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Port cities and their neighbouring areas, located at the confines between sea and land, are key hubs in the transportation of goods and people. Ports serve global transport needs, while they are embedded in local geographies, topographies, political, economic and historical settlements. People have always been attracted to human settlements at the interface of water and land. These settlements have evolved into large population centres and metropolitan areas. Major cities, economic hubs and trade centres are engines of key importance for expansive territories and the ports in their vicinity, but they are also places at the forefront of many contemporary threats, including sea level rise as a consequence of climate change. Today, according to the United Nations Development Programme, 55% of humans worldwide live in cities and 40% live within 100 kms off the coast, thus in the vicinity of water-related threats. Maritime and logistic flows cross ports and densely built territories, creating additional environmental and other challenges. The war in Ukraine, long periods of drought and excessive water levels due to heavy rainfall in Pakistan are only the latest examples of both the need for and the danger of port activities for cities and landscapes. Nonetheless, a comprehensive understanding of the relationships between ports, cities and their territories is missing. This special issue argues that we need to embrace a holistic, inclusive approach to port city development, based on ecosystems values, embedded in various layers of capital: natural, cultural, social, human, industrial and creative. To achieve a port city symbiosis and avoid parasitism—defined here as a relationship where one partner benefits at the expense of another-, new port governance frameworks will have to answer to what knowledge needs to be shared to make multiple value creation in the port city ecosystem happen. For transitions to happen, port city territories will have to nurture ecosystem values to unlock this capital. New governance ...

This paper proposes a novel train trajectory optimization approach for high-speed railways. We restrict our attention to single train operation scenarios with different scheduled/rescheduled running times aiming at generating optimal train recommended trajectories in real time, which can ensure punctuality and energy efficiency of train operation. A learning-based approach deep deterministic policy gradient (DDPG) is designed to generate optimal train trajectories based on the offline training from the interaction between the agent and the trajectory simulation environment. An allocating running time and selecting operation modes (ARTSOM) algorithm is proposed to improve train punctuality and give a series of discrete operation modes (full traction, cruising, coasting, full braking), and thus to produce a feasible training set for DDPG, which can speed up the training process. Numerical experiments show that an optimized speed profile can be generated by DDPG within seconds on a realistic railway line. In addition, the results demonstrate the generalization ability of trained DDPG in solving TTO problems with different running times and line conditions.

Electric car-sharing systems have attracted large attention in recent years as a new business model for achieving both economic and environmental benefits in urban areas. Among different types, the one considered in this paper is the so-called one-way car-sharing system whereby a user can begin and end a trip at any station of the system. At the same time, the Vehicle-to-Grid (V2G) concept is emerging as a possible innovative solution for smart power grid control. A management system that combines car-sharing system operations and V2G technology is a recent challenge for academia and industry. In this work, a mixed integer linear programming formulation is proposed to find the optimal management of electric vehicles in a one-way car-sharing system integrated with V2G technology. The proposed mathematical model allows finding the optimal start-of-day electric vehicles distribution that maximizes the total revenue obtained from system users and V2G profits through daily electric vehicles charging/discharging schedules. These schedules are based on mean daily users' electric vehicles requests and electricity prices. The model can be applied to evaluate the possible average daily profitability of V2G operations. In order to test the model performance, we applied it to a small-size test network and a real-size test network (the Delft network in the Netherlands). Under the model assumptions, the adoption of V2G technology allows to fully cover the daily charging costs due to users’ trips and to obtain V2G profits by taking advantage of electric vehicles unused time without significantly reducing the satisfied car-sharing system demand. Most of the energy purchased to charge the electric vehicles batteries is provided back to the grid during energy peak load demand, creating benefits also for energy providers. ; Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the ...