Development of a Fuel Efficiency Prediction Model for Autonomous Eco-Driving and Analysis of Road Environment Effects

Kyu Jin Lee

doi:10.7470/jkst.2025.43.6.732

All Issue

2025 Vol.43, Issue 6 Preview Page Next Page

Article

Development of a Fuel Efficiency Prediction Model for Autonomous Eco-Driving and Analysis of Road Environment Effects 자율주행 기반 경제운전의 연비 예측모형 및 도로환경 영향 분석

31 December 2025. pp. 732-742

PDF XML

Abstract

In the recent years, the internet has become accessible without limitation of time and location to anyone with smartphones. It resulted in more convenient travel information access This study aims to support the design of eco-driving-based autonomous driving systems by analyzing the combined effects of driver control behavior and roadway conditions on vehicle fuel efficiency, and by developing vehicle-specific fuel economy prediction models. First, real-world driving data from 980 passenger cars and vans were analyzed using a Classification and Regression Tree (CART) approach to quantify how the frequency of control variables—such as rapid acceleration and fuel cut usage—impacts fuel economy. The results revealed that key predictors and their thresholds vary by vehicle type, highlighting the need for tailored autonomous driving control logic based on vehicle characteristics. Next, approximately 170,000 operational records from bus tachographs were used to construct a mixed-effects model that statistically examined the structural influence of road and vehicle factors. The analysis showed that the effect of eco-driving practices significantly varies depending on service area and bus type. In particular, under congested urban conditions, external factors such as frequent stops and traffic signals tend to reduce the fuel-saving benefits of eco-driving. These findings suggest that autonomous driving systems should evolve beyond driver substitution to incorporate “context-aware” strategies responsive to the operating environment. From a technical standpoint, this study provides quantitative evidence to inform the development of eco-driving algorithms for autonomous vehicles. From a policy perspective, it also underscores the importance of addressing environmental barriers through road infrastructure improvements to enhance the effectiveness of fuel-saving strategies.

Keywords

autonomous driving

driving behavior analysis

eco-driving

fuel Economy prediction

road infrastructure

본 연구는 자율주행 기반 경제운전 로직 설계에 필요한 기초자료를 제공하기 위해, 자동차 연비에 영향을 미치는 운전자 제어 행태와 도로교통 환경 요인을 통합적으로 분석하고, 차종별 연비 예측모형을 구축하였다. 먼저, 승용차 및 승합차 980대의 실운행 데이터를 활용해 분류 및 회귀 트리(CART) 분석을 수행하고, 급가속, 퓨얼컷 등 제어 변수의 빈도가 연비에 미치는 영향을 정량적으로 규명하였다. 분석 결과, 연비에 영향을 미치는 주요 변수와 그 임계값은 차종별로 상이하게 나타났으며, 이는 차량 특성에 따른 자율주행 제어 로직의 맞춤화 필요성을 시사한다. 이어서 약 17만 건의 버스 운행기록계 데이터를 활용해 혼합효과 모형을 구축한 결과, 운행 권역과 차량 유형에 따라 경제운전 효과가 유의한 차이를 보였다. 특히 도심에서는 정체, 신호 간섭 등 외적 요인으로 인해 경제운전의 연비 개선 효과가 제한되는 경향이 나타났다. 이는 자율주행 시스템이 단순한 운전자 대체 기술을 넘어, 주행 환경을 고려하는 상황인지형 제어 전략으로 발전해야 함을 의미한다. 본 연구는 기술적 측면에서 자율주행 기반 경제운전 알고리즘 개발에, 정책적 측면에서는 도로 인프라 개선을 통한 연비 저해 요인 해소에 기여할 수 있는 실증적 근거를 제시하였다.

키워드

자율주행

운전자 행태 분석

경제운전

연비 예측

도로교통 인프라

References

Alam M. S., McNabola A. (2014), A Critical Review and Assessment of Eco-driving Policy & Technology: Benefits & Limitations, Transportation Research Part D: Transport and Environment, 35, 42-49.

10.1016/j.tranpol.2014.05.016

Boriboonsomsin K., Barth M. (2009), Impacts of Road Grade on Fuel Consumption and Carbon Dioxide Emissions Evidenced by Use of Advanced Navigation Systems, Transportation Research Record, 2139(1), 21-30.

