[vc_empty_space][vc_empty_space]
Developing a Probabilistic Model for Constructing Seaport Hinterland Boundaries
Bona Frazila R.a, Zukhruf F.a, Tsavalista Burhani J.a
a Department of Civil Engineering, Institut Teknologi Bandung, 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]© Published under licence by IOP Publishing Ltd.Seaport competitiveness has been attracting a great deal of attention. It is shaped not only by a port’s performance and shipping liner calls but also by its hinterland. However, planners face a number of difficulties for identifying the boundaries of the hinterland, as these are determined by several factors, such as commodity types and final destinations, hinterland connections, inland transport costs, travel costs, and sea shipping costs. Hence, hinterland analysis is often simplified by relating it to the administrative boundaries, which do not correctly represent the actual condition of seaport demand behaviour. Furthermore, the boundaries do not behave deterministically in response to changing factors, which creates a challenge when estimating hinterland boundaries. This paper presents a deterministic model for constructing seaport hinterland boundaries, which considers hinterland connections, generalised transport cost from any point related to the transportation network. Thus, the boundaries do not necessarily coincide with the administrative region. Moreover, a model is proposed for reflecting the stochastic decisions of shippers to select a seaport, which may be utilised for approximating port demand variation. A case study is also presented for exploring the applicability of the proposed model.[/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]Actual conditions,Demand variations,Deterministic modeling,Probabilistic modeling,Sea shipping,Transport costs,Transportation network,Travel costs[/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/10.1088/1755-1315/158/1/012023[/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]