Simulation Study of Autonomous Vehicles’ Effect on Traffic Flow Characteristics including Autonomous Buses
Bibliographic record
Abstract
Autonomous vehicles are expected to revolutionize the transportation industry. The goal of this research is to study the heterogeneity in traffic flow dynamics by comparing different penetration rates of four different types of vehicles: autonomous cars (AC), autonomous buses (AB), manual cars (MC), and manual buses (MB). For the purpose of this research, a modified cellular automata (CA) model is developed in order to analyze the effect of heterogeneous vehicles (manual and autonomous). Previously, studies have focused on manual and autonomous cars, but we believe a gap in perception and analysis of mixed traffic still exists, as inclusion of other modes of autonomous vehicle research is very limited. Therefore, we have explicitly examined the effect of the AB on overall traffic flow. Moreover, two types of lane changing behavior (aggressive lane changing and polite lane changing) were also integrated into the model. Multiple scenarios through different compositions of vehicles were simulated. As per the results, if AB is employed concurrently with AC, there will be a significant improvement in traffic flow and road capacity, as equally more passengers can be accommodated in AB as AC is also anticipated to be used in carpooling. Secondly, when the vehicles change the lanes aggressively, there is a substantial growth in the flow rate and capacity of the network. Polite lane change does not significantly affect the flow rate.
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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".