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Mode choice model for working trip under risk and uncertainty
Indriany S.a, Sjafruddin A.a, Kusumawati A.a, Weningtyas W.a
a Civil Engineering Post-Graduate Department, Civil Engineering and Environment Faculty, Institut Teknologi Bandung, 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]© 2018 Author(s).Supplying transportation facilities have been predicted from the mode choice analysis in the context of disaggregate based on the concept of utility maximization. Essentially, it is assumed that the traveler will choose a mode of transportation that has the highest utility. However, due to uncertainty in the recent development of network, people start to consider the trip risks that will impact on the traveler decisions, especially on work trip activities with specific time limitation. A descriptive model as Cumulative Prospect Theory (CPT) can be an alternative decision model made under risk and uncertainty that will affect the magnitude of the probability for transportation mode choice. This study aimed to determine the influence of uncertainty in the networks on the preference of commuter mode (bus and LRT) within South Tangerang and Jakarta regions. In this study, travel demand was obtained from the data travels agenda of household and individual activities with the same origin location or adjacent purpose to get a specific time as a reference point to measure risk. Furthermore, Binomial logit models were used as an approach to include elements of risk (cumulative prospect value) on the attributes of travel time. Referring to the result of the initial assessment, Binomial logit models that incorporate the risks involved in travel time attributes are closer to commuting travel behavior in the study area.[/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][/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][/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/10.1063/1.5042897[/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]