How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In-Depth Neural Network Approach with Digital Maps
The identification and analysis of the spatiotemporal dynamic traffic patterns in citywide road networks constitute a crucial process for complex traffic management and control. However, city-scale and synchronal traffic data pose challenges for such kind of quantification, especially during peak ho...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6648116 |
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| author | Li Zhang Ke Gong Maozeng Xu Aixing Li Yuanxiang Dong Yong Wang |
| author_facet | Li Zhang Ke Gong Maozeng Xu Aixing Li Yuanxiang Dong Yong Wang |
| author_sort | Li Zhang |
| collection | DOAJ |
| description | The identification and analysis of the spatiotemporal dynamic traffic patterns in citywide road networks constitute a crucial process for complex traffic management and control. However, city-scale and synchronal traffic data pose challenges for such kind of quantification, especially during peak hours. Traditional studies rely on data from road-based detectors or multiple communication systems, which are limited in not only access but also coverage. To avoid these limitations, we introduce real-time, traffic condition digital maps as our input. The digital maps keep spatiotemporal urban traffic information in nature and are open to access. Their pixel colors represent traffic conditions on corresponding road segments. We propose a stacked convolutional autoencoder-based method to extract a low-dimension feature vector for each input. We compute and analyze the distances between vectors. The statistical results show different traffic patterns during given periods. With the actual data of Chongqing city, we compare the feature extraction performance between our proposed method and histogram. The result shows our proposed method can extract spatiotemporal features better. For the same data set, there is little difference in the number distribution of red pixels found in the statistics result of the histogram, while differences do exist in the results of our proposed method. We find the most fluctuated morning is on Friday; the most fluctuated evening is on Tuesday; and the most stable evening is on Wednesday. The distance captured by our method can represent the evolution of different traffic conditions during the morning and evening peak hours. Our proposed method provides managers with assistance to sense the dynamics of citywide traffic conditions in quantity. |
| format | Article |
| id | doaj-art-468cdcd08e3c43c4ab9dbdaaf6c6dca0 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-468cdcd08e3c43c4ab9dbdaaf6c6dca02025-02-03T06:43:46ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66481166648116How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In-Depth Neural Network Approach with Digital MapsLi Zhang0Ke Gong1Maozeng Xu2Aixing Li3Yuanxiang Dong4Yong Wang5School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan, Shanxi 030006, ChinaSchool of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, ChinaThe identification and analysis of the spatiotemporal dynamic traffic patterns in citywide road networks constitute a crucial process for complex traffic management and control. However, city-scale and synchronal traffic data pose challenges for such kind of quantification, especially during peak hours. Traditional studies rely on data from road-based detectors or multiple communication systems, which are limited in not only access but also coverage. To avoid these limitations, we introduce real-time, traffic condition digital maps as our input. The digital maps keep spatiotemporal urban traffic information in nature and are open to access. Their pixel colors represent traffic conditions on corresponding road segments. We propose a stacked convolutional autoencoder-based method to extract a low-dimension feature vector for each input. We compute and analyze the distances between vectors. The statistical results show different traffic patterns during given periods. With the actual data of Chongqing city, we compare the feature extraction performance between our proposed method and histogram. The result shows our proposed method can extract spatiotemporal features better. For the same data set, there is little difference in the number distribution of red pixels found in the statistics result of the histogram, while differences do exist in the results of our proposed method. We find the most fluctuated morning is on Friday; the most fluctuated evening is on Tuesday; and the most stable evening is on Wednesday. The distance captured by our method can represent the evolution of different traffic conditions during the morning and evening peak hours. Our proposed method provides managers with assistance to sense the dynamics of citywide traffic conditions in quantity.http://dx.doi.org/10.1155/2021/6648116 |
| spellingShingle | Li Zhang Ke Gong Maozeng Xu Aixing Li Yuanxiang Dong Yong Wang How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In-Depth Neural Network Approach with Digital Maps Complexity |
| title | How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In-Depth Neural Network Approach with Digital Maps |
| title_full | How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In-Depth Neural Network Approach with Digital Maps |
| title_fullStr | How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In-Depth Neural Network Approach with Digital Maps |
| title_full_unstemmed | How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In-Depth Neural Network Approach with Digital Maps |
| title_short | How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In-Depth Neural Network Approach with Digital Maps |
| title_sort | how to identify patterns of citywide dynamic traffic at a low cost an in depth neural network approach with digital maps |
| url | http://dx.doi.org/10.1155/2021/6648116 |
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