# Study on a Prediction Model of Superhighway Fuel Consumption Based on the Test of Easy Car Platform

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## Abstract

**:**

## 1. Introduction

## 2. Overview of Consumption Research on Superhighways

#### 2.1. Research on Superhighways

#### 2.2. Research on Automobile Fuel Consumption

#### 2.2.1. Research on Domestic Automobile Fuel Consumption

#### 2.2.2. Research on Fuel Consumption of Foreign Automobiles

## 3. Collection of Fuel Consumption Data

#### 3.1. Test Method for Fuel Consumption in the Test of Easy Car Platform

#### 3.2. Classification of the Test Vehicle

## 4. Data Analysis and Curve Fitting by SPSS Software

#### 4.1. Fuel Consumption Model for Small Vehicles

#### 4.1.1. Fuel Consumption Data Collection for Small Vehicles

#### 4.1.2. Fitting and Analysis of Fuel Consumption Data of Small Vehicles

_{3}, b

_{2}, b

_{1}, and c are values in the model parameter table. As shown in Table 4, the model equation of model B is:

#### 4.2. Fuel Consumption Model for Compact Vehicles

#### 4.2.1. Fuel Consumption Data Collection for Compact Vehicles

#### 4.2.2. Fitting and Analysis of Fuel Consumption Data of Compact Vehicles

#### 4.3. Fuel Consumption Model for Mid-Size Vehicles

#### 4.3.1. Fuel Consumption Data Collection for Mid-Size Vehicles

#### 4.3.2. Fitting and Analysis of Fuel Consumption Data of Mid-Size Vehicles

#### 4.4. Fuel Consumption Model for SUV Vehicles

#### 4.4.1. Fuel Consumption Data Collection for SUV Vehicles

#### 4.4.2. Fitting and Analysis of Fuel Consumption Data of SUV Vehicles

## 5. Forecast and Analysis of Fuel Consumption for Different Models

#### 5.1. Fuel Consumption Predictions of Different Models

#### 5.2. Comparative Analysis of Fuel Consumption of Different Models

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- He, Y.; Pei, Y. Feasibility demonstration and Necessity Study of superhighway development. Highway
**2016**, 61, 158–162. [Google Scholar] - He, Y.; Pei, Y. Study on superhighway and its traffic capacity. J. Heilongjiang Inst. Eng.
**2017**, 31, 8–11. [Google Scholar] - He, Y.; Pei, Y. Economic evaluation of superhighway based on travel cost. Highway
**2018**, 63, 117–123. [Google Scholar] - He, Y. Study on Safety Guarantee and Economic Evaluation of Superhighway. Ph.D. Thesis, Northeast Forestry University, Harbin, China, 2017. [Google Scholar]
- He, Y.; Ding, B. Environmental and Economic Evaluation of Superhighway Based on Travel Cost. Ekoloji
**2019**, 28, 4793–4801. [Google Scholar] - He, Y.; Pei, Y.; Ran, B.; Kang, J.; Song, Y. Superhighway Virtual Track System Based on Intelligent Road Buttons. IEEE Access
**2020**, 8, 33419–33427. [Google Scholar] [CrossRef] - Pei, Y.-L.; He, Y.-M.; Ran, B.; Kang, J.; Song, Y.-T. Horizontal Alignment Security Design Theory and Application of Superhighways. Sustainability
**2020**, 12, 2222. [Google Scholar] [CrossRef][Green Version] - Zhao, Q.; Mao, H.; Liu, J. Study on safety curve radius of superhighway. West. Transp. Technol.
**2019**, 2, 159–163. [Google Scholar] - Chen, F.; Xu, P. Research on superhighway development based on SWOT analysis. Sci. Technol. Horiz.
**2018**, 5, 139–140. [Google Scholar] - Chiang, T.-R.; Huang, C.-W.; Su, S.-Y.; Chen, Y.-N. Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer. Comput. Speech Lang.
**2020**, 63, 101073. [Google Scholar] [CrossRef][Green Version] - Gysin, G. The Digital Ocean: The Next Information Superhighway. Sea Technol.
**2017**, 58, 6. [Google Scholar] - Wang, D.; Wang, C.; Bernhard, S.; Tan, Y.; Markus, O. A review of evaluation methods of pavement roughness of non speed limited Expressway in Germany. Chin. J. Highw.
