Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects
The accurate short-term travel demand predictions of ride-hailing orders can promote the optimal dispatching of vehicles in space and time, which is the crucial issue to achieve sustainable development of such dynamic demand-responsive service. The sparse demands are always ignored in the previous m...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/7690309 |
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| author | Zhiju Chen Kai Liu Tao Feng |
| author_facet | Zhiju Chen Kai Liu Tao Feng |
| author_sort | Zhiju Chen |
| collection | DOAJ |
| description | The accurate short-term travel demand predictions of ride-hailing orders can promote the optimal dispatching of vehicles in space and time, which is the crucial issue to achieve sustainable development of such dynamic demand-responsive service. The sparse demands are always ignored in the previous models, and the uncertainties in the spatiotemporal distribution of the predictions induced by setting subjective thresholds are rarely explored. This paper attempts to fill this gap and examine the spatiotemporal sparsity effect on ride-hailing travel demand prediction by using Didi Chuxing order data recorded in Chengdu, China. To obtain the spatiotemporal characteristics of the travel demand, three hexagon-based deep learning models (H-CNN-LSTM, H-CNN-GRU, and H-ConvLSTM) are compared by setting various threshold values. The results show that the H-ConvLSTM model has better prediction performance than the others due to its ability to simultaneously capture spatiotemporal features, especially in areas with a high proportion of sparse demands. We found that increasing the minimum demand threshold to delete more sparse data improves the overall prediction accuracy to a certain extent, but the spatiotemporal coverage of the data is also significantly reduced. Results of this study could guide traffic operations in providing better travel services for different regions. |
| format | Article |
| id | doaj-art-c5a5e7fe49c9487587d79f5da9e85901 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c5a5e7fe49c9487587d79f5da9e859012025-02-03T01:08:01ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/7690309Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand EffectsZhiju Chen0Kai Liu1Tao Feng2School of Transportation and LogisticsSchool of Transportation and LogisticsUrban and Data Science LabThe accurate short-term travel demand predictions of ride-hailing orders can promote the optimal dispatching of vehicles in space and time, which is the crucial issue to achieve sustainable development of such dynamic demand-responsive service. The sparse demands are always ignored in the previous models, and the uncertainties in the spatiotemporal distribution of the predictions induced by setting subjective thresholds are rarely explored. This paper attempts to fill this gap and examine the spatiotemporal sparsity effect on ride-hailing travel demand prediction by using Didi Chuxing order data recorded in Chengdu, China. To obtain the spatiotemporal characteristics of the travel demand, three hexagon-based deep learning models (H-CNN-LSTM, H-CNN-GRU, and H-ConvLSTM) are compared by setting various threshold values. The results show that the H-ConvLSTM model has better prediction performance than the others due to its ability to simultaneously capture spatiotemporal features, especially in areas with a high proportion of sparse demands. We found that increasing the minimum demand threshold to delete more sparse data improves the overall prediction accuracy to a certain extent, but the spatiotemporal coverage of the data is also significantly reduced. Results of this study could guide traffic operations in providing better travel services for different regions.http://dx.doi.org/10.1155/2022/7690309 |
| spellingShingle | Zhiju Chen Kai Liu Tao Feng Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects Journal of Advanced Transportation |
| title | Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects |
| title_full | Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects |
| title_fullStr | Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects |
| title_full_unstemmed | Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects |
| title_short | Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects |
| title_sort | examine the prediction error of ride hailing travel demands with various ignored sparse demand effects |
| url | http://dx.doi.org/10.1155/2022/7690309 |
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