Effects Evaluation of Eco-driving Behaviors on Urban Intersection Based on Microscopic Simulation Model
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study addresses the gap in understanding the macroscopic effects of eco-driving on urban traffic flow, specifically at intersections. While individual benefits of eco-driving are well-documented, its impact on overall traffic efficiency and emissions in mixed-traffic scenarios remains unclear due to interactions between eco-drivers and conventional drivers. To investigate this, the authors developed an integrated microscopic simulation platform combining the VISSIM traffic simulation model with a fuel consumption model based on Vehicle Specific Power (VSP). The methodology began with a field experiment in Madrid, Spain, where a test vehicle completed 50 trips under both normal and eco-driving conditions. Data analysis confirmed that eco-driving significantly reduced acceleration, braking intensity, and fuel consumption by 12.1% for the individual vehicle. The VISSIM model was then calibrated using this empirical data, focusing on safety distance parameters identified as critical for fuel consumption accuracy. The simulation evaluated a single signalized intersection under three traffic demand levels (400, 600, and 800 pcu/lane/h) and six eco-driving penetration rates (0% to 100% in 20% increments). A total of 126 simulations were conducted to measure average fuel consumption and travel time. The results demonstrate that the effectiveness of eco-driving is highly dependent on traffic density. Under low traffic demand (400 pcu/lane/h), eco-driving yielded negligible fuel savings and actually increased travel times, particularly at low penetration rates. Conversely, under high traffic demand (800 pcu/lane/h), eco-driving produced significant benefits. At 100% penetration, fuel consumption decreased by 5.58% and travel time dropped by 11.6% compared to normal driving. The study also found that low penetration rates (around 20%) could negatively impact intersection performance, with benefits emerging only as the proportion of eco-drivers increased. The significance of this work lies in its conclusion that eco-driving is not universally beneficial; its positive impacts on both environmental and traffic performance metrics are contingent upon high traffic volumes and high adoption rates. The findings suggest that promoting eco-driving in congested urban areas offers substantial dual benefits, whereas its application in free-flow conditions may be ineffective or counterproductive regarding travel time. The authors note limitations regarding the model's generalizability to other vehicle types and traffic conditions, calling for further research to refine the internal mechanisms of eco-driving behavior in simulations.
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-20 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Applied Guidance: countermeasure evaluation