The spatial and temporal variation in passenger service rate and its impact on train dwell time: A time-series clustering approach using dynamic time warping
place - asia, mode - subway/metro, planning - methods, ridership - behaviour
Clustering, dwell time, dynamic time warping, time series, train
This study aims to improve the understanding of the underlying mechanism of passenger boarding and alighting processes, as well as its potential influence on train dwell time and train operation. Empirical data collected from one of busiest metro stations in Seoul, Korea, demonstrates the spatial and temporal variation in the passenger service rate, as a result of interference between boarding, alighting, and standing passengers. This study postulates that the level of interference can be associated with the train car occupancy and the proximity of train door to entry points, as waiting passengers tend to cluster near the platform entries. A unique temporal service rate curve is derived from each door location. We introduce Dynamic Time Warping for similarity assessment and clustering. It revealed four groups of train doors showing distinct shapes of curve from each platform. The first cluster includes the train doors located closest to the platform entry points where the initial service rate is drastically impeded by severe inference among passengers. The level of interference gradually diminishes as the door is located farther away from the entry points, but the overall service rate decreases as well. A passenger service time model is derived to include the cluster variable. To test its significance, the prediction capability of the model is presented with and without the cluster variable. The results of this study may be used to guide waiting passengers along the platform to minimize interference and to avoid serious disruption during passenger service time.
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Lee, J., Yoo, S., Kim, H., & Chung, Y. (2018). The spatial and temporal variation in passenger service rate and its impact on train dwell time: A time-series clustering approach using dynamic time warping. International Journal of Sustainable Transportation, Vol. 12, pp. 725-736.