Abstract
In recent years, the demand for urban travel is increasing and the travel modes are diverse. Online car Hailing has become an important way to meet the travel needs of residents. The online car-hailing platform receives tens of thousands of travel requests every day. However, a large portion of the thousands of orders are unfinished, that is, canceled by passengers. This not only reduces the income of drivers but also affects the order dispatching efficiency of the online car-hailing platform. To predict the cancellation probability of online car-hailing orders(OCP), the relationship between multi-source heterogeneous data and OCP is first introduced, in which the presence of idle taxis is the main factor for passengers to cancel their orders during the waiting period. Secondly, a deep learning model based on the Seq2Seq structure is designed to predict OCP in real-time. The model consists of an attribute fusion module, encoder layer, and decoder layer. Finally, a full experiment is carried out using the Didi Chengdu online car-hailing order data set to verify the effectiveness of the algorithm.
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Acknowledgement
This research was supported in part by National Key Research and Development Plan Key Special Projects under Grant No. 2018YFB2100303, Shandong Province colleges and universities youth innovation technology plan innovation team project under Grant No. 2020KJN011, Shandong Provincial Natural Science Foundation under Grant No. ZR2020MF060, Program for Innovative Postdoctoral Talents in Shandong Province under Grant No. 40618030001, National Natural Science Foundation of China under Grant No. 61802216, and Postdoctoral Science Foundation of China under Grant No. 2018M642613.
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Sun, H., Lv, Z., Li, J., Xu, Z., Sheng, Z., Ma, Z. (2022). Prediction of Cancellation Probability of Online Car-Hailing Orders Based on Multi-source Heterogeneous Data Fusion. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13472. Springer, Cham. https://doi.org/10.1007/978-3-031-19214-2_14
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