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

TL;DR

This paper introduces a novel method to estimate and analyze passenger delay patterns in metro networks using smart card data, revealing predictable daily and seasonal regularities to improve service reliability.

Contribution

It proposes a new approach for decomposing and clustering passenger delays, highlighting the regularity of delay patterns across days and seasons in metro systems.

Findings

Average passenger delay remains stable throughout the day.

Distinct daily and weekly delay patterns are recurrent and predictable.

Seasonal variations influence delay characteristics.

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.