Visualizing Train Delays Using Tableau and the Framework of a Delay Impact Visualization System
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ORIGINAL PAPER
Visualizing Train Delays Using Tableau and the Framework of a Delay Impact Visualization System Chao Wen1,2 · Xiong Yang1 Received: 22 January 2020 / Revised: 25 April 2020 / Accepted: 30 April 2020 © Springer Nature Singapore Pte Ltd. 2020
Abstract Disturbances in a railway system greatly affect the operation of trains, and lead to train delays. Visual analysis of relevant delay indicators after the occurrences of disturbances is of great significance for dispatch decision-making. First, in this paper, the impact of railway delay indicators is defined and calculated. Next, based on the train delay data of roughly 3 months for the Dutch railway line Amsterdam Central Station-Utrecht Central Railway Station (Asd-Ut), and combined with the tableau visualization platform, a visual display interface of the delay situation is designed to comprehensively display the delay situation in stations and sections, which can assist in decision-making processes for train operations and the organization of these areas. Finally, a delay impact visualization system of railway traffic is designed, considering the system requirements analysis, system construction roadmap, and overall framework design, which provides the theoretical basis for the subsequent development of the visualization system. Keywords Railway · Train operation data · Disturbance · Delay impact · Visualization system
Introduction With the rapid development of railway information infrastructure, train operation performance data can now effectively be recorded and preserved. This data contains information on train running statuses, morning and evening point information, train interactions, equipment utilization statuses and so on. Most recently, the applications of Big-data in railway operations, maintenance, and safety have raised attention of researchers and practitioners (Fumeo et al. 2018; Ghofrani et al. 2018; Oneto et al. 2018; Thaduri et al. 2015; Zarembski 2014). Data-driven methods with Big-data have been widely used to facilitate train dispatching (Wen et al. 2019a). Researchers have conducted a series of studies on railway delays based on train operation performance data, including train delay recovery, prediction and propagation, etc. (Huang et al. 2019a, 2020; Lessan et al. 2018; Wen et al. 2019b). They have produced several optimization models * Chao Wen [email protected] 1
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, Sichuan, China
Railway Research Center, University of Waterloo, Waterloo N2L 3G1, Canada
2
and designed algorithms to solve them. However, train delays are a dynamic process, and the ideal dispatcher can continue taking measures to recover from the current delay, maximally eliminating or reducing delay propagation. The traditional model lacks generalization and cannot reflect the state of the delayed network directly enough for dispatchers to use. Data visualization provides a novel research method for the study of train delays. It consists of a presentation of the data
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