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Erschienen in: Transportation 1/2009

01.01.2009

Time taken for residents to adopt a new public transport service: examining heterogeneity through duration modelling

verfasst von: Kiron Chatterjee, Kang-Rae Ma

Erschienen in: Transportation | Ausgabe 1/2009

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Abstract

When a new public transport service is introduced it would be valuable for public authorities, financing organisations and transport operators to know how long it will take for people to start to use the service and what factors influence this. This paper presents results from research analysing the time taken for residents living close to a new guided bus service to start to use (or adopt) the service. Data was obtained from a sample of residents on whether they used the new service and the number of weeks after the service was introduced before they first used it. Duration modelling has been used to analyse how the likelihood of starting to use the new service changes over time (after the introduction of the service) and to examine what factors influence this. It is found that residents who have not used the new service are increasingly unlikely to use it as time passes. Those residents gaining greater accessibility benefits from the new service are found to be quicker to use the service, although the size of this effect is modest compared to that of other between-resident differences. Allowance for the possibility that there existed a proportion of the sample that would never use the new service was tested using a split population model (SPD) model. The SPD model indicates that 36% of residents will never use the new service and is informative in differentiating factors that influence whether Route 20 is used and when it is used.

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Fußnoten
1
The different formulation of hazard models for continuous time duration data and discrete time duration data are presented in Bhat (1996, pp. 93–94), Steffensmeier and Jones (1997, pp. 1424–1427) and Jenkins (2004, pp. 13–24). In the continuous time formulation, it is hazard rate that is modelled, while in the discrete time formulation, it is hazard probability that is modelled.
 
2
Discrete duration models without unobserved heterogeneity were estimated in Stata using the built-in routine cloglog. This routine estimates a discrete-time proportional hazard model based on a complementary log–log transformation of the interval hazard rate (Prentice and Gloeckler 1978). Discrete duration models with unobserved heterogeneity were estimated using estimation routines provided by Stephen Jenkins (University of Essex, UK) for Stata users: pgmhaz8 (Jenkins 1997) and hshaz (Jenkins 2004). Continuous duration models were estimated using Stata and Limdep built-in routines. SPD models for continuous data were estimated with Limdep.
 
3
The only known estimation routine for fitting a SPD model with a discrete time formulation is spsurv which has been written for Stata, but this does not allow the probability of event to be modelled with covariates (Lambert 2007). In Limdep Version 8.0 the facility is provided to fit a SPD model with a continuous time formulation and with both the event occurrence and timing parts of the model able to be modelled with covariates (Greene 2002, E27, pp. 25–27).
 
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Metadaten
Titel
Time taken for residents to adopt a new public transport service: examining heterogeneity through duration modelling
verfasst von
Kiron Chatterjee
Kang-Rae Ma
Publikationsdatum
01.01.2009
Verlag
Springer US
Erschienen in
Transportation / Ausgabe 1/2009
Print ISSN: 0049-4488
Elektronische ISSN: 1572-9435
DOI
https://doi.org/10.1007/s11116-008-9183-7

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