**2019**, 32, 105–113. [Google Scholar] - Redelmeier, D.A.; Bhatti, J.A. Princess Diana and Reduced Traffic Deaths in France and the United States. Am. J. Public Health
**2017**, 107, 1246–1248. [Google Scholar] [CrossRef] [PubMed] - Driscoll, R.; Page, Y.; Lassarre, S.; Ehrlich, J. LAVIA--an evaluation of the potential safety benefits of the French intelligent speed adaptation project. Annu. Proc. Assoc. Adv. Automot. Med.
**2007**, 51, 485–505. [Google Scholar] [PubMed] - Feng, H. Study on Calculation Model of Fuel Consumption of Commercial Vehicles Based on VSP in Mountainous Expressway. Master’s Thesis, Chang’an University, Xi’an, China, 2016. [Google Scholar]
- Jia, H.; Juan, Z.; Zhang, X.; Ni, A. Determination and comparison of post evaluation fuel consumption index of Expressway. J. Jilin Univ.
**2004**, 2, 298–301. [Google Scholar] - Peng, B. Study on Fuel Consumption Model of Expressway Vehicles. Master’s Thesis, Harbin University of Technology, Harbin, China, 2014. [Google Scholar]
- Guo, J. Multiple linear regression prediction model of automobile fuel consumption based on the existence of interaction term. Guangxi Qual. Superv. Guide
**2019**, 11, 138–140. [Google Scholar] - Li, Q. Prediction model of automobile fuel consumption based on multiple linear regression. Taiwan Strait Sci. Technol. Ind.
**2019**, 3, 90–92. [Google Scholar] - Zhang, J.; Li, K.; Xu, B.; Li, H. Estimation of vehicle transient fuel consumption based on least square method. Automot. Eng.
**2018**, 40, 1151–1157. [Google Scholar] - Graf von Westarp, A. A new model for the calculation of the bunker fuel speed–consumption relation. Ocean Eng.
**2020**, 204, 107262. [Google Scholar] [CrossRef] - Zhang, Y.-T.; Claudel, C.G.; Hu, M.-B.; Yu, Y.-H.; Shi, C.-L. Develop of a fuel consumption model for hybrid vehicles. Energy Convers. Manag.
**2020**, 207, 112546. [Google Scholar] [CrossRef] - Nuss, E.; Wick, M.; Andert, J.; De Schutter, J.; Diehl, M.; Abel, D.; Albin, T. Nonlinear model predictive control of a discrete-cycle gasoline-controlled auto ignition engine model: Simulative analysis. Int. J. Engine Res.
**2019**, 20, 1025–1036. [Google Scholar] [CrossRef] - Hao, L.; Wang, C.; Yin, H.; Hao, C.; Wang, H.; Tan, J.; Wang, X.; Ge, Y. Model-based estimation of light-duty vehicle fuel economy at high altitude. Adv. Mech. Eng.
**2019**, 11, 11. [Google Scholar] [CrossRef] - Gunawan, F.E.; Soewito, B.; Surantha, N.; Mauritsius, T.; Sekishita, N. A Study of the Sensitivity of the Fuel Consumption to Driving Strategy by Micro Simulation. Procedia Comput. Sci.
**2019**, 157, 375–381. [Google Scholar] [CrossRef] - Zargarnezhad, S.; Dashti, R.; Ahmadi, R. Predicting vehicle fuel consumption in energy distribution companies using ANNs. Transp. Res. Part D -Transp. Environ.
**2019**, 74, 174–188. [Google Scholar] [CrossRef] - Ben Dror, M.; Qin, L.; An, F. The gap between certified and real-world passenger vehicle fuel consumption in China measured using a mobile phone application data. Energy Policy
**2019**, 128, 8–16. [Google Scholar] [CrossRef] - Collier, S.; Ruehl, C.; Yoon, S.; Boriboonsomsin, K.; Durbin, T.D.; Scora, G.; Johnson, K.; Herner, J. Impact of Heavy-Duty Diesel Truck Activity on Fuel Consumption and Its Implication for the Reduction of Greenhouse Gas Emissions. Transp. Res. Rec.
**2019**, 2673, 125–135. [Google Scholar] [CrossRef] - Wang, B. Selection of automobile tires. Guangdong Silkworm Ind.
**2016**, 50, 26–29. [Google Scholar] - Chen, P. Comparative Analysis and Research on Competitive Vehicle Performance. Automot. Pract. Technol.
**2018**, 8, 66–69. [Google Scholar] - Zhao, R. Fuel Consumption and Emission Assessment Based on Vehicle Dynamics. Master’s Thesis, Chang’an University, Xi’an, China, 2018. [Google Scholar]

