The Role of Emerging ICT in the Ports: Increasing Utilities According to Shared Decisions

Geographical location, infrastructures, and services are the main consolidated pillars of a port in terms of its capacity to compete and cooperate with other ports. In the last years, a new pillar was identified: emerging technologies. Ports’ issues were initially solved with individual ICT solutions adopted by each decision-maker, which generated efficiencies in the three main port flows: cargo, information, and financial. However, new benefits and challenges are connected with the introduction of shared emerging ICT among decision-makers inside ports. The crucial issue concerns the fact that several decision-makers could share a decision about a single-port operation. Therefore, the effectiveness and efficiency of ports depend on how the interactions between the decision-makers are solved. Port operations are associated with movements (cargo) and transactions (information and financial) in a synchronic graph, which allows highlighting the role of emerging technologies in the modification of port operation generalized cost, considering the different decision-makers. The focal point concerns the building of a theoretical model using the formal equations of Transport System Models (TSMs) for the estimation of the cost for a Unit of Load (UL), e.g., a container traveling along a path, composed of a sequence of port operations, inside a port with and without emerging technologies. The proposed theoretical model provides the possibility of estimating ex ante the reduction of cost (port time of UL) given by introducing new technologies and a Port Community System (PCS). Different scenarios, considering some cases, ranging from the absence of ICT to the presence of a PCS, are compared, considering the different situations from a non-congested port to a congested one. The main results of the study and its novelty concern, on the one hand, the extension of TSMs to port systems, highlighting the problem of a non-single decision-maker (two or more) in some port operations and, on the other hand, the possibility of reducing the generalized cost (e.g., time) in the same operations in which there are concurrent decision-makers, through the use of an advanced PCS. The reported numerical example confirms the theoretical results. The work can be useful for researchers for port planners (e.g., port authorities) because it permits evaluating the utility for introducing shared emerging technologies using advanced PCS in a unified view.

Analysis of Market Competitiveness of Container Railway Transportation

With the personalized and diversified development of customer demand in the freight market, road transportation has become a main competitor of railway transportation in container transportation due to its high flexibility, convenience, and low prices. Based on the generalized cost and logit model, this paper constructs a container railway goods transport market competitiveness model including four indicators of economy, timeliness, environmental protection, and safety. Take 20 ft container transportation as an example, the impact of changes in railway goods charge and railway travelling speed on the competitiveness of the railway goods transport market is analysed. Some realistic suggestions, including optimizing the railway tariff system, increasing the travelling speed of railway, innovating container intermodal products, and making full use of policy-oriented advantages are concluded.

Charging station planning based on the accumulation prospect theory and dynamic user equilibrium

This study investigates the electric vehicle (EV) traffic equilibrium and optimal deployment of charging locations subject to range limitation. The problem is similar to a network design problem with traffic equilibrium, which is characterized by a bilevel model structure. The upper level objective is to optimally locate charging stations such that the total generalized cost of all users is minimized, where the user’s generalized cost includes two parts, travel time and energy consumption. The total generalized cost is a measure of the total societal cost. The lower level model seeks traffic equilibrium, in which travelers minimize their individual generalized cost. All the utilized paths have identical generalized cost while satisfying the range limitation constraint. In particular, we use origin-based flows to maintain the range limitation constraint at the path level without path enumeration. To obtain the global solution, the optimality condition of the lower level model is added to the upper level problem resulting in a single level model. The nonlinear travel time function is approximated by piecewise linear functions, enabling the problem to be formulated as a mixed integer linear program. We use a modest-sized network to analyze the model and illustrate that it can determine the optimal charging station locations in a planning context while factoring the EV users’ individual path choice behaviours.

Novel iterative ensemble smoothers derived from a class of generalized cost functions

Iterative ensemble smoothers (IES) are among the state-of-the-art approaches to solving history matching problems. From an optimization-theoretic point of view, these algorithms can be derived by solving certain stochastic nonlinear-least-squares problems. In a broader picture, history matching is essentially an inverse problem, which is often ill-posed and may not possess a unique solution. To mitigate the ill-posedness, in the course of solving an inverse problem, prior knowledge and domain experience are often incorporated, as a regularization term, into a suitable cost function within a respective optimization problem. Whereas in the inverse theory there is a rich class of inversion algorithms resulting from various choices of regularized cost functions, there are few ensemble data assimilation algorithms (including IES) which in their practical uses are implemented in a form beyond nonlinear-least-squares. This work aims to narrow this noticed gap. Specifically, we consider a class of more generalized cost functions, and establish a unified formula that can be used to construct a corresponding group of novel ensemble data assimilation algorithms, called generalized IES (GIES), in a principled and systematic way. For demonstration, we choose a subset (up to 30 +) of the GIES algorithms derived from the unified formula, and apply them to two history matching problems. Experiment results indicate that many of the tested GIES algorithms exhibit superior performance to that of an original IES developed in a previous work, showcasing the potential benefit of designing new ensemble data assimilation algorithms through the proposed framework.

Unfalsified control design using a generalized cost function for a quadrotor

Purpose The purpose of this paper is to design a controller for a quadrotor only by using input–output data without a need for the system model. Design/methodology/approach Tracking control for the quadrotor is considered by using unfalsified control, which is one of the most recent strategies of robust adaptive control. The main assumption in unfalsified control design is that there is no access to the system model. Also, ideal path tracking and controlling the quadrotor are been paid attention to in the presence of external disturbances and uncertainties. First, unfalsified control method is introduced which is a data-driven and model-free approach in the field of adaptive control. Next, model of the quadrotor and unfalsified control design for the quadrotor are presented. Second, design of a control bank consisting of four proportional integral derivative controllers and a sliding mode controller is carried out. Findings A particular innovation on an unfalsified control algorithm in this paper is use of a generalized cost function in the hysteresis switching algorithm to find the best controller. Originality/value Finally, the performance and robustness of the designed controllers are investigated by simulation studies in various operating conditions including reference trajectory changes, facing to wind disturbance, uncertainty of the system and changes in payload, which show acceptable performances.

Research on metro passenger flow route selection model based on passengers' familiarity with road network

Road network familiarity is a key attribute that affects passengers’ travel route choice. This paper constructs a differentiated travel generalized cost function based on the passenger’s road network familiarity and the influencing factors of route choice, and uses the Regret Theory to construct a route choice model. By setting passenger decision-making rule weights increase the flexibility of the model. The paper uses the method of combining RP survey and SP survey to conduct route selection behavior survey and calibrate model parameters. Finally, the prediction results before and after the passenger classification are compared with the survey data. The prediction error value is 5.98%, and the prediction accuracy after passenger classification is improved by 6.03%. The effectiveness of the prediction model is verified and the necessity of passenger classification is verified.