Radiation Range and Carrying Capacity of Logistics Core City: The Case of Xi’an, China

The investment size and construction scale for logistics infrastructure of a city depend on its logistics service scope and logistics carrying capacity primarily, especially for the core city on the Belt and Road Initiative of China at present. We propose a comprehensive field intensity and gravity model in order to demarcate the logistics service scope of the core city according to the field intensity factor, medium factor and interaction factor based on radiation theory. In addition, the logistics service scope is further demarcated into direct radiation scope, indirect radiation scope and extended radiation scope of the importance degree. Then, depending on the demarcation of logistics radiation range, the influencing factors of logistics carrying capacity in Xi’an were analyzed using the gray incidence matrix method. The measurement model of the logistics carrying capacity based on the factor correlation was constructed. The rationality of the model is verified. Xi’an is provided to illustrate the practicability and effectiveness of this method as an example. Furthermore, some suggestions about logistics are provided for core cities on the Belt and Road Initiative.

[1]  Edward K. Morlok,et al.  Measuring capacity flexibility of a transportation system , 2004 .

[2]  Qiang Meng,et al.  Delimiting port hinterlands based on intermodal network flows: Model and algorithm , 2016 .

[3]  J. Bowen.  A spatial analysis of FedEx and UPS: hubs, spokes, and network structure , 2012 .

[4]  Nicoletta González-Cancelas,et al.  How should the Sustainability of the Location of Dry Ports be Measured , 2015 .

[5]  Yanfeng Ouyang,et al.  Optimal layout of transshipment facility locations on an infinite homogeneous plane , 2015 .

[6]  Nicoletta González-Cancelas,et al.  How should the sustainability of the location of dry ports be measured? A proposed methodology using Bayesian networks and multi-criteria decision analysis , 2015 .

[7]  Michael J. Cassidy,et al.  Relation between traffic density and capacity drop at three freeway bottlenecks , 2007 .

[8]  Xuefeng He,et al.  Empirical Study on Influencing Factors of Sichuan Logistics Development Based on Grey Relation Degree Model , 2012 .

[9]  Xin Shi,et al.  Developing the port hinterland: Different perspectives and their application to Shenzhen Port, China , 2016 .

[10]  Yibin Zhang,et al.  Using Voronoi Diagram in Construction the Scope of Logistics Park Hinterland: an Engineering Application , 2011 .

[11]  Y. Sheffi,et al.  Relationship between freight accessibility and logistics employment in US counties , 2013 .

[12]  Jiuh-Biing Sheu,et al.  Forecasting time-varying logistics distribution flows in the One Belt-One Road strategic context , 2017, Transportation Research Part E: Logistics and Transportation Review.

[13]  Hai-Jun Huang,et al.  Stochastic bottleneck capacity, merging traffic and morning commute , 2014 .

[14]  S. Ukkusuri,et al.  Demand clustering in freight logistics networks , 2015 .

[15]  Hai Yang,et al.  Modeling the capacity and level of service of urban transportation networks , 2000 .

[16]  Hai Yang,et al.  A capacity paradox in network design and how to avoid it , 1998 .

[17]  I. Martínez‐Zarzoso,et al.  Relationship between logistics infrastructure and trade: Evidence from Spanish regional exports , 2015 .

[18]  Ramandeep S. Randhawa,et al.  Optimal Flexibility Configurations in Newsvendor Networks: Going Beyond Chaining and Pairing , 2010, Manag. Sci..