10.3141/2139-03

Boriboonsomsin K., Barth M., Zhu W., Vu A. (2010), Eco-driving: Pilot Evaluation of Driving Behavior Changes among US Drivers, University of California Transportation Center, California, USA.

Breiman L., Friedman J. H., Olshen R. A., Stone C. J. (1984), Classification and Regression Trees, Wadsworth International Group, New York, USA.

Cellina M., Strada S., Savaresi S. M. (2023), Vehicle Fuel Consumption Virtual Sensing from GNSS and IMU Measurements, IEEE Transactions on Intelligent Transportation Systems, 488-493.

10.1109/ITSC57777.2023.10422477

Ericsson E. (2001), Independent Driving Pattern Factors and Their Influence on Fuel-use and Exhaust Emission Factors, Transportation Research Part D: Transport and Environment, 6(5), 325-345.

10.1016/S1361-9209(01)00003-7

Hadjigeorgiou A., Timotheou S. (2025), Energy Consumption Optimization for Autonomous Vehicles via Positive Control Input Minimization, arXiv preprint, arXiv:2506.04685.

He X., Kim H. C., Ma R., Wallington T. J., Keoleian G. A., De Kleine R., Zhang S., Wu Y. (2022), Energy Consumption Simulation for Connected and Automated Vehicles: Eco-driving Benefits Versus Automation Loads, SAE International Journal of Connected and Automated Vehicles.

10.4271/12-06-01-0002

International Energy Agency (IEA) (2022), World Energy Outlook 2022, IEA, Paris, France.

Kabir R., Remias S. M., Waddell J., Zhu D. X. (2023), Time-series Fuel Consumption Prediction Assessing Delay Impacts on Energy Using Vehicular Trajectory, Transportation Research Part D, 117, 103678.

10.1016/j.trd.2023.103678

Korea Transport Safety Authority (2017), Development of a Standard Platform for Integrated Terminals for Commercial Vehicles and Safe Driving Support Technology.

Lee K. J., Yi Y. J., Choi K. C. (2018), A Methodology for Evaluating the Effects of Transportation Policies Related to Greenhouse Gas Reduction, J. Korean Soc. Transp., 36(1), Korean Society of Transportation, 1-12.

10.7470/jkst.2018.36.1.001

Li J., Wu X., Xu M., Liu Y. (2024), Multiobjective Eco-driving Strategy for Connected and Automated Electric Vehicles Considering Complex Urban Traffic Influence Factors, IEEE Transactions on Transportation Electrification, 10(4), 10043-10058.

10.1109/TTE.2023.3349025

Oh G. S., Leblanc D. J., Peng H. (2022), Vehicle Energy Dataset (VED): A Large-scale Dataset for Vehicle Energy Consumption Research, IEEE Transactions on Intelligent Transportation Systems, 23(4), 3302-3312.

10.1109/TITS.2020.3035596

Pinheiro J. C., Bates D. M. (2000), Mixed-effects Models in S and S-PLUS, Springer, Berlin, Germany.

10.1007/978-1-4419-0318-1

Pirayre A., Michel P., Rodriguez S. S., Chasse A. (2022), Driving Behavior Identification and Real-world Fuel Consumption Estimation with Crowdsensing Data, IEEE Transactions on Intelligent Transportation Systems, 23(10), 18378-18391.

10.1109/TITS.2022.3169534

Yang J., Zhao D., Jiang J., Lan J., Mason B., Tian D., Li L. (2023), A Less-disturbed Ecological Driving Strategy for Connected and Automated Vehicles, IEEE Transactions on Intelligent Transportation Systems 8(1), 413-424.

10.1109/TIV.2021.3112499

Information

Publisher :Korean Society of Transportation
Publisher(Ko) :대한교통학회
Journal Title :Journal of Korean Society of Transportation
Journal Title(Ko) :대한교통학회지
Volume : 43
No :6
Pages :732-742
Received Date : 2025-07-01
Revised Date : 2025-07-17
Accepted Date : 2025-08-18
DOI :https://doi.org/10.7470/jkst.2025.43.6.732

Journal of Korean Society of Transportation ISSN:1229-1366(Print) 2234-4217(Online) 대한교통학회지

All Issue