Calibrating Car-Following Models on Urban Streets Using Naturalistic Driving Data
Why this work is in the frame
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Bibliographic record
Abstract
Car-following models serve an important role in gaining a thorough understanding of traffic flow and driving behavior characteristics. By analyzing these characteristics, the models are critical to microscopic traffc simulation, and consequently, to traffic safety. However, lack of reliable traffic data in China has, until recently, limited the use of car-following models. As the Shanghai Naturalistic Driving Study (SH-NDS) has now made such data accessible, car-following models have been built for freeways and urban expressways, but none have yet been developed for urban streets. To compare car following for the three road types and to determine the best model for urban streets, five commonly used car-following models were calibrated and validated with 5,500 urban street-level car-following events extracted from the 161,055 km of data collected in the SH-NDS. The models were evaluated based on their parameter estimates and root mean square percentage errors (RMSPE). Results show that (1) the intelligent driver model (IDM), with a calibration error of 24% and a validation error of 28%, performed best in modeling drivers’ car-following behavior on urban Shanghai streets; and (2) in comparison to previous car-following research on Chinese freeways and urban expressways, drivers on urban streets tend to assume a relatively lower car-following speed, and maintain slightly larger time headway and maximum acceleration. Because the IDM demonstrated great performance on expressways, freeways, and urban streets in China, it is reasonable to assume the model may show similar performance when used to analyze car following in other countries.
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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 | 0.001 |
| 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 it