The Deep 3D Convolutional Multi-Branching Spatial-Temporal-Based Unit Predicting Citywide Traffic Flow
Recently, for public safety and traffic management, traffic flow prediction is a crucial task. The citywide traffic flow problem is still a big challenge in big cities because of many complex factors. However, to handle some complex factors, e.g., spatial-temporal and some external factors in the in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/21/7778 |
| _version_ | 1827702897307025408 |
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| author | Zain Ul Abideen Heli Sun Zhou Yang Amir Ali |
| author_facet | Zain Ul Abideen Heli Sun Zhou Yang Amir Ali |
| author_sort | Zain Ul Abideen |
| collection | DOAJ |
| description | Recently, for public safety and traffic management, traffic flow prediction is a crucial task. The citywide traffic flow problem is still a big challenge in big cities because of many complex factors. However, to handle some complex factors, e.g., spatial-temporal and some external factors in the intelligent traffic flow forecasting problem, spatial-temporal data for urban applications (i.e., travel time estimation, trajectory planning, taxi demand, traffic congestion, and the regional rainfall) is inherently stochastic and unpredictable. In this paper, we proposed a deep learning-based novel model called “multi-branching spatial-temporal attention-based long-short term memory residual unit (MBSTALRU)” for the citywide traffic flow from lower-level layers to high-level layers, simultaneously. In our work, initially, we have modeled the traffic flow with spatial correlations multiple 3D volume layers and propose the novel multi-branching scheme to control the spatial-temporal features. Our approach is useful for exploring temporal dependencies through the 3D convolutional neural network (CNN) multiple branches, which aim to merge the spatial-temporal characteristics of historical data with three-time intervals, namely closeness, daily, and weekly, and we have embedded features by attention-based long-short term memory (LSTM). Then, we capture the correlation between traffic inflow and outflow with residual layers units. In the end, we merge the external factors dynamically to predict citywide traffic flow simultaneously. The simulation results have been performed on two real-world datasets, BJTaxi and NYCBike, which show better performance and effectiveness of the proposed method than previous state-of-the-art models. |
| first_indexed | 2024-03-10T15:06:59Z |
| format | Article |
| id | doaj.art-5c51b0d5292d49aabf90a66e83abff3b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:06:59Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5c51b0d5292d49aabf90a66e83abff3b2023-11-20T19:39:13ZengMDPI AGApplied Sciences2076-34172020-11-011021777810.3390/app10217778The Deep 3D Convolutional Multi-Branching Spatial-Temporal-Based Unit Predicting Citywide Traffic FlowZain Ul Abideen0Heli Sun1Zhou Yang2Amir Ali3Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, ChinaDepartment of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, ChinaDepartment of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, ChinaDepartment of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaRecently, for public safety and traffic management, traffic flow prediction is a crucial task. The citywide traffic flow problem is still a big challenge in big cities because of many complex factors. However, to handle some complex factors, e.g., spatial-temporal and some external factors in the intelligent traffic flow forecasting problem, spatial-temporal data for urban applications (i.e., travel time estimation, trajectory planning, taxi demand, traffic congestion, and the regional rainfall) is inherently stochastic and unpredictable. In this paper, we proposed a deep learning-based novel model called “multi-branching spatial-temporal attention-based long-short term memory residual unit (MBSTALRU)” for the citywide traffic flow from lower-level layers to high-level layers, simultaneously. In our work, initially, we have modeled the traffic flow with spatial correlations multiple 3D volume layers and propose the novel multi-branching scheme to control the spatial-temporal features. Our approach is useful for exploring temporal dependencies through the 3D convolutional neural network (CNN) multiple branches, which aim to merge the spatial-temporal characteristics of historical data with three-time intervals, namely closeness, daily, and weekly, and we have embedded features by attention-based long-short term memory (LSTM). Then, we capture the correlation between traffic inflow and outflow with residual layers units. In the end, we merge the external factors dynamically to predict citywide traffic flow simultaneously. The simulation results have been performed on two real-world datasets, BJTaxi and NYCBike, which show better performance and effectiveness of the proposed method than previous state-of-the-art models.https://www.mdpi.com/2076-3417/10/21/7778citywide traffic flowmulti-branching spatial-temporalattention-based LSTM |
| spellingShingle | Zain Ul Abideen Heli Sun Zhou Yang Amir Ali The Deep 3D Convolutional Multi-Branching Spatial-Temporal-Based Unit Predicting Citywide Traffic Flow Applied Sciences citywide traffic flow multi-branching spatial-temporal attention-based LSTM |
| title | The Deep 3D Convolutional Multi-Branching Spatial-Temporal-Based Unit Predicting Citywide Traffic Flow |
| title_full | The Deep 3D Convolutional Multi-Branching Spatial-Temporal-Based Unit Predicting Citywide Traffic Flow |
| title_fullStr | The Deep 3D Convolutional Multi-Branching Spatial-Temporal-Based Unit Predicting Citywide Traffic Flow |
| title_full_unstemmed | The Deep 3D Convolutional Multi-Branching Spatial-Temporal-Based Unit Predicting Citywide Traffic Flow |
| title_short | The Deep 3D Convolutional Multi-Branching Spatial-Temporal-Based Unit Predicting Citywide Traffic Flow |
| title_sort | deep 3d convolutional multi branching spatial temporal based unit predicting citywide traffic flow |
| topic | citywide traffic flow multi-branching spatial-temporal attention-based LSTM |
| url | https://www.mdpi.com/2076-3417/10/21/7778 |
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