Open AccessData analytics approach for train timetable performance measures using automatic train supervision data
Author(s) -
Liu Fengbo,
Xu Ruihua,
Fan Wei,
Jiang Zhibin
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2017.0287
Subject(s) - punctuality , headway , computer science , dwell time , process (computing) , analytics , data analysis , automatic vehicle location , taxis , transport engineering , data mining , operations research , engineering , simulation , global positioning system , medicine , clinical psychology , telecommunications , operating system
Motivated by the practical requirements for timetable evaluation in China and the fact that massive train record data are collected but remain largely underused, this study presents a data analytics approach for train timetable performance measures using automatic train supervision data. A data preparation process with data cleaning and matching methods is developed for further analysis. The data analysis consists of three components: a waiting time assessment method that uses visual headway mismatching degree to estimate the effect on waiting time; a process time estimation method that introduces spatiotemporal distribution and statistical techniques to mine the data and identify practical characteristics of dwell time and running time; and an arrival punctuality examination method which checks on‐time arrival performance. The proposed data analytics approach is demonstrated through a case study of Shanghai Metro. Major findings on timetable performance, involving three aspects of timetable parameters, are presented. The relevant data analytics framework and findings have operational and planning implications for urban rail transit authorities and operators with regard to evaluating timetable parameters and improving the service quality.