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• A mixed bus fleet location-routing-scheduling framework under driving range uncertainty is proposed. • A range reliability (RR)-based two-stage stochastic program is formulated. • An adaptive time-space-energy network is proposed to address diverse forms of the refueling problem. • The vehicle routing, scheduling, and refueling station location problem are solved simultaneously. • Regular services ensure to cover the scheduled trips up to a certain reliability level with minimal total cost. This paper develops a framework to address the multi-depot vehicle location-routing-scheduling problem with multi-vehicle types, including electric buses, under range uncertainty. Three major issues in bus routing and scheduling are addressed in this framework, i.e., uncertain driving range, refueling or charging need, and locating refueling or charging facilities. Mathematically, the problem is formulated as a two-stage stochastic program. Two types of services, regular services running on fixed schedules and ad hoc services to cover incomplete scheduled trips arising from energy shortage of certain buses, are considered in this paper. An adaptive time-space-energy network is developed to model the refueling issues and location problem. We introduce the notion of range reliability to decompose and solve the two-stage stochastic formulation under range uncertainty. The regular services schedule is determined in stage-one to cover the demand under a certain range reliability level. Upon realization of the random driving range, the deployment of ad hoc services is made in stage-two to address the occurrence of energy shortage of certain buses. A range reliability-based gradient algorithm is developed to minimize the expected total cost of the system. We then apply the proposed method to bus services in Hong Kong. The range reliability-based approach shows promising results, leading to substantial cost savings as compared with the traditional methods that ignore the effects of driving range uncertainty. [ABSTRACT FROM AUTHOR]

Coastal ship berth planning and management necessitate accurate identification of ship position and size, yet this is complicated by the presence of diverse ship types and mixed distribution patterns. Traditional survey-based and position-tracking ship monitoring methods have limitations in reflecting the precise spatial information of coastal ships and their surroundings. This paper introduces a novel aerial visual data-driven methodology to estimate the berthing capacity of sheltered space for stationary ships in geographically dispersed water areas. To measure the berthing capacity, image data from an unmanned aerial vehicle (UAV) survey is collected to reflect the spatial information of all types of ships. A deep learning-based computer vision algorithm is used to automatically detect, identify, and classify ships in sample images. Additionally, we introduce the occupancy factor to empirically quantify the spatial proportional correlation between the ship size and the requisite berthing capacity. A validation test of the proposed method is conducted across all sheltered spaces in Hong Kong, and the UAV survey yields over 7,000 informative images of all types of local ships. The ship identification algorithm achieves an average identification rate of 98.6%, demonstrating higher accuracy and reduced labor and time consumption than observational survey and manual counting methods. Findings indicate that the suggested sheltered space for individual ships is 7.75 times the ship size. The approach provides greater flexibility in estimating berthing capacity for mixed berthing forms in extensive or geographically dispersed water bodies, thereby having the potential to support the design of berths, anchorage areas, and typhoon shelter facilities in coastal regions. [ABSTRACT FROM AUTHOR]

• Solved the multiple depot bus scheduling problem with multiple vehicle types. • Formulated the problem as an integer linear program to achieve global optimality. • Developed the time-space-energy network to address the range and refueling issues. • The locations of the refueling stations are optimized simultaneously. • Developed a simplified formulation for solving larger-scale problems. This paper develops a formulation for the multiple depot (MD) vehicle scheduling problem with multiple vehicle types (MVT), including electric buses (EBs), under range and refueling constraints. A novel approach is developed to generate the feasible time-space-energy (TSE) network for bus flow and time-space (TS) network for passenger flow, where the range and refueling issues can be precisely addressed. We then introduce the external cost associated with emissions, and investigate the minimum total system cost to operators and passengers by scheduling the bus fleet and locating the refueling stations. The problem is formulated as an integer linear program (ILP) to find the global optimal solution. For computational efficiency, we develop a simplified formulation based on the TS bus flow network to handle larger-scale problems for approximate solutions. We apply the methods to bus services in Hong Kong to analyze the bus fleet size needed, operational cost, passenger cost, and emissions generated by buses with multiple energy sources. Through the formulation, we study the implications of government subsidy for EBs, bus scheduling in Low Emission Zone (LEZ), and safe driving ratio design for bus refueling. [ABSTRACT FROM AUTHOR]

Despite existing debates on the maritime cluster's institutional composition, geographical boundary, or evolutionary patterns, few studies have empirically scrutinized the relatedness of industrial sectors inside a maritime cluster. This study aims to develop an industry-relatedness model to quantitatively measure the paired industry relatedness among three maritime sectors: port-related, shipping-related, and maritime business services. Maritime sectors can be labour-intensive or capital-intensive, and thereby we constructed a compound ratio of capital per capita to address the heterogeneity issue in evaluating the industry relatedness. Instead of focusing on a specific maritime cluster, we applied the model to three representative maritime clusters, namely Western Europe, Guangdong-Hong Kong-Macao Greater Bay Area (GBA), and the Yangtze River Delta (YRD), to enhance the generalizability of our findings and implications. Findings suggest that the maritime cluster in Western Europe is characterized by more inter-sector connections. In Western Europe, Ship owning and operating , and Ship management and agency are found to operate with the highest relatedness; GBA has a polarized maritime industry structure with Hong Kong specializing in Ship finance and Shipbroking ; cities in YRD operate with a speciality in Shipbuilding and repairing. This study found that differences in industry relatedness are mainly attributable to the focal region's evolutionary history and governance structure. We also provided a valuable reference for policymakers to plan and improve local and regional maritime cluster development. • Modify the co-occurrence model by considering the heterogeneity of different maritime sectors. • Quantify the industry relatedness between different maritime sectors. • Compare three typical maritime clusters in terms of maritime industry-relatedness. [ABSTRACT FROM AUTHOR]

In this study, we develop a Passenger Car Emission Unit (PCEU) framework for estimating traffic emissions. The idea is analogous to the use of Passenger Car Unit (PCU) for modeling the congestion effect of different vehicle types. In this approach, we integrate emission modeling and cost evaluation. Different emissions, typically speed-dependent, are integrated as an overall cost via their corresponding external costs. We then develop a normalization procedure to obtain a general trend that is applicable for all vehicle types, which is used to derive a standard cost curve. Different vehicle types with different emission standards are then mapped to this standard cost curve through their corresponding PCEUs that are to be calibrated. Once the standard cost curve and PCEUs have been calibrated, to estimate the overall cost of emission for a particular vehicle, we only need to multiply the corresponding PCEU of that vehicle type to the standard cost curve. We apply this PCEU approach to Hong Kong and obtain promising results. Compared with the results obtained by the full-blown emission model COPERT, the approach achieves high accuracy but obviates tedious inputs typically required for emission estimation. [ABSTRACT FROM AUTHOR]