Investigating the effects of intersection control types on emissions using microsimulation models and field measurements
Bibliographic record
Abstract
Emission estimates are becoming critical metrics to evaluate the impacts of transportation projects such as modifications of the road geometry, updating of intersection signalization or technological improvements.To evaluate the impact of projects and policies, methods to evaluate emissions related to climate change and public health (through metrics such as air quality) have become more prevalent.The general objective of this research is to determine the impact of traffic controls, in particular stop signs, in comparison to other intersection controls, on vehicular emissions.More specifically, the objective of the first project is to propose a microscopic modelling approach based on emission software tools to evaluate the impact on emission levels before and after the transformation of one-way stop to all-way stop intersections in an urban corridor.This case study used the EPA's emissions model MOVES, along with the traffic microsimulation model VISSIM, to evaluate the emissions impact of intersection modifications using a Montreal case study.Intersections in the network of interest were converted from one-way stop controlled to all-way stop controlled in a political move aimed at improving pedestrian and cyclist safety.This modification was analysed using the models and it was found that energy consumption as well as emission rates of CO, NOx, NO, NO2, atmospheric CO2, PM10 -exhaust, PM10 -brake-wear, PM2.5 -exhaust, and PM2.5 -brake-wear increased after the stop signs were added, with growth range of 4.4% to 32%.The only pollutants whose rates decreased were PM2.5 and PM10 due to tire-wear.In the second portion of the research, a PEMS device was used with several test vehicles.These vehicles were driven throughout the network which was tested in the first portion of this research, along with several networks of a similar composition.Data was sorted based on type of intersection and compared.Results showed that within a 30m buffer of the intersection, a general pattern exists where intersections with stop in the minor approach generate the least emissions, followed by all-way stop intersections, then signalized intersections with emissions increases of approximately 50% and 20% between the types respectively.However, this pattern disappears when data is controlled for the number of seconds a vehicle spends within each type of intersections, with emission rates becoming relatively equal.Furthermore, the trajectories of these experiments were entered into MOVES in order to compare the model's predictions to the ground-truth data that was collected.It was found that MOVES estimates were inconsistent, with the model providing a relatively accurate prediction of fuel consumption, over-predicting NO and NO2, and under-predicting CO2.A weak correlation was observed with absolute values ranging between 0.006 and 0.269.Among the finding of this research we can highlight the fact that microsimulation models seem to introduce inaccuracies into the evaluation.Despite the safety benefits that stop signs can introduce, the addition of stop signs, and the subsequent required stop, significantly increases vehicular emissions related to climate change and human health issues.When upgrading intersections, or implementing other roadway modifications, the impacts on the environment and public health should be considered in the decision and design process.Furthermore, emission estimation tools such as MOVES should be further evaluated in the Canadian context to validate their accuracy and calibration.
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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 | 0.001 |
| 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 | 0.001 |
| 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".