Strategic maritime container transport design in oligopolistic markets

AbstractThis paper considers the maritime container assignment problem in a market setting with two competing firms. Given a series of known, exogenous demands for service between pairs of ports, each company is free to design a liner service network serving a subset of the ports and demand, subject to the size of their fleets and the potential for profit. The model is designed as a three-stage complete information game: in the first stage, the firms simultaneously invest in their fleet; in the second stage, they individually design their networks and solve the route assignment problem with respect to the transport demand they expect to serve, given the fleet determined in the first stage; in the final stage, the firms compete in terms of freight rates on each origin-destination movement. The game is solved by backward induction. Numerical solutions are provided to characterize the equilibria of the game

Exploring pathways for sustainable water management in river deltas in a changing environment

Exploring adaptation pathways into an uncertain future can support decisionmaking in achieving sustainable water management in a changing environment. Our objective is to develop and test a method to identify such pathways by including dynamics from natural variability and the interaction between the water system and society. Present planning studies on long-term water management often use a few plausible futures for one or two projection years, ignoring the dynamic aspect of adaptation through the interaction between the water system and society. Our approach is to explore pathways using multiple realisations of transient scenarios with an Integrated Assessment Meta Model (IAMM). This paper presents the first application of the method using a hypothetical case study. The case study shows how to explore and evaluate adaptation pathways. With the pathways it is possible to identify opportunities, threats, timing and sequence of policy options, which can be used by policymakers to develop water management roadmaps into the future. By including the dynamics between the water system and society, the influence of uncertainties in both systems becomes clearer. The results show, among others, that climate variability rather than climate change appears to be important for taking decisions in water management

The Role of Trust and Interaction in GPS Related Accidents: A Human Factors Safety Assessment of the Global Positioning System (GPS)

The Global Positioning System (GPS) uses a network of orbiting and geostationary satellites to calculate the position of a receiver over time. This technology has revolutionised a wide range of safety-critical industries and leisure applications ranging from commercial fisheries through to mountain running. These systems provide diverse benefits; supplementing the users existing navigation skills and reducing the uncertainty that often characterises many route planning tasks. GPS applications can also help to reduce workload by automating tasks that would otherwise require finite cognitive and perceptual resources. However, the operation of these systems has been identified as a contributory factor in a range of recent accidents. Users often come to rely on GPS applications and, therefore, fail to notice when they develop faults or when errors occur in the other systems that use the data from these systems. Further accidents can stem from the ‘over confidence’ that arises when users assume automated warnings will be issued when they stray from an intended route. Unless greater attention is paid to the human factors of GPS applications then there is a danger that we will see an increasing number of these failures as positioning technologies are integrated into increasing numbers of application

Predicting Marine Traffic in the Ice-Covered Baltic Sea

Icebreaking activity and seasonal ice propose challenges for marine traffic prediction in the Baltic Sea. Traffic prediction is a vital part in the planning of icebreaking activities, but it remains largely as a manual task. The aim of this thesis is to examine factors influencing marine traffic modelling in ice-covered waters and propose a novel A*-based method for modelling traffic in ice. The current state of the marine traffic modelling and factors affecting vessel movement are concluded by examining the literature and historical vessel tracks. The field of traffic modelling research is growing rapidly. Currently the biggest challenges are evaluation of results and the lack of publicly available datasets. Moreover, the current approaches to model vessel movement in ice are promising but fail to capture how icebreaking activity influences vessel routes. The proposed model consists of sea, maneuverability, route and speed modelling. The model uses historical AIS data, topography of the sea, vessel type and dirways as main data inputs. The model is trained with summer tracks and dirways are used for modelling the ice channels kept open by icebreakers. The accuracy of the model is evaluated by examining route, speed, traffic and ETA (estimated time of arrival) prediction results separately. Moreover, the area between the actual and predicted route is introduced as an accuracy measure for route prediction. The model shows that winter route prediction can be improved by incorporating dirways to the modelling. However, the use of dirways did not affect the speed, traffic or ETA prediction accuracy. Finally, the datasets and source code used in this thesis are published online

Predicting Marine Traffic in the Ice-Covered Baltic Sea

Icebreaking activity and seasonal ice propose challenges for marine traffic prediction in the Baltic Sea. Traffic prediction is a vital part in the planning of icebreaking activities, but it remains largely as a manual task. The aim of this thesis is to examine factors influencing marine traffic modelling in ice-covered waters and propose a novel A*-based method for modelling traffic in ice. The current state of the marine traffic modelling and factors affecting vessel movement are concluded by examining the literature and historical vessel tracks. The field of traffic modelling research is growing rapidly. Currently the biggest challenges are evaluation of results and the lack of publicly available datasets. Moreover, the current approaches to model vessel movement in ice are promising but fail to capture how icebreaking activity influences vessel routes. The proposed model consists of sea, maneuverability, route and speed modelling. The model uses historical AIS data, topography of the sea, vessel type and dirways as main data inputs. The model is trained with summer tracks and dirways are used for modelling the ice channels kept open by icebreakers. The accuracy of the model is evaluated by examining route, speed, traffic and ETA (estimated time of arrival) prediction results separately. Moreover, the area between the actual and predicted route is introduced as an accuracy measure for route prediction. The model shows that winter route prediction can be improved by incorporating dirways to the modelling. However, the use of dirways did not affect the speed, traffic or ETA prediction accuracy. Finally, the datasets and source code used in this thesis are published online

A shipping simulation through pathfinding: SEL within the MSP Challenge simulation platform

The authors present the design of the shipping simulation SEL and its integration in the MSP Challenge Simulation Platform. This platform is designed to give policymakers and planners insight into the complexity of Maritime Spatial Planning (MSP) and can be used for interactive planning support. It uses advanced game technology to link real geo- and marine data with simulations for ecology, energy and shipping. The shipping sector is an important economic sector with influential stakeholders. SEL calculates the (future) impact of MSP decisions on shipping routes. This is dynamically shown in key performance indicators (e.g. route efficiencies) and visualised in heat maps of ship traffic. SEL uses a heuristic-based graph-searching algorithm to find paths from one port to another during each simulated month. The performance of SEL was tested for three sea basins: the firth of Clyde, Scotland (smallest), North Sea (with limited data) and Baltic Sea regions (largest, with most complete data). The behaviour of the model is stable and valid. SEL takes between 4 and 17 seconds to generate the desired monthly output. Experiences in 20 sessions with 302 planners, stakeholders and students indicate that SEL is a valuable addition to MSP Challenge, and thereby to MSP

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluation