Assessment of economic impacts from unexpected events with an interregional commodity flow and multimodal transportation network model

2005 ◽  
Vol 39 (10) ◽  
pp. 849-860 ◽  
Author(s):  
Heejoo Ham ◽  
Tschangho John Kim ◽  
David Boyce
Keyword(s):  
Network Model ◽  
Economic Impacts ◽  
Transportation Network ◽  
Unexpected Events ◽  
Commodity Flow ◽  
Multimodal Transportation

scholarly journals Economic Impacts of Natural Disasters Using a Transportation Network-Based Interregional Commodity Flow Model

2009 ◽  
Vol 26 (0) ◽  
pp. 219-227
Author(s):  
Yoshio KAJITANI ◽  
Yuji MIZUKAMI ◽  
Masaru MIINAGAWA ◽  
Ikumasa YOSHIDA
Keyword(s):  
Natural Disasters ◽  
Flow Model ◽  
Economic Impacts ◽  
Transportation Network ◽  
Commodity Flow

scholarly journals Modeling the Multicommodity Multimodal Routing Problem with Schedule-Based Services and Carbon Dioxide Emission Costs

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Yan Sun ◽  
Maoxiang Lang
Keyword(s):  
Carbon Dioxide ◽  
Carbon Dioxide Emissions ◽  
Transportation Network ◽  
Linearization Method ◽  
Mixed Integer ◽  
Multicommodity Flow ◽  
Routing Problem ◽  
Multimodal Transportation ◽  
Proposed Model ◽  
Freight Routing

We explore a freight routing problem wherein the aim is to assign optimal routes to move commodities through a multimodal transportation network. This problem belongs to the operational level of service network planning. The following formulation characteristics will be comprehensively considered: (1) multicommodity flow routing; (2) a capacitated multimodal transportation network with schedule-based rail services and time-flexible road services; (3) carbon dioxide emissions consideration; and (4) a generalized costs optimum oriented to customer demands. The specific planning of freight routing is thus defined as a capacitated time-sensitive multicommodity multimodal generalized shortest path problem. To solve this problem systematically, we first establish a node-arc-based mixed integer nonlinear programming model that combines the above formulation characteristics in a comprehensive manner. Then, we develop a linearization method to transform the proposed model into a linear one. Finally, a computational experiment from the Chinese inland container export business is presented to demonstrate the feasibility of the model and linearization method. The computational results indicate that implementing the proposed model and linearization method in the mathematical programming software Lingo can effectively solve the large-scale practical multicommodity multimodal transportation routing problem.

scholarly journals Impact analysis of two kinds of failure strategies in Beijing road transportation network

2018 ◽  
Vol 29 (01) ◽  
pp. 1850005
Author(s):  
Zundong Zhang ◽  
Xiaoyang Xu ◽  
Zhaoran Zhang ◽  
Huijuan Zhou
Keyword(s):  
Network Model ◽  
Transportation Network ◽  
Dynamic Network ◽  
Network Models ◽  
Global Network ◽  
Statistical Characteristics ◽  
Road Transportation ◽  
Road Infrastructure ◽  
Urban Road ◽  
Infrastructure Network

The Beijing road transportation network (BRTN), as a large-scale technological network, exhibits very complex and complicate features during daily periods. And it has been widely highlighted that how statistical characteristics (i.e. average path length and global network efficiency) change while the network evolves. In this paper, by using different modeling concepts, three kinds of network models of BRTN namely the abstract network model, the static network model with road mileage as weights and the dynamic network model with travel time as weights — are constructed, respectively, according to the topological data and the real detected flow data. The degree distribution of the three kinds of network models are analyzed, which proves that the urban road infrastructure network and the dynamic network behavior like scale-free networks. By analyzing and comparing the important statistical characteristics of three models under random attacks and intentional attacks, it shows that the urban road infrastructure network and the dynamic network of BRTN are both robust and vulnerable.

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