Design and Implementation of a CANBus-Based Eco-Driving System for Public Transport Bus Services
DOI: 10.1109/access.2020.2964119
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the challenge of maintaining long-term fuel efficiency in public transport fleets, noting that while eco-driving training initially reduces fuel consumption and CO2 emissions, its effects often diminish after a few months. The authors argue that continuous monitoring and real-time feedback are necessary to sustain these benefits, particularly for heavy-duty vehicles like buses. To this end, the study introduces a CANBus-based eco-driving system designed to provide both comparative evaluation of driver performance and instant in-vehicle guidance. Unlike previous systems that focused solely on post-trip reporting or isolated real-time warnings, this system combines fair, context-aware benchmarking with immediate driver assistance. The system architecture utilizes On-board Units (OBUs) connected to the vehicle’s Controller Area Network (CANBus) to collect real-time data, including fuel consumption, speed, and engine load, alongside GPS and trip metadata. This data is transmitted via wireless networks to a central application server. The software employs a Model-View-Controller design, where the server processes data to calculate eco-driving performance metrics. A key methodological innovation is the creation of "comparison groups" to ensure fair evaluation. Drivers are compared only against peers operating under identical conditions, defined by route, direction, bus type, season, and time of day. This isolates driver-independent factors, allowing fuel consumption to serve as a reliable performance indicator. The system calculates an "economy percentage" for each driver by comparing their weighted average fuel consumption against the group average. The system was deployed and evaluated over a 15-month period in a public metrobus system serving approximately 250,000 passengers daily, with hardware installed in 64 buses. The in-vehicle interface provides drivers with real-time warnings and feedback based on their current performance relative to their comparison group’s average, using simple visual cues to minimize distraction. Operators access a web-based interface for detailed reports on individual and fleet-wide fuel efficiency. The evaluation results were promising, demonstrating fuel savings of up to approximately 5% in short-term monthly comparisons. Both drivers and operators found the system useful, indicating that the combination of fair comparative benchmarking and instant feedback effectively supports sustained eco-driving behaviors. The significance of this work lies in its practical implementation of a holistic eco-driving solution that bridges the gap between offline performance reporting and real-time driving assistance. By addressing the limitations of prior studies that failed to account for contextual driving conditions or lacked continuous feedback mechanisms, this system offers a scalable model for reducing fuel consumption and emissions in public transport fleets. The findings suggest that integrating CANBus data with context-aware comparative analysis can effectively maintain driver engagement and achieve measurable economic and environmental benefits.
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 | Crossref | — | — | 1 | 2026-06-20 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Applied Guidance: countermeasure evaluation
- Methodological Resource: tool software