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Exploring the trip chaining behaviour of public transport users in Melbourne · Study

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ArticleExploring the trip chaining behaviour of public transport users in Melbourne

Graham Currie2010

Article2010

Exploring the trip chaining behaviour of public transport users in Melbourne

Graham Currie · Alexa Delbosc

106 citations·0/0 files stored·Transport Policy

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Transportation Planning and Optimization Urban Transport and Accessibility Transportation and Mobility Innovations

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References

2009Quantifying spatial gaps in public transport supply based on social needs 2006An exploration of the relationship between mode choice and complexity of trip chaining patterns 2000Trip chaining as a barrier to the propensity to use public transport 2007Defining and understanding trip chaining behaviour 2003Neighborhood services, trip purpose, and tour-based travel 1995Activity, Travel, and the Allocation of Time 1994Effects of household structure and selected travel characteristics on trip chaining 2005Trip-Chaining Trends in the United States: Understanding Travel Behavior for Policy Making

Cited by

2013Tour-based mode choice of joint household travel patterns on weekend and weekday 2011Grocery Shopping 2015Pricing and mode choice based on nested logit model with trip-chain costs 2013Modelling public-transport users’ behaviour at connection point 2016Driving reduction after the introduction of light rail transit: Evidence from an experimental-control group evaluation of the Los Angeles Expo Line

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Year2010

Typearticle

Published2010-08-27

VenueTransport Policy

ISSN-L0967-070X

Languageen

Citations106

Files0 public / 0

ShelfTransportation Planning and Optimization

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doi10.1016/j.tranpol.2010.08.003

openalex_workW2012551394

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2019Travel satisfaction inequality and the role of the urban metro system

2013Multiple purposes at single destination: A key to a better understanding of the relationship between tour complexity and mode choice

2021Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China