Spatiotemporal Segmentation of Metro Trips Using Smart Card Data

Abstract

Contactless smart card systems have gained universal prevalence in modern metros. In addition to its original goal of ticketing, the large amount of transaction data collected by the smart card system can be utilized for many operational and management purposes. This paper investigates an important problem: how to extract spatiotemporal segmentation information of trips inside a metro system. More specifically, for a given trip, we want to answer several key questions: How long does it take for a passenger to walk from the station gantry to the station platform? How much time does he/she wait for the next train? How long does he/she spend on the train? How long does it take to transfer from one line to another? This segmentation information is important for many application scenarios such as travel time prediction, travel planning, and transportation scheduling. However, in reality, we only assume that only each trip's tap-in and tap-out time can be directly obtained; all other temporal endpoints of segments are unknown. This makes the research very challenging. To the best of our knowledge, we are the first to give a practical solution to this important problem. By analyzing the tap-in/tap-out event pattern, our intuition is to pinpoint some special passengers whose transaction data can be very helpful for segmentation. A novel methodology is proposed to extract spatiotemporal segmentation information: first, for nontransfer trips, by deriving the boarding time between the gantry and the platform, and then, for with-transfer trips, by deriving the transfer time. Evaluation studies are based on large-scale real-system data of the Shenzhen metro system, which is one of the largest metro systems in China and serves millions of passengers daily. Onsite investigations validate that our algorithm is accurate and that the average estimation error is only around 15%.