# Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. Motivation

## 4. Vehicle Emission Model

#### 4.1. Acceleration or Deceleration

#### 4.2. Cruising

#### 4.3. Idling

## 5. Traffic Flow Prediction Method

- Accuracy: Accuracy is the most basic requirement. Accurate prediction of future traffic conditions is the basis for accurate navigation.
- Real-time: Real-time is the precondition of the application. The process of training, solving, and prediction of the model needs to have high efficiency. As the traffic flow varies greatly in a short time, once the predicted result loses its timeliness, it loses its significance.
- Adaptability: Adaptability is essential to guarantee the stability of the whole system. The traffic flow will be disturbed by many factors in a short time. The prediction model needs to be able to change flexibly and deal with different conditions through simple adjustment of parameters to ensure the stability of the system.

#### 5.1. Traffic Flow Prediction Based on Support Vector Regression

- Data Preparation: Extract historical data from the database. Construct a road map, calibrate coordinates, and transform historical data into traffic flow data. Normalize the data and divide them into a training set and a test set.
- Data Analysis: Analyze the characteristics of the data, choose parameters of SVR, and obtain the decision function.
- Model Construction: Build the SVR model with the training set. Evaluate the forecasting results using the test set. Finally, apply the model to real-time traffic flow data for real-time forecasting.

- Penalty coefficient C: It adjusts the proportion between empirical risk and expected risk so as to make the model get the best generalization ability.
- Insensitivity coefficient $\u03f5$: It affects the number of support vectors, thus affecting the generalization ability of the model.
- Kernel function parameters $\sigma $: It influences the distribution of input samples in the feature space and the correlation between support vectors.

#### 5.2. GAPSO-Enhanced SVR

## 6. Low-Carbon-Emission-Oriented Navigation Method

## 7. Experimental Results

#### 7.1. Experimental Framework

#### 7.2. Evaluation of the Traffic Flow Prediction Model

#### 7.3. Evaluation of the Low-Carbon-Emission-Oriented Navigation Algorithm

## 8. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Parameter | Interpretation | Value |
---|---|---|

$\xi $ | fuel-to-air mass ratio | 1 |

K | engine friction factor | 0.2 |

g | gravity acceleration | 9.81 |

${C}_{d}$ | coefficient of aerodynamic drag | 0.7 |

$\rho $ | air density | 1.2041 |

${C}_{r}$ | coefficient of rolling resistance | 0.01 |

${\eta}_{tf}$ | efficiency of mechanical transmission | 0.4 |

$\eta $ | efficiency of the engine | 0.9 |

$\theta $ | road slope angle | 0 |

$\psi $ | energy conversion value | 737 |

Parameter | Interpretation | Value |
---|---|---|

m | vehicle net weight | 1495 kg |

${m}_{0}$ | load weight | 300 kg |

N | engine speed | 33 rpm |

V | engine displacement | 1.395 L |

A | the frontal surface area | 2.721 m${}^{2}$ |

$\kappa $ | fuel calorific value | 43 |

Item | Germany Cologne |
---|---|

Longitude and latitude | 6.762104, 50.772113, 7.223816, 51.127596 |

SUMO boarders | 0.00, 0.00, 32765.27, 34478.96 |

Traffic flow time line | 6:00–8:00 a.m. |

Node number | 30,354 |

Road number | 68,642 |

Road connection number | 190,630 |

Route ID | Head | Tail | Weight | Time Consumption |
---|---|---|---|---|

238549234#3 | 1942418153 | 1942483552 | 0.0201 | 37.98 |

238549234#4 | 1942483552 | 445359497 | 0.0254 | 49.10 |

238549234#5 | 445359497 | 1996182197 | 0.0413 | 61.41 |

238549234#6 | 1996182197 | 445359806 | 0.0165 | 32.43 |

188982766#1 | 445359806 | 445360200 | 0.0232 | 40.39 |

188982766#2 | 445360200 | 1942418274 | 0.0121 | 26.65 |

188982766#3 | 1942418274 | 1996182256 | 0.0098 | 24.87 |

188982766#4 | 1996182256 | 2613699165 | 0.0176 | 35.99 |

188982766#5 | 2613699165 | 445359828 | 0.0165 | 34.09 |

Route ID | Head | Tail | Weight | Time Consumption |
---|---|---|---|---|

… the same as Table 4 … | ||||

238549234#6 | 1996182197 | 445359806 | 0.0451 | 142.73 |

… the same as Table 4 … |

Route ID | Head | Tail | Weight | Time Consumption |
---|---|---|---|---|

238549234#3 | 1942418153 | 1942483552 | 0.0201 | 37.98 |

238549234#4 | 1942483552 | 445359497 | 0.0254 | 49.10 |

238549234#5 | 445359497 | 1996182197 | 0.0413 | 61.41 |

-188982779#4 | 1996182197 | 445359806 | 0.0270 | 63.79 |

37932733#10 | 445359806 | 445360200 | 0.0170 | 33.22 |

188982766#2 | 445360200 | 1942418274 | 0.0121 | 26.65 |

188982766#3 | 1942418274 | 1996182256 | 0.0098 | 24.87 |

188982766#4 | 1996182256 | 2613699165 | 0.0176 | 35.99 |

188982766#5 | 2613699165 | 445359828 | 0.0165 | 34.09 |

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**MDPI and ACS Style**

Peng, T.; Yang, X.; Xu, Z.; Liang, Y. Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods. *Sustainability* **2020**, *12*, 8118.
https://doi.org/10.3390/su12198118

**AMA Style**

Peng T, Yang X, Xu Z, Liang Y. Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods. *Sustainability*. 2020; 12(19):8118.
https://doi.org/10.3390/su12198118

**Chicago/Turabian Style**

Peng, Tu, Xu Yang, Zi Xu, and Yu Liang. 2020. "Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods" *Sustainability* 12, no. 19: 8118.
https://doi.org/10.3390/su12198118