Superhighway Grade | Grade III | Grade II | Grade I | ||||||
---|---|---|---|---|---|---|---|---|---|

Design speed(km/h) | 180 | 160 | 140 | 160 | 140 | 120 | 140 | 120 | 100 |

Number | Vehicle Type | Number | Vehicle Type |
---|---|---|---|

A | Dongfeng yueda Kia xiu er 1.6 L GL automatic | Z | Changan Suzuki Tianyu SX4 1.6 L urban fashion |

B | Changan Suzuki Swift 1.5LSuper flash version flash sharp | G | Brilliance China junjie FRV 1.3 L Manual comfort |

C | Tianjin faw xiali N5 1.3 L Manual luxury model | H | Dongfeng Honda civic 1.8 L EXi automatic comfort version |

D | Great Wall dazzle 1.3 L Manual CROSS version | I | Beijing hyundai i30 1.6 L Automatic comfort type |

E | Changan Ford new fiesta 1.5 L Manual fashion model | J | Saic roewe 550 1.8 L DVVT S Automatic start |

K | Buick regal 2.4 T | P | Nissan xiao passenger CVT 2WD |

L | Mazda rui wing 2.5 L Supreme edition | Q | Nissan qijun 2.5 L XL luxury edition |

M | New jing cheng SX 2.0 T AT | R | Great Wall harvard H3 skylight |

N | Audi A6 2.0 T | S | Kia lion run 2.0 T Automatic 2WD GLS |

O | Passat new area 1.8 T Automatic | T | Modern Tucson 2.0 T Automatic skylight |

Speed (km/h) | 40 | 50 | 60 | 70 | 80 | 90 | 100 | 110 | 120 |
---|---|---|---|---|---|---|---|---|---|

A | 5.37 | 4.91 | 5.11 | 5.52 | 6.14 | 6.61 | 7.24 | 8.06 | 9.11 |

B | 5.24 | 4.68 | 4.39 | 4.95 | 5.21 | 5.77 | 6.30 | 7.09 | 7.78 |

C | 5.80 | 4.98 | 4.89 | 5.32 | 5.47 | 5.68 | 6.39 | 7.05 | 8.10 |

D | 4.69 | 3.77 | 4.01 | 4.30 | 4.83 | 5.49 | 5.96 | 6.88 | 7.59 |

E | 5.89 | 5.27 | 4.91 | 5.30 | 5.66 | 6.24 | 6.81 | 7.50 | 8.51 |

Model Equations | Model Summary | Parameter Estimation | |||||||
---|---|---|---|---|---|---|---|---|---|

R-Squared | F | df_{1} | df_{2} | Sig. | Constant | b_{1} | b_{2} | b_{3} | |

Linear equation | 0.778 | 24.574 | 1 | 7 | 0.002 | 2.775 | 0.037 | ||

Logarithmic curve | 0.641 | 12.524 | 1 | 7 | 0.009 | −4.926 | 2.460 | ||

Reciprocal curve | 0.488 | 6.666 | 1 | 7 | 0.036 | 7.734 | −143.291 | ||

Conic | 0.974 | 110.213 | 2 | 6 | 0.000 | 7.428 | −0.093 | 0.001 | |

Cubic curve | 0.991 | 189.186 | 3 | 5 | 0.000 | 12.180 | −0.298 | 0.004 | −1.138 × 10^{−5} |

Compound curve | 0.775 | 24.146 | 1 | 7 | 0.002 | 3.428 | 1.006 | ||

Power curve | 0.643 | 12.584 | 1 | 7 | 0.009 | 0.935 | 0.415 | ||

S curve | 0.489 | 6.700 | 1 | 7 | 0.036 | 2.067 | −24.163 | ||

Growth curve | 0.775 | 24.146 | 1 | 7 | 0.002 | 1.232 | 0.006 | ||

Exponential curve | 0.775 | 24.146 | 1 | 7 | 0.002 | 3.428 | 0.006 | ||

Logistic | 0.775 | 24.146 | 1 | 7 | 0.002 | 0.292 | 0.994 |

Speed (km/h) | 40 | 50 | 60 | 70 | 80 | 90 | 100 | 110 | 120 |
---|---|---|---|---|---|---|---|---|---|

Z | 5.57 | 4.84 | 4.99 | 5.23 | 5.76 | 6.33 | 7.00 | 7.58 | 7.81 |

G | 5.07 | 4.69 | 4.60 | 4.96 | 5.53 | 6.02 | 6.67 | 7.35 | 8.13 |

H | 5.31 | 4.73 | 4.41 | 4.53 | 4.84 | 5.21 | 5.87 | 6.42 | 7.15 |

I | 4.69 | 4.01 | 4.30 | 4.70 | 5.18 | 5.62 | 6.17 | 6.76 | 7.70 |

J | 5.89 | 5.59 | 5.58 | 5.18 | 5.64 | 6.04 | 6.73 | 7.30 | 8.11 |

Model Equations | Model Summary | Parameter Estimation | |||||||
---|---|---|---|---|---|---|---|---|---|

R-Squared | F | df_{1} | df_{2} | Sig. | Constant | b_{1} | b_{2} | b_{3} | |

Linear equation | 0.382 | 4.320 | 1 | 7 | 0.076 | 4.584 | 0.022 | ||

Logarithmic curve | 0.242 | 2.230 | 1 | 7 | 0.179 | 0.765 | 1.289 | ||

Reciprocal curve | 0.123 | 0.986 | 1 | 7 | 0.354 | 7.208 | −61.542 | ||

Conic | 0.939 | 46.421 | 2 | 6 | 0.000 | 11.298 | −0.165 | 0.001 | |

Cubic curve | 0.981 | 83.846 | 3 | 5 | 0.000 | 17.478 | −0.432 | 0.005 | −1.480 × 10^{−5} |

Compound curve | 0.365 | 4.028 | 1 | 7 | 0.085 | 4.819 | 1.003 | ||

Power curve | 0.229 | 2.073 | 1 | 7 | 0.193 | 2.727 | 0.193 | ||

S curve | 0.113 | 0.895 | 1 | 7 | 0.376 | 1.965 | −9.070 | ||

Growth curve | 0.365 | 4.028 | 1 | 7 | 0.085 | 1.573 | 0.003 | ||

Exponential curve | 0.365 | 4.028 | 1 | 7 | 0.085 | 4.819 | 0.003 | ||

Logistic | 0.365 | 4.028 | 1 | 7 | 0.085 | 0.208 | 0.997 |

Speed (km/h) | 40 | 50 | 60 | 70 | 80 | 90 | 100 | 110 | 120 |
---|---|---|---|---|---|---|---|---|---|

K | 6.40 | 5.09 | 5.43 | 4.89 | 5.53 | 6.20 | 6.60 | 7.80 | 8.34 |

L | 5.77 | 5.56 | 5.29 | 5.66 | 5.83 | 6.19 | 6.81 | 7.59 | 8.24 |

M | 6.28 | 5.73 | 5.91 | 5.64 | 6.16 | 6.62 | 7.20 | 7.89 | 8.73 |

N | 6.90 | 6.30 | 6.09 | 6.19 | 6.64 | 7.54 | 7.65 | 8.23 | 9.44 |

O | 6.08 | 5.09 | 5.14 | 4.87 | 5.17 | 5.79 | 6.33 | 6.89 | 7.47 |

Model Equations | Model Summary | Parameter Estimation | |||||||
---|---|---|---|---|---|---|---|---|---|

