Assessing emissions impacts of automated vehicles
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Summary
This paper presents a proof-of-concept methodology for assessing the environmental impacts of automated vehicles (AVs) by integrating traffic microsimulation with emissions modeling. The research addresses the need for a robust framework to evaluate the complex, second-order effects of AVs on surface transportation, specifically regarding fuel consumption and pollutant emissions. As AVs are introduced into existing systems, understanding their potential benefits and dis-benefits is critical for policy development. The study aims to demonstrate that emissions and fuel usage can be accurately calculated from microsimulation outputs, serving as an early implementation step within a broader USDOT Volpe National Transportation Systems Center framework. The researchers employed the VISSIM microsimulation model to simulate vehicle behavior on a single-lane, two-mile roadway under four distinct scenarios: at-capacity (1,500 vehicles/hour) and over-capacity (3,000 vehicles/hour) conditions, each with either 100% human drivers or 100% automated vehicles. To differentiate AV behavior, the simulation removed oscillation in following distance by setting specific Weidman 99 parameters to zero, resulting in smoother, constant following distances compared to human drivers. Trajectory data from these simulations was processed in MATLAB to calculate Vehicle Specific Power (VSP) and assign operating modes. These mode distributions were then input into the EPA’s MOVES 2014a model to estimate fuel consumption and emissions for carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), and particulate matter (PM2.5). The results indicate that automated vehicles generally produce less emissions and consume less fuel than human drivers, though significant trade-offs exist. In terms of fuel efficiency, AVs consumed 43% less energy in at-capacity conditions and 20% less in over-capacity conditions. For NOx, VOCs, and PM2.5, AVs performed equal to or better than human drivers, with NOx emissions reduced by 32% and 17% in at-capacity and over-capacity scenarios, respectively. However, CO emissions—which constitute the largest portion of total emissions—were significantly higher for AVs in at-capacity conditions (4.4 kg vs. 1.8 kg for humans) due to the inverted relationship between CO emissions and speed. Consequently, while AVs reduced overall emissions by 7% in over-capacity scenarios, human drivers produced half the total emissions of AVs in at-capacity scenarios. The study concludes that while AVs offer substantial fuel savings and reductions in certain pollutants, they can generate dis-benefits in others depending on traffic conditions. This validates the necessity of a comprehensive assessment framework that captures these complex interactions rather than assuming uniform benefits. The successful integration of VISSIM and MOVES demonstrates that such detailed environmental impact analyses are feasible, providing a foundation for future research into more varied AV behaviors and operational scenarios.
Key finding
AVs reduced fuel consumption 43% (at-capacity) and 20% (over-capacity); NOx reductions were 32% and 17%. However, at-capacity humans halved total emissions due to 2.4x less CO at higher speeds. Over-capacity AVs produced 7% fewer total emissions despite higher CO.
Methodology
simulation_modeling
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (5 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Theoretical Contribution: computational model