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To reduce emissions, shipping companies are deploying eco-friendly ships, like those powered by LNG or methanol. Unlike traditional fuels, LNG bunkering is available at a limited number of ports. At the same time, tramp or bulk ships have fewer ports of call on their shipping lines, which may necessitate LNG-fueled ships sailing to alternative bunkering ports for refueling. The selection of bunkering ports impacts the operating costs of shipping lines due to differences in fuel prices and geographical locations. Moreover, it must factor in ship speed limits to meet loading and unloading schedules. To optimize the shipping route, speeds, and bunkering plan for LNG-fueled tramp ships, we develop an arc-based mixed-integer linear programming model with the objective of minimizing total operation costs. Subsequently, we propose a leg-based formulation based on the predetermined port call orders. Finally, case studies are conducted to demonstrate the effectiveness and efficiency of the models. Experimental results indicate that the bunkering mode with alternative bunkering ports effectively reduces total operating costs without altering the total voyage time, particularly with substantial differences in fuel prices between ports. • A novel optimization problem for the LNG-fueled tramp shipping line is introduced. • Route, speed, and bunkering for the tramp shipping line are jointly optimized. • An arc-based and a leg-based mixed-integer programming model are proposed. • Case studies using instances derived from a tramp shipping line are conducted. [ABSTRACT FROM AUTHOR]

Although urban rail transit systems play a crucial role in urban mobility, they frequently suffer from unexpected disruptions due to power loss, severe weather, equipment failure, and other factors that cause significant disruptions in passenger travel and, in turn, socioeconomic losses. To alleviate the inconvenience of affected passengers, bus bridging services are often provided when rail service has been suspended. Prior research has yielded various methodologies for effective bus bridging services; however, they are mainly based on the strong assumption that passengers must follow predetermined bus bridging routes. Less attention is paid to passengers' path choice behaviors, which could affect the performance of the bus bridging services deployed. In this paper, we specifically take passengers' path choice behaviors into account and address the bus bridging optimization problem under urban rail transit disruptions. Incorporating a PS-logit model to estimate the probabilities of passenger path choices, we propose a mixed-integer nonlinear programming model to simultaneously determine the selection of bus bridging routes and vehicle deployment on selected bridging routes, with the objective of minimizing the cost associated with passenger travel time and unsatisfied demand. To solve this computationally challenging large-scale nonlinear model, we design a customized variable neighborhood search algorithm framework. A case study based on the Shanghai rail transit system is conducted to demonstrate the applicability and feasibility of the proposed approach. The results indicate that our approach can provide an effective bus bridging scheme that considers passenger path choice, which facilitates rapid response to rail disruptions. Our scheme substantially outperforms the current bridging designs that do not consider passenger path choice behaviors by significantly reducing the number of unserved passengers. • A path-choice-constrained bus bridging design under rail disruptions is introduced. • A programming model is developed for route selection and bus allocation. • A PS-logit model is incorporated to estimate the probabilities of path choices. • A customized variable neighborhood search framework is designed to solve the model. • A case study based on the Shanghai rail transit system is conducted. [ABSTRACT FROM AUTHOR]

With increasing container-shipping traffic, congestion at transshipment hub ports happens from time to time incurring longer-than-expected waiting time for vessels and loss of transshipment connections. This situation is even worse for feeder companies, due to their relatively lower berthing priority. It is essential to design the feeder vessel routes and schedules in response to hub port congestion while ensuring efficient transshipment connection with their connecting long-haul services. In this paper, we study the vessel routing and transshipment coordination problem for a feeder liner company where only limited choices of berthing time slots are available at the hub port. We proposed an arc-based mixed integer linear programming model and a set partitioning model with the objective of minimizing the total operating and transshipment connection cost. Two solution approaches are developed (1) a branch-&-price exact algorithm that can solve instances with up to 30 ports to optimality within reasonable computational time; and (2) a column generation heuristic method that yields high-quality near-optimal solutions very efficiently without having to implement a full-scale branch-&-bound search. Computational experiments based on a real-world shipping network and randomly generated test instances confirm the effectiveness and efficiency of the solution approaches. It is also demonstrated that, by integrating the hub port calling time decision with feeder vessel routing, transshipment coordination can be significantly enhanced even under hub port congestion situations. • Investigate feeder vessel routing and transshipment coordination for shipping companies. • Propose a set partitioning formulation and a branch-&-price exact algorithm. • Design a column generation based heuristic approach. • Conduct real-world case studies based on APL's container shipping networks. • Container transshipment at the hub port can be significantly improved. [ABSTRACT FROM AUTHOR]

