20 research outputs found

    Accommodating correlation across days in multiple discrete-continuous models for time use

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    The MDCEV modelling framework has established itself as the preferred method for modelling time allocation, with data very often collected through travel or activity diaries. However, standard implementations fail to recognise the fact that many of these datasets contain information on multiple days for the same individual, with possible correlations and substitution between days. This paper discusses how the theoretical accommodation of these effects is not straightforward, especially with budget constraints at the day and multi-day level. We rely on additive utility functions where we accommodate correlation between activities at the within-day and between-day level using a mixed MDCEV model, with multivariate random distributions. We illustrate our approach using a well-known time use datasets, confirming our theoretical points and highlighting the benefits of allowing for correlation across days in terms of model fit and behavioural insights

    Evaluation of penalty and enforcement strategies to combat speeding offences among professional drivers : a Hong Kong stated preference experiment

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    202308 bcchAccepted ManuscriptOthersData-Supported Transportation Operations and Planning (D-STOP) Tier 1 University Transportation Center; U.S. Department of Transportation; Hong Kong Polytechnic University; Hong Kong Arts Development CouncilPublishe

    Modeling the evolution of ride-hailing adoption and usage : a case study of the Puget Sound region

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    202310 bcchAccepted ManuscriptRGCOthersData-Supported Transportation Operations and Planning Center; Center for Teaching Old Models New Tricks (TOMNET); Ministry of Human Resource Development (MHRD) of the Government of IndiaPublishe
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