MICROSCOPIC TRAFFIC FLOW MODEL WITH INFLUENCE OF PASSENGER TRANSPORT
Bibliographic record
Abstract
To analyze the influence of passenger transport on traffic flow, we develop a microscopic traffic flow model that incorporates various factors such as vehicle speed, acceleration, deceleration, lane-changing behavior, and interaction between different types of vehicles. The model takes into account the specific characteristics of passenger transport vehicles, their behavior in mixed traffic, and their impact on the overall traffic flow. We conducted extensive simulations using the developed microscopic traffic flow model to evaluate the influence of passenger transport on the traffic flow characteristics. The simulations were based on real-world scenarios and considered different traffic conditions, including varying traffic volumes. Our results demonstrate that the presence of passenger transport vehicles has a significant impact on the microscopic characteristics of traffic flow on country roads. We observed that the introduction of passenger transport vehicles affects the overall traffic flow dynamics, including vehicle speeds, acceleration patterns, and lane-changing behavior of both passenger transport and other vehicles in traffic flow. Furthermore, we found that the interaction between passenger transport and other vehicles plays a crucial role in determining the traffic flow characteristics. Additionally, our study highlights the importance of considering passenger transport in traffic flow models and transportation planning. The presence of passenger transport vehicles can significantly impact the overall performance of the road network, including travel time, congestion, and safety. Therefore, incorporating the characteristics and behavior of passenger transport vehicles into traffic flow models can provide more accurate predictions and assist in developing effective traffic management strategies. In conclusion, this study contributes to a better understanding of the influence of passenger transport on the microscopic characteristics of traffic flow on roads. The developed microscopic traffic flow model provides valuable insights into the behavior of passenger transport vehicles and their interaction with other vehicles, leading to a comprehensive understanding of traffic flow dynamics. The findings of this study can aid transportation planners and policymakers in making informed decisions for improving the efficiency, safety, and sustainability of road networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".