Travel Behavior of Car Travelers with the Presence of Park-and-Ride Facilities and Autonomous Vehicles
Bibliographic record
Abstract
Travelers' behavior is predicted based on their individual preferences. People search for alternatives to maximize their benefit from doing activities, such as increasing the activity time by minimizing the travel time. Traffic congestion and the scarcity of parking spaces in the city center motivate the decision-makers to encourage travelers to use the park-and-ride (P&R) system. An evaluation concerning the impact of using the P&R system on the travel behavior of car users is conducted. Some of the existing P&R facilities are incorporated into the daily activity plans of car travelers to produce new daily activity plans (i.e., P&R facility is considered an activity). By using the Multi-Agent Transport Simulation (MATSim) open-source tool, simulations of the daily activity plans including the P&R system and autonomous vehicles (AVs) are conducted. The study examines three scenarios: (1) a simulation of the existing condition, (2) a simulation of the daily activity plans of the travelers with the P&R system, and (3) a simulation of the daily activity plans of the travelers with the P&R system and AVs. The results show that using the P&R system increases the overall travel time compared with the existing conditions, and the use of AVs as a transport mode impacts the existing modal share as follows: 64 % of the car users switch to AVs, while 15 % of the car users switch to public transport. The output of this study might be used by policy-makers in parking pricing and the location of the P&R facilities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".