Unified Estimator for Excess Journey Time under Heterogenous Passenger Incidence Behavior using Smartcard Data

TitleUnified Estimator for Excess Journey Time under Heterogenous Passenger Incidence Behavior using Smartcard Data
Publication TypeJournal Article
Year of Publication2013
AuthorsJinhua Zhao, Michael Frumin, Nigel Wilson, Zhan Zhao
JournalTransportation Research Part C
KeywordsExcess journey time, London Overground, Passenger incidence behavior, Service quality, Smartcard data

Excess journey time (EJT), the difference between actual passenger journey times and journey times implied by the published timetable, strikes a useful balance between the passenger’s and operator’s perspectives of public transport service quality. Using smartcard data, this paper tried to characterize transit service quality with EJT under heterogeneous incidence behavior (arrival at boarding stations). A rigorous framework was established for analyzing EJT, in particular for reasoning about passenger’ journey time standards as implied by varying incidence behavior. It was found that although the wrong assumption about passenger incidence behavior and journey time standards could result in a biased estimate of EJT for individual passenger journeys, the unified estimator of EJT proposed in this paper is unbiased at the aggregate level regardless of the passenger incidence behavior (random incidence, scheduled incidence, or a mixture of both). A case study based on the London Overground network (with a tap-in-and-tap-out smartcard system) was conducted to demonstrate the applicability of the proposed method. EJT was estimated using the smartcard (Oyster) data at various levels of spatial and temporal aggregation in order to measure and evaluate the service quality. Aggregate EJT was found to vary substantially across the different London Overground lines and across time periods of weekday service.


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Advanced Mobility Management, Singapore-MIT Alliance for Research and Technology (SMART)

Singapore-MIT Alliance for Research and Technology (SMART), 2016-2020

As part of the Future Urban Mobility (FM) IRG of the Singapore-MIT Alliance for Research and Technology (SMART), the team led by Prof. Zhao combine behavioral science and transportation technology to envision a future urban mobility system for Singapore that integrates public transit, walking and bicycling, shared mobility and autonomous vehicles.  Read More