Using Floating Car Data to Analyse the Effects of ITS Measures and Eco-Driving
DOI: 10.3390/s141121358
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Summary
This study addresses the need for accurate calibration data in microscopic traffic and emission models, which are essential for assessing the environmental impact of Intelligent Transportation Systems (ITS) and eco-driving measures. Because road transportation contributes significantly to global CO2 emissions, precise simulation of traffic flow and vehicle dynamics is critical. However, traditional model calibration often relies on aggregated data that fails to capture individual driving behaviors, such as acceleration and deceleration patterns, leading to potential inaccuracies in emission estimates. The authors propose using Floating Car Data (FCD) to derive detailed speed and acceleration profiles, providing a more robust basis for calibrating simulation tools like VISSIM and AIMSUN. The research was conducted in Madrid, Spain, focusing on the M-30 ring motorway and adjacent urban streets. Data was collected from nine drivers over approximately 3,000 trips, utilizing smartphones for GPS tracking and On-Board Diagnostics (OBD) keys to record instantaneous speed, acceleration, fuel consumption, and engine revolutions per minute at a 1 Hz frequency. The study analyzed four specific measures: Section Speed Control, Variable Speed Limits, Cruise Control, and Eco-Driving. The collected data was processed to calculate key variables corresponding to simulation parameters, including the 95th percentile of instantaneous speed, maximum acceleration, maximum deceleration, and positive accumulated acceleration per kilometer. These variables were compared between base scenarios and scenarios where the respective measures were applied. The results demonstrate that ITS measures and eco-driving significantly alter driving patterns, thereby affecting fuel consumption and emissions. Section Speed Control reduced the standard deviation of speed by 23.1%, creating a more homogeneous traffic flow, and decreased fuel consumption by 3.8%. Variable Speed Limits also reduced speed variability by 23.8% but increased acceleration and braking values due to driver reactions to signage, resulting in a modest 1.94% fuel reduction. Cruise Control proved highly effective in smoothing driving behavior, reducing positive accumulated acceleration by 47.1% and fuel consumption by 4.70%. Eco-Driving yielded the most substantial changes in driving dynamics, particularly in urban areas where it reduced maximum speed by 6.4% and decreased acceleration and braking levels by nearly 15%. On motorways, eco-driving reduced fuel consumption by 8.61%, while in urban settings, it achieved a 7.14% reduction. The significance of this work lies in its provision of empirical reference values for calibrating microscopic traffic models. By quantifying the specific changes in speed and acceleration profiles caused by various ITS and behavioral measures, the study enables more accurate ex-ante assessments of emission reductions. The authors conclude that while specific input values depend on local conditions, the percentage changes observed serve as valuable orders of magnitude for modeling similar road networks. This methodology highlights the utility of FCD in enhancing the reliability of traffic simulations, ultimately supporting better-informed decisions for implementing technologies and policies aimed at reducing transportation-related emissions.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-19 |
| archive | success | openalex | — | — | 5 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-19 |
| chunk | success | chunk | — | — | 1 | 2026-06-19 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-19 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-19 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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