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