Impact of Buses, Taxis, Passenger Cars, and Traffic Infrastructure on Average Travel Speed
Bibliographic record
Abstract
It is known that many variables influence traffic, yet very little is known about the weight of each factor in the dynamics of traffic in cities of developing countries, in many cases due to their peculiar traffic regulations. In this work, we search for the variables that have the most significant impact on the average travel speed of three distinct types of vehicles: passenger cars, taxis, and buses. First, we developed a tool featuring algorithms that simulate ordinary overtaking and car-following behaviors, along with controls for setting vehicles’ actions, particularly buses’ and taxis’ stops. Then, we chose a particular zone to study, based on its common geometry and the particular traffic infrastructure (speed bumps, traffic lights, and bus stops) inside it. Later on, three experiments were carried out, with the following results. (1) Both the buses’ arrival frequency and curbside bus stops affect the passenger cars’ average travel speed. The buses’ response was affected by the bus bay and curbside bus stops. The buses’ speed tendency influenced neither the passenger cars’ nor buses’ response. (2) Taxis’ arrival frequency, stopping frequency, and speed tendency were found to influence the passenger cars’ response. Taxis’ response was altered by taxis’ speed tendency, while buses’ response was affected by taxis’ arrival frequencies. (3) The number of speed bumps, the arrival frequency of passenger cars, and their speed conditions (homogeneous and heterogeneous) affect the passenger cars’ response. We expect that the findings presented in this study, along with the recommendations made from the results, may pave the way for better road design public policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".