In recent years, natural disasters happening over the world pose severe challenges to emergency evacuation systems, especially for highly-populated urban community areas. Underground spaces not systematically utilized yet provide a new development direction for the construction of emergency shelters. Emergency response for mitigating negative impacts of disasters calls for well-designed shelter system and effective management of pedestrian evacuation. In this paper, we study the location selection of underground emergency shelters in urban community areas, along with pedestrian evacuation planning once disaster happens. The aim is to effectively use the existing underground space and sidewalk network to satisfy pedestrian evacuation demand as much as possible. We formulate the problem as a network flow model, and develop a minimum-cost-maximum-flow solution approach. A real-world case study based on the East Nanjing Road community area in central Shanghai is conducted. The results show that sidewalks are the bottleneck of the pedestrian evacuation network due to their limited capacity. Reserving vehicle lanes, implementing one-way evacuation, and opening local small-scale underground shelters, are shown to be effective measures for improving the pedestrian evacuation performance. Overall, the proposed methodology and managerial insights based on the case study in Shanghai show a great potential of employing underground space as shelters for pedestrian evacuation system design in urban community areas. [ABSTRACT FROM AUTHOR]

Airport buses play a crucial role in addressing the last-mile problem of air travel, especially in cities and countries lacking inner-city rail transit systems. Nevertheless, airport buses are currently witnessing a decline in ridership due to drawbacks such as long departure intervals, inflexible stops, and considerable distances between stops. Consequently, delivering high-quality airport bus services has become a pressing concern for public transport operators. Motivated by new flexible buses and ride-sharing services, this paper explores a flexible airport bus service that integrates ride-sharing services for passengers traveling from bus stops to their destinations. This problem entails integrated decisions involving bus stop selection, passenger assignment to drop-off bus stops, as well as bus and ride-sharing routing. Accordingly, this problem presents more challenges in decision-making than traditional flexible bus or ride-sharing routing problems. We first develop an arc-based mixed-integer linear programming model. Subsequently, we design a double decomposition math-heuristic algorithm that builds upon logic-based Benders decomposition and column generation algorithms to obtain a near-optimal solution within practical computation time limits for practical-scale instances. Additionally, we implement an adaptive large neighborhood search algorithm to evaluate the solution quality of this math-heuristic algorithm and to solve large-scale instances. To validate the effectiveness of both the model and the algorithms, we conduct numerical experiments using instances derived from Shenzhen airport bus lines. The experimental results demonstrate that the flexible service mode offers significant advantages in reducing both passenger ride time and vehicle mileage over traditional airport bus or taxi modes. • The flexible airport bus problem with last-mile ride-sharing services is introduced. • An arc-based mixed-integer programming model is proposed. • A logic-based Benders decomposition math-heuristic algorithm is designed. • An adaptive large neighborhood search algorithm is developed. • Numerical experiments using instances derived from Shenzhen airport are conducted. [ABSTRACT FROM AUTHOR]