R-Squared | F | df_{1} | df_{2} | Sig. | Constant | b_{1} | b_{2} | b_{3} | |

Linear equation | 0.705 | 16.722 | 1 | 7 | 0.005 | 4.497 | 0.034 | ||

Logarithmic curve | 0.563 | 9.013 | 1 | 7 | 0.020 | −2.486 | 2.245 | ||

Reciprocal curve | 0.412 | 4.901 | 1 | 7 | 0.062 | 9.029 | −128.236 | ||

Conic | 0.958 | 67.758 | 2 | 6 | 0.000 | 9.654 | −0.110 | 0.001 | |

Cubic curve | 0.969 | 51.482 | 3 | 5 | 0.000 | 13.302 | −0.267 | 0.003 | −8.737 × 10^{−6} |

Compound curve | 0.704 | 16.664 | 1 | 7 | 0.005 | 4.982 | 1.005 | ||

Power curve | 0.563 | 9.031 | 1 | 7 | 0.020 | 1.970 | 0.298 | ||

S curve | 0.411 | 4.883 | 1 | 7 | 0.063 | 2.207 | −17.002 | ||

Growth curve | 0.704 | 16.664 | 1 | 7 | 0.005 | 1.606 | 0.005 | ||

Exponential curve | 0.704 | 16.664 | 1 | 7 | 0.005 | 4.982 | 0.005 | ||

Logistic | 0.704 | 16.664 | 1 | 7 | 0.005 | 0.201 | 0.995 |

Speed (km/h) | 40 | 50 | 60 | 70 | 80 | 90 | 100 | 110 | 120 |
---|---|---|---|---|---|---|---|---|---|

P | 5.69 | 5.42 | 5.80 | 6.25 | 6.68 | 7.28 | 7.96 | 9.06 | 9.98 |

Q | 5.68 | 5.54 | 5.73 | 6.24 | 6.80 | 7.13 | 7.92 | 9.07 | 9.72 |

R | 8.03 | 7.65 | 6.98 | 7.65 | 7.85 | 8.75 | 9.77 | 10.67 | 11.55 |

S | 7.08 | 6.63 | 6.19 | 6.88 | 7.65 | 8.54 | 9.50 | 10.70 | 11.98 |

T | 7.33 | 7.26 | 6.80 | 7.15 | 8.00 | 8.90 | 10.08 | 10.80 | 11.82 |

Model Equations | Model Summary | Parameter Estimation | |||||||
---|---|---|---|---|---|---|---|---|---|

R-Squared | F | df_{1} | df_{2} | Sig. | Constant | b_{1} | b_{2} | b_{3} | |

Linear equation | 0.855 | 41.156 | 1 | 7 | 0.000 | 3.764 | 0.061 | ||

Logarithmic curve | 0.735 | 19.443 | 1 | 7 | 0.003 | −9.519 | 4.209 | ||

Reciprocal curve | 0.591 | 10.123 | 1 | 7 | 0.015 | 12.239 | −252.091 | ||

Conic | 0.977 | 125.319 | 2 | 6 | 0.000 | 9.641 | −0.103 | 0.001 | |

Cubic curve | 0.992 | 215.368 | 3 | 5 | 0.000 | 16.781 | −0.411 | 0.005 | −1.710 × 10^{−5} |

