Trajectory Simulation of Emergency Vehicles and Interactions with Surrounding Traffic
Bibliographic record
Abstract
Emergency services play an important role in the life of a city and are subject to constant public scrutiny. The efficient dispatch of emergency vehicles (EMVs) requires realistic shortest-path algorithms involving the movement of EMVs within an urban network under emergency conditions. Trip-time estimates used in shortest-path algorithms would be much more precise if it were possible to model more realistically the interactions between EMVs and surrounding traffic, as well as the reactions of other vehicles in the presence of an EMV. Therefore, EMV trajectories should be studied at the microscopic level to accurately model the impact of EMV travel along a path shared with other vehicles. In this research, we develop three models to incorporate specific non-EMV reactions associated with changing lanes, mounting the sidewalk, and approaching an intersection, plus two algorithms to actuate traffic lights at signalized intersections. These models and algorithms were coded in commercial microscopic traffic simulation software through the implementation of an application programming interface (API) designed to overcome the limitations of the software to realistically simulate disturbed traffic conditions and anomalous nonemergency vehicle driver behaviour observed in the presence of an EMV. Basic information about these real-world effects was gleaned from video footage recorded in Santiago, Chile, by traffic cameras, fire truck-mounted cameras, and truck-originated GPS pulses. To validate the design, a real EMV trip captured by the footage was simulated by the API. The simulation considerably reduced the degree of error in delineating the path followed by the EMV compared to the default simulations generated by most commercially available software, thereby demonstrating that the API can provide highly accurate estimates of EMV trip times in an emergency context.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".