal in Halifax, Canada that poses considerable uncertainty for travelers in the morning rush hours during a construction period. Two models were evaluated: (1) a model without the DT component (Model 1) and (2) a model with the DT component (Model 2). Model 2 offers methodological promises in studying traveler behavior under uncertainty. The proposed CPT-based DT model is advantageous to capture nonlinearity in quantifying travelers’ perception of transportation choice utility. The results of Model 2 significantly differ from the results of the traditional model without the DT component in terms of network performance. For instance, if the DT choice is considered, total traffic delays significantly increase in the early rush hours due to construction-related sudden bridge closure. In Model 2, queue increases at local intersections for initial hours if drivers’ DT adjustment is explicitly modeled within the traffic microsimulation modeling framework. Results of this study provide insights into developing emergency transportation management strategies in the case of sudden disruptions to daily travel activities and traffic operations in the network.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".