Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional Networks
Accurate and reliable taxi demand prediction is of great importance for intelligent planning and management in the transportation system. To collectively forecast the taxi demand in all regions of a city, many existing studies focus on the capturing of spatial and temporal correlations among regions...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/4427638 |
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| _version_ | 1832552948451770368 |
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| author | Dapeng Zhang Feng Xiao Gang Kou Jian Luo Fan Yang |
| author_facet | Dapeng Zhang Feng Xiao Gang Kou Jian Luo Fan Yang |
| author_sort | Dapeng Zhang |
| collection | DOAJ |
| description | Accurate and reliable taxi demand prediction is of great importance for intelligent planning and management in the transportation system. To collectively forecast the taxi demand in all regions of a city, many existing studies focus on the capturing of spatial and temporal correlations among regions but ignore the local statistical differences throughout the geographical layout of a city. This limits the further improvement of prediction accuracy. In this paper, we propose a new deep learning framework, called the locally connected spatial-temporal fully convolutional neural network ( LC-ST-FCN), to learn the spatial-temporal correlations and local statistical differences among regions simultaneously. We evaluate the proposed model on a real dataset from a ride-hailing service platform (DiDi Chuxing) and observe significant improvements compared with a bunch of baseline models. Besides, we further explore the working mechanism of the proposed model by visualizing its feature extraction processes. The visualization results showed that our approach can better localize and capture useful features from spatial-related regions. |
| format | Article |
| id | doaj-art-e4729289f73e4386a1e0404e4b246b37 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e4729289f73e4386a1e0404e4b246b372025-02-03T05:57:25ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/4427638Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional NetworksDapeng Zhang0Feng Xiao1Gang Kou2Jian Luo3Fan Yang4School of Business AdministrationSchool of Business AdministrationSchool of Business AdministrationChengdu Transportation Operation Coordination CenterChengdu Transportation Operation Coordination CenterAccurate and reliable taxi demand prediction is of great importance for intelligent planning and management in the transportation system. To collectively forecast the taxi demand in all regions of a city, many existing studies focus on the capturing of spatial and temporal correlations among regions but ignore the local statistical differences throughout the geographical layout of a city. This limits the further improvement of prediction accuracy. In this paper, we propose a new deep learning framework, called the locally connected spatial-temporal fully convolutional neural network ( LC-ST-FCN), to learn the spatial-temporal correlations and local statistical differences among regions simultaneously. We evaluate the proposed model on a real dataset from a ride-hailing service platform (DiDi Chuxing) and observe significant improvements compared with a bunch of baseline models. Besides, we further explore the working mechanism of the proposed model by visualizing its feature extraction processes. The visualization results showed that our approach can better localize and capture useful features from spatial-related regions.http://dx.doi.org/10.1155/2023/4427638 |
| spellingShingle | Dapeng Zhang Feng Xiao Gang Kou Jian Luo Fan Yang Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional Networks Journal of Advanced Transportation |
| title | Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional Networks |
| title_full | Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional Networks |
| title_fullStr | Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional Networks |
| title_full_unstemmed | Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional Networks |
| title_short | Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional Networks |
| title_sort | learning spatial temporal features of ride hailing services with fusion convolutional networks |
| url | http://dx.doi.org/10.1155/2023/4427638 |
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