Predictability of Vehicle Fuel Consumption Using LSTM: Findings from Field Experiments-Reference-Cited by-同舟云学术

Predictability of Vehicle Fuel Consumption Using LSTM: Findings from Field Experiments

Published:2023-05 Issue:5 Volume:149 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Wang Guanqun¹^ORCID,Zhang Licheng²^ORCID,Xu Zhigang³^ORCID,Wang Runmin⁴,Hina Syeda Mahwish⁵,Wei Tao⁶,Qu Xiaobo⁷^ORCID,Yang Ran⁵

Affiliation:

1. Ph.D. Candidate, School of Information Engineering, Chang’an Univ., Xi’an 710064, China. ORCID: .

2. Senior Engineer, School of Information Engineering, Chang’an Univ., Xi’an 710064, China. ORCID: .

3. Professor, School of Information Engineering, Chang’an Univ., Xi’an 710064, China (corresponding author). ORCID: .

4. Senior Engineer, School of Information Engineering, Chang’an Univ., Xi’an 710064, China.

5. Master’s Student, School of Information Engineering, Chang’an Univ., Xi’an 710064, China.

6. Engineer, Shaanxi Lingyun Electronics Group Co., Ltd., No. 1 Yuquan Rd., Baoji 721006, China.

7. Professor, Dept. of Architecture and Civil Engineering, Chalmers Univ. of Technology, Gothenburg SE-412 96, Sweden. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-7643

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4. Amer, A., A. Abdalla, A. Noraziah, and A. A. C. Fauzi. 2014. “Prediction of vehicle fuel consumption model based on artificial neural network.” Appl. Mech. Mater. 492 (Jan): 3–6. https://doi.org/10.4028/www.scientific.net/AMM.492.3.

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