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Title: | A general framework for unmet demand prediction in on-demand transport services | Authors: | Li, W Cao, J Guan, J Zhou, S Liang, G So, WKY Szczecinski, M |
Issue Date: | Aug-2019 | Source: | IEEE transactions on intelligent transportation systems, Aug. 2019, v. 20, no. 8, p. 2820-2830 | Abstract: | Emerging on-demand transport services, such as Uber and GoGoVan, usually face the dilemma of demand supply imbalance, meaning that the spatial distributions of orders and drivers are imbalanced. Due to such imbalance, much supply resource is wasted while a considerable amount of order demand cannot be met in time. To address this dilemma, knowing the unmet demand in the near future is of high importance for service providers because they can dispatch their vehicles in advance to alleviate the impending demand supply imbalance, we develop a general framework for predicting the unmet demand in future time slots. Under this framework, we first evaluate the predictability of unmet demand in on-demand transport services and find that unmet demand is highly predictable. Then, we extract both static and dynamic urban features relevant to unmet demand from data sets in multiple domains. Finally, multiple prediction models are trained to predict unmet demand by using the extracted features. As demonstrated via experiments, the proposed framework can predict unmet demand in on-demand transport services effectively and flexibly. | Keywords: | On-demand transport service Predictability Prediction model Unmet demand |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on intelligent transportation systems | ISSN: | 1524-9050 | EISSN: | 1558-0016 | DOI: | 10.1109/TITS.2018.2873092 | Rights: | ©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication W. Li et al., "A General Framework for Unmet Demand Prediction in On-Demand Transport Services," in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 8, pp. 2820-2830, Aug. 2019 is available at https://doi.org/10.1109/TITS.2018.2873092. |
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Li_General_Framework_Unmet.pdf | Pre-Published version | 7.69 MB | Adobe PDF | View/Open |
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