The burden of first-mile connection to public transit stations is a key barrier that discourages riders from taking public transportation. Public transit agencies typically operate a modest fleet of vehicles to provide first-mile services due to the high operating costs, thus failing to adequately meet the first-mile travel demands, especially during peak hours. At the same time, private cars are underutilized and have a lot of idle time. With the emergence of self-driving vehicles, new opportunities for addressing the current dilemma arise, such as integrating idle private self-driving vehicles to provide first-mile services, which is beneficial for public transportation agencies to provide high-quality services at low costs. This study investigates the first-mile ridesharing problem in which public transit agencies utilize idle privately-owned autonomous vehicles to dynamically inflate their fleet. This problem is more challenging in decision-making than conventional first-mile problems, as it involves decisions on heterogeneous fleet scheduling, vehicle routing, and time scheduling, all while taking into account the service quality for riders. To address this problem, an arc-based mixed-integer linear programming (MILP) model and a trip-based set-partitioning model are developed, both aiming to minimize total operational costs. To identify promising trips, we propose a tailored labeling algorithm with a novel dominance rule, along with a time window shift algorithm to determine the best schedule. To yield high-quality solutions in a short computation time, a tailored column-generation matheuristic algorithm is introduced. A branch-and-price exact algorithm and an adaptive large neighborhood search algorithm are developed to assess the matheuristic algorithm. Numerical experiments are conducted to demonstrate the effectiveness and applicability of the proposed models and algorithms. Experiments also show that this kind of ridesharing service can provide low-cost and high-quality services for the first-mile problem. • The first-mile ridesharing problem with public and private autonomous vehicles is introduced. • Passengers' requirements for service quality, such as the latest arrival time and the maximum ride time, are considered. • An arc-based mixed-integer programming model and a trip-based set-partitioning model are proposed. • A branch-and-price algorithm and a tailored column-generation matheuristic algorithm are designed. • A tailored labeling algorithm with a novel dominance rule and a time schedule algorithm for routes are proposed. [ABSTRACT FROM AUTHOR]

• Ice thickness, ice conditions, and ice class directly affect shipping costs. • Ice conditions are softening, decreasing the expected NSR fee level. • Ships of some ice-classes can sail within the NSR independently. • Ships without ice protection are unprofitable for shipping in the NSR. • The NSR icebreaker escort fee may be several times lower than the SCR toll. Even though the Arctic ice is melting and Polar routes are being extensively studied, the amount of cargo shipped through the Northern Sea Route (NSR) remains low. Employing current data on the NSR operation provided by China Ocean Shipping Company, this paper investigates annual profitability of the NSR-SCR combined use, when a vessel sails through the NSR in summer and the SCR in winter. A Handysize general cargo ship is used as a model vessel since it allows reaching nearly maximum load rate and can sail through shallow areas of the NSR. This article proposes including ice thickness and ice conditions in the traditional cost comparison. The first parameter directly affects sailing speed, and hence other cost variables, while the second one is included in the official NSR navigation rules and connected with icebreaker escort fee. This paper also contributes to the existing literature by considering total shipping cost as a function of a vessel's ice class with corresponding building premiums and fuel consumption increments. The proposed approach is easily replicable, so shipping companies might use it to rethink their positions on shipping through the NSR. The overall conservative comparison reveals that the NSR-SCR combined shipping scheme can only be more competitive than sailing through the SCR if a shipping company provides a sufficient load rate on the NSR leg and uses a ship of a reliable ice class to navigate throughout the summer navigation. The NSR-SCR combined shipping scheme becomes attractive if the NSR leg is short, and the crude oil price is high. [ABSTRACT FROM AUTHOR]

The scheduling of barge transportation in narrow channels is critical to transportation efficiency, especially when the tide exists and affects the channel navigation of barges. Barge movements in channels are frequently correlated with other activities (e.g., berth allocation) and must thus be considered concurrently. To enhance transportation efficiency, effective scheduling algorithms are required. In this study, we examine the barge scheduling problem arising from a river-to-sea channel with tidal restrictions in the real world, where barges' loading and unloading operations are placed at both ends of the navigation channel and must be planned and coordinated with their channel movements. To reduce the overall completion time for all barges, we develop a mixed integer linear programming model with several valid inequalities. To satisfy the second-level computational efficiency requirement, a straightforward yet efficient variable neighborhood search algorithm is proposed. Real-world case studies based on a barge transportation project in Boffa, Guinea are conducted. It is demonstrated that the proposed approach is applicable for efficient decision-making and effective in assisting barges to avoid missing feasible tide windows by slightly altering the first-come-first-serve schedule of the barges. With the proposed approach, the size and speed of the barge fleet can be optimized, and the tidal influence is also evaluated quantitatively. • Introduce a new real barge scheduling problem in a river-to-sea channel with tidal impacts. • Developing a mixed integer linear programming model with valid inequalities. • Verify the performance of the proposed algorithm via computational experiments. • It assists barge operators in determining fleet size and speed, and evaluate tidal impact. [ABSTRACT FROM AUTHOR]

