Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach

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. A...
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Autor*in:	Yang Yu [verfasserIn] Xiaobo Qu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Microscopic traffic flow models Car following models Parameter calibration Eco-driving

Übergeordnetes Werk:	In: International Journal of Transportation Science and Technology - KeAi Communications Co., Ltd., 2017, 11(2022), 2, Seite 268-282
Übergeordnetes Werk:	volume:11 ; year:2022 ; number:2 ; pages:268-282

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.ijtst.2021.04.004

Katalog-ID:	DOAJ042291771

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allfieldsSound	10.1016/j.ijtst.2021.04.004 doi (DE-627)DOAJ042291771 (DE-599)DOAJe2f56c08cff641f7b4550413cc82e0d3 DE-627 ger DE-627 rakwb eng TA1001-1280 Yang Yu verfasserin aut Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Microscopic traffic flow models Car following models Parameter calibration Eco-driving Transportation engineering Xiaobo Qu verfasserin aut In International Journal of Transportation Science and Technology KeAi Communications Co., Ltd., 2017 11(2022), 2, Seite 268-282 (DE-627)880472332 (DE-600)2884863-9 20460449 nnns volume:11 year:2022 number:2 pages:268-282 https://doi.org/10.1016/j.ijtst.2021.04.004 kostenfrei https://doaj.org/article/e2f56c08cff641f7b4550413cc82e0d3 kostenfrei http://www.sciencedirect.com/science/article/pii/S2046043021000265 kostenfrei https://doaj.org/toc/2046-0430 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 11 2022 2 268-282
language	English
source	In International Journal of Transportation Science and Technology 11(2022), 2, Seite 268-282 volume:11 year:2022 number:2 pages:268-282
sourceStr	In International Journal of Transportation Science and Technology 11(2022), 2, Seite 268-282 volume:11 year:2022 number:2 pages:268-282
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Microscopic traffic flow models Car following models Parameter calibration Eco-driving Transportation engineering
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container_title	International Journal of Transportation Science and Technology
authorswithroles_txt_mv	Yang Yu @@aut@@ Xiaobo Qu @@aut@@
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callnumber-first	T - Technology
author	Yang Yu
spellingShingle	Yang Yu misc TA1001-1280 misc Microscopic traffic flow models misc Car following models misc Parameter calibration misc Eco-driving misc Transportation engineering Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach
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topic_title	TA1001-1280 Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach Microscopic traffic flow models Car following models Parameter calibration Eco-driving
topic	misc TA1001-1280 misc Microscopic traffic flow models misc Car following models misc Parameter calibration misc Eco-driving misc Transportation engineering
topic_unstemmed	misc TA1001-1280 misc Microscopic traffic flow models misc Car following models misc Parameter calibration misc Eco-driving misc Transportation engineering
topic_browse	misc TA1001-1280 misc Microscopic traffic flow models misc Car following models misc Parameter calibration misc Eco-driving misc Transportation engineering
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title	Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach
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title_full	Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach
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title_sort	development of parametric eco-driving models for fuel savings: a novel parameter calibration approach
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title_auth	Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach
url	https://doi.org/10.1016/j.ijtst.2021.04.004 https://doaj.org/article/e2f56c08cff641f7b4550413cc82e0d3 http://www.sciencedirect.com/science/article/pii/S2046043021000265 https://doaj.org/toc/2046-0430
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up_date	2024-07-04T00:24:03.195Z
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Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach

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