This research investigates the situation of Container Shipping Operational Risk (CSOR) upon the application of blockchain technology by the process of risk identification to risk assessment. It develops and validates novel quantitative tools for risk analysis, such as multiple-event risk scenarios analysis and uncertainty quantification analysis.
To the container shipping industry, not only the identified single-event risk scenarios but also their causal connections, likelihood, consequences, and uncertainty are crucial data for CSOR mitigation and prevention activities.
Container shipping operational risk is the existence of potential hazardous events that might lead to actual negative consequences to the ability of the container shipping services providers (e.g. shipping companies, port operators, freight forwarders) to maintain their services at a certain level of quality, quantity or profitability.
Blockchain, or Distributed Ledger, is mentioned by advocates as a solution to prevent multiple CSORs by improving the efficiency and security of the industry’s information flow. However, the uncertainty in implementation and operation, as well as the immaturity of the technology, creates a favourable environment for new CSORs to develop, hindering the application of the technology.
Container shipping operational risks (CSORs) exist in the logistics operations related to the use of standardised containers for transportation. CSOR is defined in this project as the existence of potential hazardous events (HEs) in operations related to container transportation that may negatively affect the shipping service providers’ ability to maintain their services at a certain level of quality, quantity, and profitability.
Information CSORs were estimated in previous studies to have lower criticality than physical CSORs. The involvement of information CSORs as causal factors in physical or financial HEs, however, has not been investigated rigorously. For example, cyberattacks on the transport system of ports and shipping companies highlighted the relationship between information risks and actual damages in the physical and financial flows.
In recent years, the potential and impact of blockchain technology are increasingly mentioned in both industry and academic communications. Various trials, prototypes, and even commercialisations of this technology have been observed in the industry. There are hopes, promises, advertisements, and demonstrations that blockchain applications could reduce or even eliminate certain types of CSORs. However, there are also sceptical views of stakeholders regarding the current true state of the technology and the solutions developed from it.
These observations suggest the necessity of a deeper, up-to-date, and anticipative CSOR understanding, which cannot be offered by the current state-of-the-art. To maintain a level of service quality, manufacturing, and profitability, parties in the container shipping industry must cope with CSORs by a regime of risk management. A core of risk management is being aware of the risk situation through risk analysis.
Consider container shipping. Multiple factors are nurturing the formation of new operational risks in container shipping systems, which are becoming more connected under the effects of digitalisation and will be even more connected with higher extent of blockchain integration.
These factors include unestablished legal frameworks, lacklustre technology understandings, risk accumulation – multiple-event scenarios with unreliable insurability, the increasing potential of cyberattacks, dependence and fixation on legacy systems, and low interoperability between blockchain applications. They suggest the possibility of CSORs associated with container shipping systems that have blockchain integration. However, this possibility has not been confirmed nor investigated in the literature.
The aim of the project is to investigate the situation of CSOR upon the application of blockchain technology in the industry. Investigating and understanding the risk situation requires conducting a risk analysis, which includes risk identification – description of potential scenarios, and risk assessment – description of these scenarios’ magnitude. For risk assessment, a risk quantitative analysis model is also developed to consider the characteristics of CSOR.
- Obtaining the perspective and insights of the container shipping service providers (i.e., port operators, container shipping companies, freight forwarders) about the application of blockchain in the industry (e.g. settings, functions, connections with existing systems).
- Identification of potential operational risks in container shipping systems with the application of blockchain technology.
- Identification of multiple-event risk scenarios in which an initial HE can cause other consequential HEs that cause cumulative consequences.
- Development of a risk analysis model to assess the identified risks considering the characteristics of the identified CSORs and risk general characteristics (i.e., uncertainty, ambiguity, and complexity).
- Prioritisation of the identified CSORs reflecting the situation of risk in the perspective of container shipping service providers.
Traffic congestion is the result of traffic demand exceeding the roadway supply capacity, which could be identified by low speeds, longer travel times and lengthy vehicle queues. The economic and population growth, enhancement in communal needs and lifestyles are major factors which contribute to high travel demands and traffic congestion.
Even though economic activities influence the traffic congestion, the growth and stability of an economy are at the mercy of traffic congestion. Department of Infrastructure and Regional Development identified that the congestion cost of Australia was $16.5 billion in 2015 and expected to reach between $ 27.7 -37.3 billion by 2030 if major policy changes were not introduced, which will be a higher burden for the Australian economy. Further, Australian Automobile Association finds that major cities in Australia such as Sydney, Melbourne, and Brisbane are facing rapid population booms due to urban sprawl, which may result to increase congestion and may cause traffic gridlock in the near future unless decisive action is taken.
The introduction of novel traffic monitoring and management techniques are an attractive avenue to manage the traffic congestion as building new infrastructure is not a justifiable solution. Demand management strategies and traffic control strategies are the broader categories of methods to reduce traffic congestion found in the literature. Although there are numerous traffic control strategies in literature, less work has been focused on demand management strategies.
After a careful review of the existing literature, we break down traffic demand management strategies into two categories:
- Strategies focused on reducing demand or shifting the travellers to other transport modes such as public transport, ridesharing, parking restrictions etc.
- The strategies focused on redistribution of demand over space and time such as route guidance, congestion pricing, peak-hour pricing, flexible working hours, etc.
We see that a vast number of studies were focused on spatial redistribution of demand and less attention was given to temporal redistribution of demand although such strategies have a very high potential in mitigating traffic congestion. At the same time, we see that the successful implementation of any demand management strategy often relies on accurate demand estimates. Nevertheless, existing demand estimation techniques face challenges in scalability to large scale networks.
Given the above research gaps, this study will focus on developing demand management and demand estimation techniques for large scale traffic networks. Our study will build upon macroscopic traffic models based on the macroscopic fundamental diagram (MFD) to understand the complex interaction of large-scale traffic networks. MFD enables analytically tractable approaches for complex problems in demand management and estimation.
The aim of this study is set to develop demand management and demand estimation tools for large-scale traffic networks. Two objectives are accomplished in this project where we focus on developing demand management and demand estimation strategies for large-scale traffic networks by incorporating the MFD-based traffic dynamics.
A thorough review of the literature shows that there are no promising methods to demand management and demand estimation in large scale traffic networks. The existing traffic demand management (TDM) strategies often face incompliance problems as they require substantial schedule changes. Hence, there is a need for developing TDM strategies which target to achieve system optimum conditions by limited schedule changes to travellers.
On the other hand, there exist demand uncertainties due to technology penetration of advanced traveller information systems and incompliance of travellers to TDM strategies. The existing demand estimation methods suffer challenges in scalability to large-scale networks and reliability in estimates. Thus, there is a need to develop computationally feasible methods to estimate demand using analytically tractable macroscopic traffic models.
Two objectives are formulated to bridge the research gap.
Develop a method to manage the demand in a large-scale traffic network by limited schedule changes.
Develop a robust demand estimation method applicable to large-scale and complex traffic networks.
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