System Dynamics Analysis for the Governance Measures Against Container Port Congestion

Container throughput in Shanghai port has been continually increasing, with severe congestion impacts. The effectiveness and comprehensiveness of governance policies are increasingly important. This study proposes a conceptual framework and simulation of congestion governance measures and their effectiveness on congestion control in a data-driven case study for Shanghai port. The contributions herein serve as a starting point in the identification of four alternative congestion governance options (Increasing the transport infrastructure, Multimodal transport, Smart strategy, and Interconnection), and explore dynamic interactions among them. The design of this study is to firstly develop a container port congestion evaluation model to measure the governance effect; secondly to undertake the triangle analysis in the dynamic system balance: demand of hinterland transport, container port congestion, and container port congestion governance; thirdly to describe four congestion governance options with a detailed description of feedback loops generated by the system dynamics model; finally to propose a policy framework aiming at the advantages and disadvantages of these four options. This study function as stepping stones towards the revealing of the biggest challenge in effective mitigation of port congestion systems by being considered not just a hardware issue (option 1) but more importantly organizational issues (options 2~4). It should be a process of coordination and optimization among 4 options.

[1]  Anming Zhang,et al.  Urban Road Congestion and Seaport Competition , 2013 .

[2]  Jaehun Sim,et al.  A carbon emission evaluation model for a container terminal , 2018, Journal of Cleaner Production.

[3]  Chun-Hsiung Liao,et al.  Supply chain integration, information technology, market orientation and firm performance in container shipping firms , 2015 .

[4]  Sang-Moon Soak,et al.  Evaluation of the Marine Traffic Congestion of North Harbor in Busan Port , 2007 .

[5]  Giorgio Mossa,et al.  Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making , 2020 .

[6]  Dylan F. Jones,et al.  Container port infrastructure in north-west Europe: Policy-level modeling , 2012 .

[7]  T. Notteboom,et al.  Off-peak truck deliveries at container terminals: the “Good Night” program in Israel , 2016 .

[8]  Nikolina Brnjac,et al.  Dry Port Terminal Location Selection by Applying the Hybrid Grey MCDM Model , 2020, Sustainability.

[9]  Shima Mohebbi,et al.  Extreme weather events and wastewater infrastructure: A system dynamics model of a multi-level, socio-technical transition. , 2020, The Science of the total environment.

[10]  Kevin X. Li,et al.  Port competition with accessibility and congestion: a theoretical framework and literature review on empirical studies , 2018 .

[11]  Hao Hu,et al.  Multi-agent optimization of the intermodal terminal main parameters by using AnyLogic simulation platform: Case study on the Ningbo-Zhoushan Port , 2020, Int. J. Inf. Manag..

[12]  Gang Chen,et al.  Managing truck arrivals with time windows to alleviate gate congestion at container terminals , 2013 .

[13]  Lu Zhen,et al.  Modeling of yard congestion and optimization of yard template in container ports , 2016 .

[14]  Mee Hong Ling,et al.  Towards Smart Port Infrastructures: Enhancing Port Activities Using Information and Communications Technology , 2020, IEEE Access.

[15]  Robert C. Leachman,et al.  Congestion analysis of waterborne, containerized imports from Asia to the United States , 2011 .

[16]  Xinping Yan,et al.  Safety management of waterway congestions under dynamic risk conditions - A case study of the Yangtze River , 2017, Appl. Soft Comput..

[17]  Hao Hu,et al.  Tactical berth and yard template design at container transshipment terminals: A column generation based approach , 2015 .

[18]  Anming Zhang,et al.  Internalization of port congestion: strategic effect behind shipping line delays and implications for terminal charges and investment , 2016 .

[19]  Xin Liu,et al.  Research on Traffic Congestion Based on System Dynamics: The Case of Chongqing, China , 2020, Complex..

[20]  Debjit Roy,et al.  A non-linear traffic flow-based queuing model to estimate container terminal throughput with AGVs , 2016 .

[21]  Jagan Jeevan,et al.  The Malaysian Intermodal Terminal System: The Implication on the Malaysian Maritime Cluster , 2016 .

[22]  Violeta Roso,et al.  Dry ports: research outcomes, trends, and future implications , 2020, Maritime Economics & Logistics.

[23]  Qingcheng Zeng,et al.  Optimization Model for Truck Appointment in Container Terminals , 2013 .

[24]  Raghav Pant,et al.  Stochastic measures of resilience and their application to container terminals , 2014, Comput. Ind. Eng..

[25]  Pei Liu,et al.  A system dynamics model for emissions projection of hinterland transportation , 2019, Journal of Cleaner Production.

[26]  Hao Hu,et al.  The Introduction to System Dynamics Approach to Operational Efficiency and Sustainability of Dry Port’s Main Parameters , 2019, Sustainability.

[27]  Hyangsook Lee,et al.  A Study on Emissions from Drayage Trucks in the Port City-Focusing on the Port of Incheon , 2019, Sustainability.

[28]  Wayne K. Talley,et al.  Port multi-service congestion , 2016 .

[29]  Mohammad Torkjazi,et al.  Truck appointment systems considering impact to drayage truck tours , 2018, Transportation Research Part E: Logistics and Transportation Review.

[30]  Harry Geerlings,et al.  Dynamics in sustainable port and hinterland operations: A conceptual framework and simulation of sustainability measures and their effectiveness, based on an application to the Port of Shanghai , 2016 .

[31]  Anming Zhang,et al.  Effects of Hinterland Accessibility on U.S. Container Port Efficiency , 2014 .

[32]  Shijie Li,et al.  Planning inland vessel operations in large seaports using a two-phase approach , 2017, Comput. Ind. Eng..

[33]  M. Taskhiri,et al.  Australasian Conference on Information Systems Neagoe et al 2017 , Hobart , Australia Port Terminal Congestion Management 1 Port terminal congestion management . An integrated information systems approach for improving supply chain value , 2017 .