Compound curve | 0.860 | 42.844 | 1 | 7 | 0.000 | 4.927 | 1.007 | ||

Power curve | 0.746 | 20.610 | 1 | 7 | 0.003 | 1.112 | 0.471 | ||

S curve | 0.605 | 10.701 | 1 | 7 | 0.014 | 2.542 | −28.312 | ||

Growth curve | 0.860 | 42.844 | 1 | 7 | 0.000 | 1.595 | 0.007 | ||

Exponential curve | 0.860 | 42.844 | 1 | 7 | 0.000 | 4.927 | 0.007 | ||

Logistic | 0.860 | 42.844 | 1 | 7 | 0.000 | 0.203 | 0.993 |

Speed (km/h) | 130 | 140 | 150 | 160 | 170 | 180 |
---|---|---|---|---|---|---|

A | 10.30 | 11.61 | 13.06 | 14.68 | 16.43 | 18.32 |

B | 9.05 | 10.30 | 11.72 | 13.32 | 15.06 | 16.96 |

C | 9.26 | 10.60 | 12.13 | 13.87 | 15.77 | 17.86 |

D | 8.96 | 10.29 | 11.78 | 13.46 | 15.27 | 17.25 |

E | 9.85 | 11.27 | 12.89 | 14.72 | 16.73 | 18.92 |

Speed (km/h) | 130 | 140 | 150 | 160 | 170 | 180 |
---|---|---|---|---|---|---|

Z | 9.06 | 10.07 | 11.20 | 12.46 | 13.81 | 15.29 |

G | 9.34 | 10.55 | 11.90 | 13.42 | 15.06 | 16.84 |

H | 8.37 | 9.61 | 11.03 | 12.65 | 14.42 | 16.37 |

I | 8.73 | 9.85 | 11.10 | 12.49 | 13.99 | 15.63 |

J | 9.26 | 10.50 | 11.91 | 13.51 | 15.27 | 17.19 |

Speed (km/h) | 130 | 140 | 150 | 160 | 170 | 180 |
---|---|---|---|---|---|---|

K | 10.03 | 11.63 | 13.46 | 15.56 | 17.85 | 20.38 |

L | 9.35 | 10.49 | 11.79 | 13.26 | 14.86 | 16.60 |

M | 9.92 | 11.19 | 12.63 | 14.26 | 16.03 | 17.98 |

N | 10.58 | 11.91 | 13.41 | 15.11 | 16.98 | 19.02 |

O | 8.80 | 10.06 | 11.52 | 13.18 | 15.01 | 17.02 |

Speed (km/h) | 130 | 140 | 150 | 160 | 170 | 180 |
---|---|---|---|---|---|---|

P | 11.24 | 12.58 | 14.07 | 15.72 | 17.49 | 19.41 |

Q | 10.98 | 12.24 | 13.62 | 15.16 | 16.81 | 18.59 |

R | 13.39 | 15.15 | 17.14 | 19.39 | 21.83 | 24.51 |

S | 13.98 | 16.00 | 18.27 | 20.82 | 23.58 | 26.58 |

T | 13.65 | 15.39 | 17.33 | 19.50 | 21.85 | 24.40 |

Model | Mean Square Error | Model | Mean Square Error | Model | Mean Square Error | Model | Mean Square Error |
---|---|---|---|---|---|---|---|

A | 0.027 | Z | 0.085 | K | 0.071 | P | 0.014 |

B | 0.046 | G | 0.030 | L | 0.011 | Q | 0.024 |

C | 0.037 | H | 0.023 | M | 0.022 | R | 0.087 |

D | 0.077 | I | 0.047 | N | 0.070 | S | 0.074 |

E | 0.038 | J | 0.022 | O | 0.064 | T | 0.088 |

Speed (km/h) | 130 | 140 | 150 | 160 | 170 | 180 |
---|---|---|---|---|---|---|

Small vehicles | 9.48 | 10.81 | 12.32 | 14.01 | 15.85 | 17.86 |

Compact vehicles | 8.95 | 10.12 | 11.43 | 12.91 | 14.51 | 16.26 |

Mid-size vehicles | 9.74 | 11.06 | 12.56 | 14.27 | 16.15 | 18.20 |

SUV vehicles | 12.65 | 14.27 | 16.09 | 18.12 | 20.31 | 22.70 |

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## Share and Cite

**MDPI and ACS Style**

He, Y.-M.; Kang, J.; Pei, Y.-L.; Ran, B.; Song, Y.-T.
Study on a Prediction Model of Superhighway Fuel Consumption Based on the Test of Easy Car Platform. *Sustainability* **2020**, *12*, 6260.
https://doi.org/10.3390/su12156260

**AMA Style**

He Y-M, Kang J, Pei Y-L, Ran B, Song Y-T.
Study on a Prediction Model of Superhighway Fuel Consumption Based on the Test of Easy Car Platform. *Sustainability*. 2020; 12(15):6260.
https://doi.org/10.3390/su12156260

**Chicago/Turabian Style**

He, Yong-Ming, Jia Kang, Yu-Long Pei, Bin Ran, and Yu-Ting Song.
2020. "Study on a Prediction Model of Superhighway Fuel Consumption Based on the Test of Easy Car Platform" *Sustainability* 12, no. 15: 6260.
https://doi.org/10.3390/su12156260