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Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach
- Source :
- International Journal of Transportation Science and Technology. 11:268-282
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- The existing conventional traffic flow models aims to simulate human-driven following vehicles in real world. In this era of emerging transport solutions, controlling or intervening traffic flow to achieve high fuel efficiency along with good driving safety and travel efficiency becomes a reality. As such, it is worth exploring the possibility of developing eco-driving models to optimise vehicle movements for fuel consumption minimisation, while maintaining safety and efficiency. In this study, we propose a modified genetic algorithm (GA) based calibration method that enables the calibrated parametric traffic flow (car following) models to simulate or control vehicles in an eco-driving manner. By developing a novel objective function for the GA method based on the widely-used VT-Micro fuel consumption model, the proposed method can calibrate model parameters towards improving fuel efficiency. Besides, by subtly using heavy fuel consumptions as a surrogate index to represent low travel efficiency or dangerous driving strategies, the modified GA method with the novel objective function can guide the calibrated model towards achieving complete eco-driving requirements. Experimental simulation results further indicate that traffic flow models calibrated by the modified GA-based method can also alleviate traffic disturbances and oscillations in a more effective manner.
- Subjects :
- Minimisation (psychology)
050210 logistics & transportation
Computer science
05 social sciences
Transportation
010501 environmental sciences
Management, Monitoring, Policy and Law
Traffic flow
01 natural sciences
Automotive engineering
Microscopic traffic flow model
Dangerous driving
0502 economics and business
Automotive Engineering
Genetic algorithm
Fuel efficiency
Calibration
0105 earth and related environmental sciences
Civil and Structural Engineering
Parametric statistics
Subjects
Details
- ISSN :
- 20460430
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- International Journal of Transportation Science and Technology
- Accession number :
- edsair.doi...........7f0372b9030337ed6f36fd3f27b91d64