# Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach

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## Abstract

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## 1. Introduction

- Proposing a solution for dealing with inaccurate and abnormal GPS data on parallel multi-lane arterial roads in Vietnam;
- Designing LSTM network and tuning its hyper-parameters to predict traffic speed on the urban arterial road under historical voyage GPS-monitored data;
- Comparing the proposed method to other standard traffic flow prediction methods. Experiments show that the proposed model outperforms different approaches to traffic speed forecasting.

## 2. Data and Methods

#### 2.1. Parametric Approaches

#### 2.2. Non-Parametric Approaches

#### 2.3. Deep Learning Approaches

## 3. Model Description

## 4. Experimental Data Description

#### 4.1. Data Collection

#### 4.2. Data Pre-Processing

#### 4.2.1. Road Segmentation

#### 4.2.2. Map Matching

- Step 1: Filtering points in the time frame under consideration;
- Step 2: Determining each vehicle’s route (set of points) through the vehicle code;
- Step 3: Removing the outlines for each route.

#### 4.2.3. Computing Speed

_{i}is the i-th segment’s average speed; v

_{ij}is the speed of the i-th segment’s j-th vehicle; and m is some samples of the i-th segment. The average speed as used to train and predict the traffic speed of the arterial road.

#### 4.2.4. Abnormal Data Processing

## 5. Experimental Results and Discussion

#### 5.1. Designing LSTM Network and Dataset Preparation

#### 5.1.1. Designing LSTM Network

#### 5.1.2. Dataset Preparation

#### 5.1.3. Performance Indicator

#### 5.2. Tuning Hyper-Parameters of LSTM Network

#### 5.2.1. Turning the Window Size

#### 5.2.2. Tuning the Number of Epochs

#### 5.2.3. Tuning the Number of Neurons

#### 5.3. Forecasting Results and Discussions

## 6. Conclusions and Future Work

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Description of the experimental road. (

**a**) Location of the experimental road. (

**b**) Experimental road with studied sections.

**Figure 4.**The performance of the proposed model and traditional models. (

**a**) The testing RMSE; (

**b**) the testing MAE; (

**c**) the testing MDAE.

**Figure 5.**Box and whisker plot of testing performance indicators. (

**a**) Summarizing RMSE testing; (

**b**) summarizing MAE testing; and (

**c**) summarizing MDAE testing.

Paper | Models | Experimental Data | Experimental Area | Interval | Source |
---|---|---|---|---|---|

Parametric Approaches | |||||

Pavlyuk et al. [24] | ARIMA, VARMA | From 29 May 2016–3 September 2016 (13 weeks) | Minnesota, USA. Considers only one direction (from southeast to northwest) | 5 min | Loop detector |

Pan et al. [25] | ARIMA, ES, MLP | From 1 November 2011–7 December 2011 | Entire LA county highways and arterial streets | 5 min | Loop detector |

Voort et al. [26] | Kohonen-ARIMA | July and August 1990 | Beaune, where the flow along three feeder motorways converges onto a single motorway | 30 min | Loop detector |

Fusco et al. [27] | Seasonal ARIMA | GPS of private vehicles from August to December 2014 | The primary urban road network of the EUR district in the southern area of Rome | 5 min | 100,000 GPS-equipped private vehicles |

Williams et al. [28] | ARIMAX | From months of July and August from 1984 to 1990. | Four French motorway sites | 30 min | Loop detector |

Non-Parametric Approaches | |||||

Filmon et al. [29] | Enhanced K-NN | Multiple datasets were collected from 3 months to 12 months. | Different regions in the United States and United Kingdom | 15 min | |

Baozhen Yao et al. [30] | SVM | One month of taxi data | Foshan | 30 s | GPS taxi data |

Kit Yan Chan et al. [31] | Hybrid exponential smoothing method and the Levenberg–Marquardt (LM) algorithm | 12 traffic flow data sets (from weeks 38, 41, and 52 in 2008 and weeks 2, 12, and 27 in 2009) | Reid Highway and Mitchell Freeway intersection, western Australia | 1 min (the 2 h peak traffic period (7:30–9:30 a.m.)) | Two detector stations |

Muhammad Zahid et al. [32] | Fast forest quantile regression (FFQR) | From around 06:00 a.m. to 12:00 p.m. | A portion of Beijing’s 2nd freeway Ring Road | Different time intervals, including 5, 10, 15 min | Loop detector |

Deep Learning Approaches | |||||

Ma et al. [33] | Deep convolutional neural networks (deep CNN) | From 1 May 2015 to 6 June 2015 (37 days) | Two sub-transportation networks of Beijing. | 2 min | Taxicab GPS |

