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An analysis of out-of-home non-work activity time use and timing behaviour based on work schedule and trip time
Agustien M.a, Sjafruddin A.a, Lubis H.A.R.S.a, Wibowo S.S.a
a Faculty of Civil Engineering, Bandung Institute of Technology, Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 The Authors. Published by Elsevier Ltd.This paper attempts to integrate the discrete of activity timing and the continuous data of time allocation based on the concept of utility. The analysis combines the concept of discrete-continuous simulation equations framework to represent the interactions between activity timing and time allocation behaviour during the day. Activity timing choice was modelled as a discrete choice variable involving three alternatives broad periods: home-to-work period, work-based period and post home period. For fixed time workers, it was found that the model in which activity time allocation is assumed to be determined first influence activity timing show statistical measure of fit. Significant parameter of trip characteristics and social economic variables also influence activity timing choice. Activity timing choice model based on time-allocation data can be used to estimate the number of induced and reduce trips and evaluate the impacts of alternative transportation improvement projects to decision to carry out the activities and trips.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Continuous data,Discrete choice,Discrete-continuous simulation,Induced and reduce trips,Non-work activities,Social-economic,Statistical measures,Time allocation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Activity timing,Discrete-continuous simulation equations,Induced and reduce trips,Time allocation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1016/j.proeng.2015.11.044[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]