Developing an Integrated Activity-Based Travel Demand Model for Analyzing the Impact of Electric Vehicles on Traffic Networks and Vehicular Emissions
Bibliographic record
Abstract
The integrated Transport Land-use and Energy (iTLE) model is combined with traffic and emission simulators to examine the effects of electric vehicle (EV) adoption on traffic networks and greenhouse gas (GHG) emissions reduction. The research uses data from the 2022 Halifax Travel Activity (HaliTRAC) survey to develop an EV type choice model within iTLE, while traffic and emission simulators are created on PTV VISUM and MOVES3.0 platforms. Along with the base case scenario (2022), two more scenarios are considered at five-year intervals: 2027 (scenario-1), and 2032 (scenario-2). The traffic assignment model is calibrated and validated for the base year (2022) considering traffic volume data of fifty (50) different intersections of Halifax Regional Municipality (HRM). The findings reveal that the younger generation, higher-income households, and full-time workers are more inclined to adopt EVs in the future. The estimated EV adoption rate for 2027 is 16.3% from the base year (2022), increasing to 31% by 2032. Regarding GHG emissions, the morning peak period in 2027 witnessed a notable 18.5% decrease compared to the base year of 2022, and in 2032, this reduction became even more substantial at 31.8% compared to 2027. Similarly, the evening peak period also experienced significant declines, with a 22.4% decrease in 2027 and a more remarkable 32.9% reduction in 2032. The outcomes of this research will be valuable for urban planners, engineers, and government officials when implementing climate action plans as they devise effective policy measures related to vehicles.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".