Exploring the Effects of Built Environment on Traffic Microcirculation Performance Using XGBoost Model
Bibliographic record
Abstract
As the rapid motorization worldwide, the urban issue of traffic congestion continues to deteriorate. Among the solutions to address the traffic congestions issue, making full use of urban microcirculation roads so as to divert the traffic volume from main roads has been recognized as an efficient approach. However, few studies have explored what built environment determinants can affect the microcirculation performance and how do they correlate. By taking the central urban area in Tianjin, China, as a case study, his study aims to explore the nonlinear effect of urban built environment on the traffic microcirculation performance by using XGBoost model. The results show that it is observed with two troughs in traffic microcirculation performance by hours of a day, corresponding to the morning peak around 7:20–8:40am and evening peak around 16:00–19:00pm, respectively. Additionally, crossroad density is overwhelmingly dominant that affect the microcirculation performance, with an overall contribution approximately 40%. Moreover, one‐way street, high‐rise residential allocation, and T‐intersection density are also the key determinants that contribute to the traffic microcirculation performance. Furthermore, most of the important built environment elements show a nonlinear relationship with the efficiency of traffic microcirculation, with slight difference between peak hours and off‐peak hours. These findings can be used to collectively guide the local government to reasonably allocate the built environment elements so as to alleviate the traffic congestions.
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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".