Arief Koesdwiady et al. [34] | Deep belief networks (DBNs) | Mixed between history weather and traffic data from 1 August 2013 to 25 November 2013. | San Francisco Bay Area of California, 47 freeways. | 30 s (then aggregated into 5 and 15 min. | The inductive-loop sensors |

Xiaolei Ma et al. [35] | Long short-term neural network (LSTM NN), | From 1 June 2013 to 30 June 2013 | Two separated locations in a major ring road around Beijing | 2 min | Traffic microwave detectors in Beijing |

Jiawei Wang et al. [36] | CNN-LSTM, bi-directional LSTM | Three months, from 23 January 2018 to 22 April 2018 | Xuancheng, China. A road network consists of 112 road segments | 5 min | Automatic vehicle identification (AVI) detectors |

Yisheng Lv et al. [37] | Stacked autoencoder | The first three months of the year 2013 | Freeway systems across California. | 30 s (aggregated to 5 min) | 15,000 loop detectors |

Chen et al. [38] | Combination of the fuzzy method and the deep residual convolution network | The taxicab GPS data | Beijing, China | 48 samples per day | Taxicab GPS |

Zhang et al. [39] | A combination model of spatial-temporal analysis and CNN algorithm | From all weekdays from 1 January to 31 March 2016 | I-5 Freeway in Seattle, USA | 5 min | Loop detector |

Li et al. [40] | A graph and attention-based LSTM network | From 1 April 2016 to 31 August 2016 | Caltrans performance measurement system (PeMS) database | 5 min | 100 detector stations |

Vijayalakshmi et al. [41] | Attention-based CNN-LSTM | Between 1 August 2018 and 30 October 2018 | The location Interstate 405-Northbound | 5 min | Detectors at 37 locations |

No | Segment Name | Optimal Window Size Value | No | Segment Name | Optimal Window Size Value |
---|---|---|---|---|---|

1 | Segment 1 | 11 | 9 | Segment 9 | 96 |

2 | Segment 2 | 42 | 10 | Segment 10 | 35 |

3 | Segment 3 | 73 | 11 | Segment 11 | 65 |

4 | Segment 4 | 97 | 12 | Segment 12 | 16 |

5 | Segment 5 | 46 | 13 | Segment 13 | 94 |

6 | Segment 6 | 47 | 14 | Segment 14 | 11 |

7 | Segment 7 | 40 | 15 | Segment 15 | 56 |

8 | Segment 8 | 47 | 16 | Segment 16 | 97 |

Metric | Model | Count | Mean | Std. | Min | 25% | Median | 75% | Max |
---|---|---|---|---|---|---|---|---|---|

RMSE Test | CNN | 16 | 10.376 | 3.233 | 6.405 | 8.159 | 9.502 | 11.867 | 17.121 |

LSTM | 16 | 9.012 | 1.652 | 6.286 | 8.052 | 8.7 | 10.062 | 12.048 | |

MAE Test | CNN | 16 | 8.142 | 2.65 | 4.869 | 6.436 | 7.393 | 9.258 | 13.358 |

LSTM | 16 | 6.994 | 1.327 | 4.802 | 6.322 | 6.704 | 7.869 | 9.405 | |

MDAE Test | CNN | 16 | 6.736 | 2.265 | 4.159 | 4.981 | 6.461 | 7.434 | 10.976 |

LSTM | 16 | 5.745 | 1.172 | 4.114 | 5.12 | 5.523 | 6.564 | 7.814 |

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## Share and Cite

**MDPI and ACS Style**

Tran, Q.H.; Fang, Y.-M.; Chou, T.-Y.; Hoang, T.-V.; Wang, C.-T.; Vu, V.T.; Ho, T.L.H.; Le, Q.; Chen, M.-H.
Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach. *Sustainability* **2022**, *14*, 6351.
https://doi.org/10.3390/su14106351

**AMA Style**

Tran QH, Fang Y-M, Chou T-Y, Hoang T-V, Wang C-T, Vu VT, Ho TLH, Le Q, Chen M-H.
Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach. *Sustainability*. 2022; 14(10):6351.
https://doi.org/10.3390/su14106351

**Chicago/Turabian Style**

Tran, Quang Hoc, Yao-Min Fang, Tien-Yin Chou, Thanh-Van Hoang, Chun-Tse Wang, Van Truong Vu, Thi Lan Huong Ho, Quang Le, and Mei-Hsin Chen.
2022. "Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach" *Sustainability* 14, no. 10: 6351.
https://doi.org/10.3390/su14106351