The advent of drones is leading to a paradigm shift in courier services, while their large-scale deployment is confined by a limited range. Here, we design a low-cost product that allows drones to drop parcels onto and pick them up from the roofs of moving passenger vehicles. With this, we propose a ground-air cooperation (GAC) based business model for parcel delivery in an urban environment. As per our case study using real-world data in Beijing, the new business model will not only shorten the parcel delivery time by 86.5% with a comparable cost, but also reduce road traffic by 8.6%, leading to an annual social benefit of 6.67 billion USD for Beijing. The proposed model utilizes the currently “wasted or unused” rooftops of passenger vehicles and has the potential to replace most parcel trucks and trailers, thus fundamentally addressing the congestion, noise, pollution, and road wear and tear problems caused by trucks, and bringing in immense social benefit.

This paper addresses two shortcomings of the data-driven stochastic fundamental diagram for freeway traffic. The first shortcoming is related to the least-squares methods which have been widely used in establishing traffic flow fundamental diagrams. We argue that these methods are not suitable to generate the percentile-based stochastic fundamental diagrams, because the results generated by least-squares methods represent weighted sample mean, rather than percentile. The second shortcoming is widespread use of independent modeling methodology for a family of percentile-based fundamental diagrams. Existing methods are inadequate to coordinate the fundamental diagrams in the same family, and consequently, are not in alignment with the basic rules in probability theory and statistics. To address these issues, this paper proposes a holistic modeling framework based on the concept of mean absolute error minimization. The established model is convex, but non-differentiable. To efficiently implement the proposed methodology, we further reformulate this model as a linear programming problem which could be solved by the state-of-the-art solvers. Experimental results using real-world traffic flow data validate the proposed method.

Ship air emissions are recognized as one of the key concerns of the maritime industry. Competent authorities have issued various regulations to manage air emissions from ships. Although the authorities are policy makers, the effectiveness of policies is up to the shipping industry who operates the vessels and terminals to fulfill maritime transportation works. Given this characteristic, bi-level optimization model has been widely adopted in studies that optimize policy design or evaluate its effectiveness. The framework of a typical bi-level optimization model for ship emission management problem is given to show the basic structure of similar issues. A series of applications of bi-level optimization model in managing ship emissions is reviewed, including cases of Energy Efficiency Design Index, Emissions Control Area, Market Based Measure, Carbon Intensity Indicator, and Vessel Speed Reduction Incentive Program. We hope this paper can enlighten scholars interested in this area and provide help for them.

Maritime transport is the backbone of international trade and globalization. Maritime transport research can be roughly divided into two categories, namely the shipping side and the port side. Most of the classic approaches adopted to address practical problems in these research topics are based on long-term observations and expert knowledge, while few of them are based on historical data accumulated from practice. In recent years, emerging approaches, which we refer to as machine learning and deep learning techniques in this essay, have been receiving a wider attention to solve practical problems. As a relatively conservative industry, there are some initial trials of applying the emerging approaches to solve practical problems in the maritime sector. The objective of this essay is to review the application of emerging approaches to maritime transport research. The main research topics in maritime transport and classic methods developed to solve them are first presented. The introduction of emerging approaches and their suitability to be applied in maritime transport research is then discussed. Related existing studies are then reviewed according to problem settings, main data sources, and emerging approaches adopted. Challenges and solutions in the process are also discussed from the perspectives of data, model, users, and targets. Finally, promising future research directions are identified. This essay is the first to give a comprehensive review of existing studies on developing machine learning and deep learning models together with popular data sources used to address practical problems in maritime transport.

The 76th session of the Marine Environment Committee (MEPC 76) of the International Maritime Organization adopted several mandatory measures in June 2021 to reduce carbon emissions from ships. One of the measures is the carbon intensity indicator (CII), which is the carbon emissions per unit transport work for each ship. Several options of CIIs are available and none of them is chosen to be applied yet. We prove that, at least in theory, requiring the attained annual CII of a ship to be less than a reference value, no matter which CII option is applied, may increase its carbon emissions. Therefore, more elaborate models, combined with real data, should be developed to analyze the effectiveness of each CII option and possibly to design a new CII.