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Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method
- Source :
- Applied Sciences, Volume 10, Issue 19, Applied Sciences, Vol 10, Iss 6681, p 6681 (2020)
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
Abstract
- The development of the intelligent transport system has created conditions for solving the supply&ndash<br />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 (&ldquo<br />PTX&rdquo<br />). 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 R2 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.
- Subjects :
- online taxi-hailing demand
Schedule
Operations research
Computer science
Taxis
02 engineering and technology
lcsh:Technology
Data-driven
lcsh:Chemistry
Resource (project management)
backpropagation neural network
0502 economics and business
0202 electrical engineering, electronic engineering, information engineering
real-time prediction
General Materials Science
lcsh:QH301-705.5
Instrumentation
Fluid Flow and Transfer Processes
050210 logistics & transportation
Artificial neural network
lcsh:T
business.industry
Process Chemistry and Technology
05 social sciences
General Engineering
Demand forecasting
lcsh:QC1-999
Backpropagation
Computer Science Applications
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Public transport
extreme gradient boosting
020201 artificial intelligence & image processing
lcsh:Engineering (General). Civil engineering (General)
business
lcsh:Physics
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....f0a34f7a0a91e7eea5eb4d401c4cb4fe
- Full Text :
- https://doi.org/10.3390/app10196681