Forecasting Airfare Based on Route Characteristics
Zhong Lizhen1,Ma Minshu1(),Zhou Changfeng2
1School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China 2Passenger Transport Department, China State Railway Group Co., Ltd., Beijing 100033, China
[Objective] This paper predicts airfare on routes with fewer daily average flights and incomplete or even no historical data, aiming to help passengers choose better ticketing time.[Methods] We used historical data of multiple routes to predict airfares of the targets. Based on previous research and data, we extracted characteristic variables related to airfare fluctuations. We also classified these variables to establish the airfare forecasting model.[Results] When the model contains variables like the distance and the socio-economic characteristics of the route, the prediction error was significantly reduced.[Limitations] We did not include transit flights and local residents’ income data in our study. More research is needed to evaluate the performance of predicting algorithms.[Conclusions] The characteristics related to the year, the distance between the two places and the socio-economic factors of the routes are the main reasons for airfare fluctuations.
Etzioni O, Tuchinda R, Knoblock C , et al. To Buy or Not to Buy: Mining Airfare Data to Minimize Ticket Purchase Price [C]// Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2003: 119-128.
[2]
Wohlfarth T, Clémençon S, Roueff F , et al. A Data-Mining Approach to Travel Price Forecasting [C]// Proceedings of the 10th International Conference on Machine Learning & Applications & Workshops. 2011: 84-89.
( Gu Zhaojun, Wang Shuang, Zhao Yi . Flight Ticket Fare Prediction Model Based on Time-Serial[J]. Journal of Civil Aviation University of China, 2013,31(2):80-84.)
[4]
Tziridis K, Kalampokas T, Papakostas G A , et al. Airfare Prices Prediction Using Machine Learning Techniques [C]// Proceedings of the 25th European Signal Processing Conference. 2017: 1036-1039.
[5]
Chen Y, Cao J, Feng S , et al. An Ensemble Learning Based Approach for Building Airfare Forecast Service [C]// Proceedings of the 2015 IEEE International Conference on Big Data. 2015: 964-969.
[6]
Xu Y, Cao J . OTPS: A Decision Support Service for Optimal Airfare Ticket Purchase [C]// Proceedings of the 2017 IEEE International Conference on Big Data. 2017: 1363-1368.
( Wang Zhen, Zhang Zhimin, Zhuo Baoling . Research on the Urban Characteristics of Qingdao City Center Based on Multi-source Data[C]//Proceedings of 2018 China Urban Planning Annual Meeting. 2018.
( Lu Xiaohan, Luo Ji, Ju Rui , et al. Research on the Characteristics of National Central Cities Based on Regional Competitiveness[C]//Proceedings of 2018 China Urban Planning Annual Meeting. 2018.
[9]
He D, Lin Y C, Chen J , et al. Microstructural Evolution and Support Vector Regression Model for an Aged Ni-Based Superalloy During Two-Stage Hot Forming with Stepped Strain Rates[J]. Materials & Design, 2018,154:51-62.
