The Strait of Istanbul, the narrow waterway separating Europe from Asia, holds a strategic importance in maritime transportation as it links the Black Sea to the Mediterranean. It is considered as one of the world's most congested and difficult-to-navigate waterways. Over 55,000 transit vessels pass through the Strait annually, roughly 20% of which carry dangerous cargo. In this study, we have analyzed safety risks pertaining to transit vessel maritime traffic in the Strait of Istanbul and proposed ways to mitigate them. Safety risk analysis was performed by incorporating a probabilistic accident risk model into the simulation model. A mathematical risk model was developed based on probabilistic arguments regarding instigators, situations, accidents, consequences, and historical data, as well as subject-matter expert opinions. Scenario analysis was carried out to study the behavior of the accident risks, with respect to changes in the surrounding geographical, meteorological, and traffic conditions. Our numerical investigations suggested some significant policy indications. Local traffic density and pilotage turned out to be two main factors affecting the risks at the Strait of Istanbul. Results further indicate that scheduling changes to allow more vessels into the Strait will increase risks to extreme levels. Conversely, scheduling policy changes that are opted to reduce risks may cause major increases in average vessel waiting times. This in turn signifies that the current operations at the Strait of Istanbul have reached a critical level beyond which both risks and vessel delays are unacceptable.
Managing the transit vessel traffic in the Strait of Istanbul is a highly complex operation since vessels, weather and water conditions, and a set of regulations affect its operation significantly. At the present time, the Vessel Traffic Services (VTS) operators manage the traffic based on some fundamental rules. After discussions with the VTS, in this report, we present a mathematical formulation of the scheduling process currently in place and validate it by comparing its results with scheduling decisions made by the operators in some days of 2005. The results are highly promising. The fundamental philosophy of the algorithm is to schedule the vessels with highest waiting time first while giving priority to large vessels carrying dangerous cargo. Our goal has been to incorporate the algorithm into a simulation model designed to be used for risk analysis purposes. The proposed algorithm can be slightly altered and used for traffic scheduling in other waterways as well.
Maritime means of transportation have been heavily used for freight throughout recorded history. Although the importance of maritime transportation for passengers has decreased in recent decades as a result of developments in aviation, passengers still use cruises and ferries. However, the demands on maritime transportation for cargo increase daily, result in many accidents, and lead to considerable economic losses and tragic outcomes such as human casualty and environmental damage. These dangers have prompted research for qualifying or quantifying safety risks in ports or waterways. This paper examines the concept of risk analysis and its definition in different fields. Finally, much of the literature related to safety risks in the field of maritime transportation is reviewed. This review may serve as a guide for researchers in the field and government organizations, including regulatory agencies, coast guards, port and waterway stakeholders, and various industries and practitioners.
We built three simulation models that can assist rail transit planners and operators to evaluate high and low probability rail-centered hazard events that could lead to serious consequences for rail-centered networks and their surrounding regions. Our key objective is to provide these models to users who, through planning with these models, can prevent events or more effectively react to them. The first of the three models is an industrial systems simulation tool that closely replicates rail passenger traffic flows between New York Penn Station and Trenton, New Jersey. Second, we built and used a line source plume model to trace chemical plumes released by a slow-moving freight train that could impact rail passengers, as well as people in surrounding areas. Third, we crafted an economic simulation model that estimates the regional economic consequences of a variety of rail-related hazard events through the year 2020. Each model can work independently of the others. However, used together they help provide a coherent story about what could happen and set the stage for planning that should make rail-centered transport systems more resistant and resilient to hazard events. We highlight the limitations and opportunities presented by using these models individually or in sequence.
Simulation, İstanbul Channel, Transportation,
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