Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method
The development of the intelligent transport system has created conditions for solving the supply–demand imbalance of public transportation services. For example, forecasting the demand for online taxi-hailing could help to rebalance the resource of taxis. In this research, we introduced a method to...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/19/6681 |
| _version_ | 1797552749213122560 |
|---|---|
| author | Zhizhen Liu Hong Chen Xiaoke Sun Hengrui Chen |
| author_facet | Zhizhen Liu Hong Chen Xiaoke Sun Hengrui Chen |
| author_sort | Zhizhen Liu |
| collection | DOAJ |
| description | The development of the intelligent transport system has created conditions for solving the supply–demand imbalance of public transportation services. For example, forecasting the demand for online taxi-hailing could help to rebalance the resource of taxis. In this research, we introduced a method to forecast real-time online taxi-hailing demand. First, we analyze the relation between taxi demand and online taxi-hailing demand. Next, we propose six models containing different information based on backpropagation neural network (BPNN) and extreme gradient boosting (XGB) to forecast online taxi-hailing demand. Finally, we present a real-time online taxi-hailing demand forecasting model considering the projected taxi demand (“PTX”). The results indicate that including more information leads to better prediction performance, and the results show that including the information of projected taxi demand leads to a reduction of MAPE from 0.190 to 0.183 and an RMSE reduction from 23.921 to 21.050, and it increases R<sup>2</sup> from 0.845 to 0.853. The analysis indicates the demand regularity of online taxi-hailing and taxi, and the experiment realizes real-time prediction of online taxi-hailing by considering the projected taxi demand. The proposed method can help to schedule online taxi-hailing resources in advance. |
| first_indexed | 2024-03-10T16:05:34Z |
| format | Article |
| id | doaj.art-51c6bb3802684c8588da80ade3ff5d65 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:05:34Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-51c6bb3802684c8588da80ade3ff5d652023-11-20T14:56:19ZengMDPI AGApplied Sciences2076-34172020-09-011019668110.3390/app10196681Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning MethodZhizhen Liu0Hong Chen1Xiaoke Sun2Hengrui Chen3College of Transportation Engineering, Chang’an University, Xi’an 710000, ChinaCollege of Transportation Engineering, Chang’an University, Xi’an 710000, ChinaCollege of Transportation Engineering, Chang’an University, Xi’an 710000, ChinaCollege of Transportation Engineering, Chang’an University, Xi’an 710000, ChinaThe development of the intelligent transport system has created conditions for solving the supply–demand imbalance of public transportation services. For example, forecasting the demand for online taxi-hailing could help to rebalance the resource of taxis. In this research, we introduced a method to forecast real-time online taxi-hailing demand. First, we analyze the relation between taxi demand and online taxi-hailing demand. Next, we propose six models containing different information based on backpropagation neural network (BPNN) and extreme gradient boosting (XGB) to forecast online taxi-hailing demand. Finally, we present a real-time online taxi-hailing demand forecasting model considering the projected taxi demand (“PTX”). The results indicate that including more information leads to better prediction performance, and the results show that including the information of projected taxi demand leads to a reduction of MAPE from 0.190 to 0.183 and an RMSE reduction from 23.921 to 21.050, and it increases R<sup>2</sup> from 0.845 to 0.853. The analysis indicates the demand regularity of online taxi-hailing and taxi, and the experiment realizes real-time prediction of online taxi-hailing by considering the projected taxi demand. The proposed method can help to schedule online taxi-hailing resources in advance.https://www.mdpi.com/2076-3417/10/19/6681online taxi-hailing demandbackpropagation neural networkextreme gradient boostingreal-time prediction |
| spellingShingle | Zhizhen Liu Hong Chen Xiaoke Sun Hengrui Chen Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method Applied Sciences online taxi-hailing demand backpropagation neural network extreme gradient boosting real-time prediction |
| title | Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method |
| title_full | Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method |
| title_fullStr | Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method |
| title_full_unstemmed | Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method |
| title_short | Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method |
| title_sort | data driven real time online taxi hailing demand forecasting based on machine learning method |
| topic | online taxi-hailing demand backpropagation neural network extreme gradient boosting real-time prediction |
| url | https://www.mdpi.com/2076-3417/10/19/6681 |
| work_keys_str_mv | AT zhizhenliu datadrivenrealtimeonlinetaxihailingdemandforecastingbasedonmachinelearningmethod AT hongchen datadrivenrealtimeonlinetaxihailingdemandforecastingbasedonmachinelearningmethod AT xiaokesun datadrivenrealtimeonlinetaxihailingdemandforecastingbasedonmachinelearningmethod AT hengruichen datadrivenrealtimeonlinetaxihailingdemandforecastingbasedonmachinelearningmethod |