Development and Validation of Improved Impedance Functions for Roads with Mixed Traffic Using Taxi GPS Trajectory Data and Simulation
Bibliographic record
Abstract
This paper proposes an improved impedance function for roads with mixed traffic. It is known that only limited studies consider the impact of nonmotorized traffic on travel impedance of a road segment, and a comparison of the impedance considering nonmotorized traffic with the classic BPR function, which does not consider the former, is scarce. Most of the previous studies targeted road conditions in developed countries, where the presence of nonmotorized traffic is negligible, and therefore limited efforts have been invested to develop improved impedance function considering mixed traffic. To overcome this limitation, this paper develops an improved impedance function and carries out a case study for a road in the city of Wuhan, China. The improved impedance function explicitly considers the interaction between motorized and nonmotorized traffic. Taxi GPS data from the case study road is used to extract and analyze the travel time of the “probe vehicles” running through the sampled segment at any time during a sampling day. The capacity of the road segment is measured, and the traffic flow of motorized vehicles and nonmotorized vehicles on the segment is counted. Based on the above data, the classic BPR function and the improved one proposed in this paper are calibrated. After comparing and analyzing the observed road impedance based on both analytical and simulation results, the classic BPR function and the proposed impedance function, the proposed impedance function is found to be more accurate to simulate the observed road impedance, with the error reducing from 14.83 s with the classic BPR impedance function to 6.50 s with the improved function. The proposed impedance function possesses a simple structure and high flexibility, and the parameters calibrated in this paper can be applied to similar roads to provide more realistic impedance than the previous ones based on the classic BPR function. The calibrated improved impedance function’s transferability to other similar roads is validated by applying it to another road and the results show that the percentage error between the predicted travel times and the observed ones is only 3.8%.
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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 | 0.001 |
| 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 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".