基于因素分析的出租车运力规模预测

随着网约车的兴起和城市公共交通建设的不断完善, 传统出租车行业面临的竞争不断加剧, 可能出现出租车运力规模过剩的问题. 通过影响因素分析, 筛选8个主要因素, 构建了基于多元回归的出租车运力规模关系模型, 利用宁波市客管局实测数据进行模型标定, 并引入网约车修正系数, 预测未来年的出租车运力规模. 结果表明: 2017年宁波市出租车运力需求为增长态势, 而巡游出租车运力规模因网约车的冲击仍将减少.

Factor analysis based prediction of taxi capacity scale

With the development of internet-based car services and the improvement of urban public transportation systems, the operation of traditional taxi industry becomes more and more difficult. Problems arising from the excessive capacity supply of taxi have been more and more noted by the public. To predict a reasonable taxi capacity scale, we construct a taxi capacity scale relational model based on eight major factors using multiple regression method. The model is calibrated using empirical data from the Ningbo passenger transportation authority. By citing correction factor of network cars, the model, can be used to predict taxi capacity scale for next year. The results reveal that for Ningbo in 2017 the taxi transport capacity demand may climb, but taxi capacity scale should be shrunk given the impacts of internet-based car service on the conventional operation mode with taxi business.