基于回归分析的乘客平均延误时间模型研究

Research on passenger average delay time model based on regression analysis

地铁是大城市交通出行的主要方式。对于地铁乘客来说,延误时间是影响乘客的满意度和地铁的服务水平的一个重要指标,与客流量和发车间隔密切相关。现有研究缺乏基于客流数据的乘客延误时间与客流量、进站时间及发车间隔之间定量关系的研究。文章根据福州地铁1号线的200多万条AFC刷卡原始数据统计分析出6 000多组客流量、进站时间、发车间隔和乘客延误时间的数据集,并通过3种经典回归分析算法进行分析,结果表明采用决策树回归算法的误差最小,可为地铁列车运行时刻表优化提供理论支撑。

Metro is the main mode of transport in big cities. For metro passengers, delay time is an important factor, which is closely related to passenger flow and departure interval, and it affects passenger satisfaction and metro service level. There is a lack of quantitative research on the relationship between passenger delay time and passenger flow, arrival time and departure interval based on passenger flow data. According to more than 6,000 passenger flows generated from statistical analysis on the 2 millions AFC card original data from Fuzhou metro line 1, this paper also analyzes the data sets of stop time, departure intervals and delay time of passengers. Through the three classical regression analysis algorithm, the results show that the minimum error of decision tree regression algorithm provides theoretical support for the metro train timetable schedule optimization.