Day-to-day and seasonal regularity of network passenger delay for metro networks

• URL: http://arxiv.org/abs/2107.14094v1
• Date: Wed, 7 Jul 2021 12:28:16 GMT
• Title: Day-to-day and seasonal regularity of network passenger delay for metro networks
• Authors: Panchamy Krishnakumari, Oded Cats and Hans van Lint
• Abstract summary: We propose a novel method for estimating network passenger delay from individual trajectories. We employ temporal clustering to reveal daily and seasonal regularity in delay patterns of the transit network. Our findings show that the average passenger delay is relatively stable throughout the day.
• Score: 8.60488284249921
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In an effort to improve user satisfaction and transit image, transit service providers worldwide offer delay compensations. Smart card data enables the estimation of passenger delays throughout the network and aid in monitoring service performance. Notwithstanding, in order to prioritize measures for improving service reliability and hence reducing passenger delays, it is paramount to identify the system components - stations and track segments - where most passenger delay occurs. To this end, we propose a novel method for estimating network passenger delay from individual trajectories. We decompose the delay along a passenger trajectory into its corresponding track segment delay, initial waiting time and transfer delay. We distinguish between two different types of passenger delay in relation to the public transit network: average passenger delay and total passenger delay. We employ temporal clustering on these two quantities to reveal daily and seasonal regularity in delay patterns of the transit network. The estimation and clustering methods are demonstrated on one year of data from Washington metro network. The data consists of schedule information and smart card data which includes passenger-train assignment of the metro network for the months of August 2017 to August 2018. Our findings show that the average passenger delay is relatively stable throughout the day. The temporal clustering reveals pronounced and recurrent and thus predictable daily and weekly patterns with distinct characteristics for certain months.

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