Supply level planning for shared e-scooters considering spatiotemporal heteroscedastic demand
Accurate demand forecasting is a key success for mobility service businesses, especially shared electric (e-)scooters, for their volatile demand, high operational costs, and strict regulations. The heteroscedasticity of transportation demand is usually overlooked even it is very important for design...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Transportation Research Interdisciplinary Perspectives |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590198224000058 |
| _version_ | 1797320919419453440 |
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| author | Narith Saum Mongkut Piantanakulchai Satoshi Sugiura |
| author_facet | Narith Saum Mongkut Piantanakulchai Satoshi Sugiura |
| author_sort | Narith Saum |
| collection | DOAJ |
| description | Accurate demand forecasting is a key success for mobility service businesses, especially shared electric (e-)scooters, for their volatile demand, high operational costs, and strict regulations. The heteroscedasticity of transportation demand is usually overlooked even it is very important for designing efficient supply management. This study proposed a supply planning framework considering heteroscedasticity in the hourly e-scooter demand. Three shared e-scooter datasets (Austin TX, Minneapolis MN, and Thammasat TH) were examined to extract temporal patterns. These features were used as inputs for the demand prediction models, including machine learning and deep learning models. Then, the squared residuals were subjected to variance prediction, including constant or daily variance and variance predicted by Autoregressive Conditional Heteroscedasticity (ARCH). Finally, the outputs of these models were combined to determine the supply level. Four supply level models (with constant, daily, Seasonal Generalized ARCH or SGARCH, and Box Cox variances) were compared based on the Mean Oversupply (MO) metric. As a result, demand prediction models with Box Cox transformed data possibly provide higher prediction accuracy than those with original or normalized data, specifically Mean Absolute Error (MAE). Supply level models with Box Cox variance had the lowest MO at lower percentages of served demand, whereas those with SGARCH variance had lower MO at higher percentages of served demand. At 95 % served demand, considering heteroscedastic demand in supply level planning could reduce oversupply by 26.22 %. From a policy perspective, operators could use our framework to minimize the demand uncertainty for daily operation, along with other potential policies such as customer incentives and hybrid real-time and periodic rebalancing. |
| first_indexed | 2024-03-08T04:50:01Z |
| format | Article |
| id | doaj.art-cd5d6716d4264a4696002939ddea407f |
| institution | Directory Open Access Journal |
| issn | 2590-1982 |
| language | English |
| last_indexed | 2024-03-08T04:50:01Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Research Interdisciplinary Perspectives |
| spelling | doaj.art-cd5d6716d4264a4696002939ddea407f2024-02-08T05:17:04ZengElsevierTransportation Research Interdisciplinary Perspectives2590-19822024-01-0123101019Supply level planning for shared e-scooters considering spatiotemporal heteroscedastic demandNarith Saum0Mongkut Piantanakulchai1Satoshi Sugiura2School of Civil Engineering and Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand; Division of Engineering and Policy for Sustainable Environment, Hokkaido University, Hokkaido 060-0808, JapanSchool of Civil Engineering and Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand; Corresponding author.Division of Engineering and Policy for Sustainable Environment, Hokkaido University, Hokkaido 060-0808, JapanAccurate demand forecasting is a key success for mobility service businesses, especially shared electric (e-)scooters, for their volatile demand, high operational costs, and strict regulations. The heteroscedasticity of transportation demand is usually overlooked even it is very important for designing efficient supply management. This study proposed a supply planning framework considering heteroscedasticity in the hourly e-scooter demand. Three shared e-scooter datasets (Austin TX, Minneapolis MN, and Thammasat TH) were examined to extract temporal patterns. These features were used as inputs for the demand prediction models, including machine learning and deep learning models. Then, the squared residuals were subjected to variance prediction, including constant or daily variance and variance predicted by Autoregressive Conditional Heteroscedasticity (ARCH). Finally, the outputs of these models were combined to determine the supply level. Four supply level models (with constant, daily, Seasonal Generalized ARCH or SGARCH, and Box Cox variances) were compared based on the Mean Oversupply (MO) metric. As a result, demand prediction models with Box Cox transformed data possibly provide higher prediction accuracy than those with original or normalized data, specifically Mean Absolute Error (MAE). Supply level models with Box Cox variance had the lowest MO at lower percentages of served demand, whereas those with SGARCH variance had lower MO at higher percentages of served demand. At 95 % served demand, considering heteroscedastic demand in supply level planning could reduce oversupply by 26.22 %. From a policy perspective, operators could use our framework to minimize the demand uncertainty for daily operation, along with other potential policies such as customer incentives and hybrid real-time and periodic rebalancing.http://www.sciencedirect.com/science/article/pii/S2590198224000058Box Cox TransformationDeep LearningMachine LearningSGARCHShared E-ScootersSupply Planning |
| spellingShingle | Narith Saum Mongkut Piantanakulchai Satoshi Sugiura Supply level planning for shared e-scooters considering spatiotemporal heteroscedastic demand Transportation Research Interdisciplinary Perspectives Box Cox Transformation Deep Learning Machine Learning SGARCH Shared E-Scooters Supply Planning |
| title | Supply level planning for shared e-scooters considering spatiotemporal heteroscedastic demand |
| title_full | Supply level planning for shared e-scooters considering spatiotemporal heteroscedastic demand |
| title_fullStr | Supply level planning for shared e-scooters considering spatiotemporal heteroscedastic demand |
| title_full_unstemmed | Supply level planning for shared e-scooters considering spatiotemporal heteroscedastic demand |
| title_short | Supply level planning for shared e-scooters considering spatiotemporal heteroscedastic demand |
| title_sort | supply level planning for shared e scooters considering spatiotemporal heteroscedastic demand |
| topic | Box Cox Transformation Deep Learning Machine Learning SGARCH Shared E-Scooters Supply Planning |
| url | http://www.sciencedirect.com/science/article/pii/S2590198224000058 |
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