A Peak Traffic Congestion Prediction Method Based on Bus Driving Time
<p>Framework of the traffic congestion prediction based on bus driving time (TCP-DT) method. LSTM, long short-term memory; CI-C, congestion index and classification.</p> "> Figure 2
<p>Longitudinal division based on the benchmark of 0° longitude.</p> "> Figure 3
<p>Bus driving time diagram.</p> "> Figure 4
<p>Structure of time prediction based on long short-term memory (T-LSTM) model.</p> "> Figure 5
<p>Structure of LSTM Cell.</p> "> Figure 6
<p>(<b>a</b>) Average driving time of six road sections; (<b>b</b>) distribution of congestion time and index.</p> "> Figure 7
<p>Distribution of real and predicted congestion time: (<b>a</b>) morning peak, (<b>b</b>) evening peak.</p> "> Figure 8
<p>Equal interval classification of predicted data: (<b>a</b>) morning peak, (<b>b</b>) evening peak.</p> "> Figure 9
<p>Natural breakpoint classification of predicted data: (<b>a</b>) morning peak, (<b>b</b>) evening peak.</p> "> Figure 10
<p>Geometric interval classification of predicted data: (<b>a</b>) morning peak, (<b>b</b>) evening peak.</p> ">
Abstract
:1. Introduction
2. Literature Review
3. TCP-DT Method
3.1. Framework
3.2. T-LSTM Model
3.2.1. Driving Time Speculation
- if is east longitude:
- if is west longitude:
- if is north latitude:
- if is south latitude:
3.2.2. Calculating Congestion Time
3.2.3. Congestion Time Prediction
3.3. CI-C Model
3.3.1. Calculating TomTom Congestion Index
3.3.2. Classification of Congestion Level
3.3.3. Calculating Information Entropy
4. Experiment Results and Discussion
4.1. Data Predescription
4.1.1. Bus Station and Line Vector Data
4.1.2. Bus Trajectory Data
4.2. Data Preprocessing
4.3. Prediction Results
4.3.1. Parameter Descriptions
4.3.2. Performance Indicators
4.3.3. Prediction of Congestion Time
4.3.4. Classification of Congestion Levels
4.3.5. Calculating Information Entropy
4.4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Origin | Destination | Section Label |
---|---|---|
Luoshou south residence | Shangjiao | section 1 |
Shangjiao | Wuzhou decoration city | section 2 |
Wuzhou decoration city | Longtan village | section 3 |
Datang village | Tianhe south | section 4 |
Tianhe south | Tianhe bus station | section 5 |
Chuangde shoe factory | West second village | section 6 |
Data Type | Description | Feature |
---|---|---|
Station and line data | Six road sections, total of 66,228 daily records, covering 66 working days | Station name and bus line, ID, latitude, and longitude |
Bus trajectory data | Low-frequency sampling every 60 s | Direction angle, time of data acquisition, bus plate number, instantaneous latitude, longitude, and speed |
Parameter | Description | Value |
---|---|---|
rnn_unit | Number of hidden layer neurons | 10 |
lstm_layers | Number of hidden layers | 3 |
learning_rate | Learning rate in training process | 0.0006 |
keep_prob | Probability of retained neurons in dropout layer | 0.5 |
batch_size | Size of batch training | 40 |
time_step | Time step | 30 |
Station | Peak | ||
---|---|---|---|
section 1 | Morning | 12.7% | 4.02 |
Evening | 13.5% | 3.84 | |
section 2 | Morning | 11.5% | 4.70 |
Evening | 11.3% | 2.90 | |
section 3 | Morning | 8.0% | 35.00 |
Evening | 15% | 13.67 | |
section 4 | Morning | 12.6% | 34.20 |
Evening | 12.1% | 44.50 | |
section 5 | Morning | 10.8% | 8.50 |
Evening | 9.7% | 11.50 | |
section 6 | Morning | 11.9% | 3.05 |
Evening | 12.3% | 11.06 |
Method | Peak | Section 1 | Section 2 | Section 3 | Section 4 | Section 5 | Section 6 |
---|---|---|---|---|---|---|---|
Equal Interval | Morning | 39% | 39% | 73% | 58% | 35% | 47% |
Evening | 31% | 50% | 19% | 54% | 69% | 61% | |
Natural Breakpoint | Morning | 58% | 53% | 66% | 61% | 46% | 62% |
Evening | 58% | 58% | 60% | 54% | 69% | 62% | |
Geometric Interval | Morning | 58% | 53% | 58% | 62% | 49% | 62% |
Evening | 62% | 58% | 62% | 54% | 58% | 61% |
Method | Peak | Section 1 | Section 2 | Section 3 | Section 4 | Section 5 | Section 6 |
---|---|---|---|---|---|---|---|
Equal Interval | Morning | 1.85 | 2.18 | 2.18 | 2.26 | 2.10 | 2.11 |
Evening | 1.81 | 1.97 | 1.65 | 2.19 | 1.98 | 2.06 | |
Natural Breakpoint | Morning | 2.28 | 2.26 | 2.22 | 2.19 | 2.24 | 2.21 |
Evening | 2.23 | 2.28 | 2.28 | 2.30 | 2.28 | 2.28 | |
Geometric Interval | Morning | 2.30 | 2.30 | 2.29 | 2.30 | 2.26 | 2.30 |
Evening | 2.29 | 2.32 | 2.30 | 2.30 | 2.29 | 2.30 |
Peak | Equal Interval | Natural Breakpoint | Geometric Interval |
---|---|---|---|
Morning | 12.68 | 13.40 | 13.76 |
Evening | 11.67 | 13.64 | 13.79 |
Total | 24.35 | 27.04 | 27.55 |
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Huang, Z.; Xia, J.; Li, F.; Li, Z.; Li, Q. A Peak Traffic Congestion Prediction Method Based on Bus Driving Time. Entropy 2019, 21, 709. https://doi.org/10.3390/e21070709
Huang Z, Xia J, Li F, Li Z, Li Q. A Peak Traffic Congestion Prediction Method Based on Bus Driving Time. Entropy. 2019; 21(7):709. https://doi.org/10.3390/e21070709
Chicago/Turabian StyleHuang, Zhao, Jizhe Xia, Fan Li, Zhen Li, and Qingquan Li. 2019. "A Peak Traffic Congestion Prediction Method Based on Bus Driving Time" Entropy 21, no. 7: 709. https://doi.org/10.3390/e21070709
APA StyleHuang, Z., Xia, J., Li, F., Li, Z., & Li, Q. (2019). A Peak Traffic Congestion Prediction Method Based on Bus Driving Time. Entropy, 21(7), 709. https://doi.org/10.3390/e21070709