With the trend towards mega-vessels and shipping alliance, the importance of transshipment activities keeps increasing. In transshipment yards, a “yard template” is often used to stack containers in dedicated areas (sub-blocks) pre-reserved for their own destination vessels. At short-term planning level, the yard template is given, but the containers going to a specific vessel still have high flexibility to be allocated among many pre-reserved sub-blocks. The amount of containers allocated to each sub-block, i.e. “container allocation”, not only affects the traffic congestion, but more importantly determines the number of yard cranes (YCs) required in each block. The limited YCs have to switch blocks to fit the needs of container allocation in different periods, i.e. “YC deployment”. This study integrated these two closely related problems and formulated a MIP model. Since the model has a nice block-diagonal structure, column generation under Dentzig Wolfe decomposition was proposed to get lower bounds. A novel branch-and-price (B&P) method was proposed to find near-optimal solutions. To reduce the searching tree size, our B&P method branched on YC paths during the planning horizon, instead of branching on decision variables directly. Numerical experiments under both small and large scale problems showed that our B&P method could efficiently solve the integrated planning problem. The results also showed that YC movements could be reduced effectively without sacrificing operational efficiency or using more yard cranes. [ABSTRACT FROM AUTHOR]

Travel to intercity transportation hubs, such as railway stations and airports, can be the most troublesome and inefficient part of the entire air/railway travel journey, as travelers often carry large luggage and have stringent arrival time requirements. Taking public transportation, such as metro and bus services, is inconvenient to carry luggage and less reliable in arrival time while taking taxi services could be less economical. As a result, providing reliable and convenient yet economical on-demand first-mile services for travelers to intercity transportation hubs is essential. This paper proposes a ridesharing approach for the first-mile transport system for travelers heading towards the intercity transportation hub and develops a mixed-integer linear programming (MILP) model with the objective of minimizing the total operating costs for ridesharing service operators. The MILP model considers (1) large luggage that may occupy seats when the car trunk is not large enough to place them; (2) passengers' requirements on arrival time and ride time; and (3) travel time uncertainty ensuring that riders' arrival time and ride time can be satisfied. A tailored adaptive large neighborhood search algorithm with an acceleration strategy is developed for obtaining robust near-optimal solutions within a reasonable time. To assess the solution quality, the MILP model is reformulated as a set-partitioning model, and the column generation algorithm is leveraged to determine a tight lower bound; a greedy algorithm is introduced to obtain an upper bound. Computational experiments on Shanghai South Railway Station demonstrate that ridesharing is an effective strategy for reducing overall travel costs while meeting the first-mile travel demand. In addition, it is essential to consider luggage and travel time uncertainty for determining ridesharing schemes. • The first-mile ridesharing problem to intercity transportation hubs is introduced. • Ride time, arrival time, large luggage, and travel time uncertainty are considered. • Two mathematical models, arc-based and route-based, are proposed. • A robust adaptive large neighborhood search algorithm is designed. [ABSTRACT FROM AUTHOR]

In this paper, we study a barge and tugboat joint scheduling problem arising from a river–sea intermodal transport system, where non-self-propelled barges travel with the help of tugboats between an inland river bulk port and sea transshipment platforms to transport cargo. Due to limited channels and the sequential scheduling among ships, transit inefficiency and resource under-utilization often occur, thus it is essential to design barge timetables and tugboat paths. This paper jointly schedules barges and tugboats in a river–sea transshipment port with a tidally influenced channel, and focuses on barge round trips with multiple legs. We construct a state-space–time network from the perspective of tugboats, in which the state implies whether a barge is served by a tugboat. The problem is formulated as an integer programming model based on the network to minimize time taken for bulk cargo transshipment. A tailored variable neighborhood search algorithm is designed and its efficiency, effectiveness, and stability are demonstrated by numerical experiments, which are based on real data from the Boffa port. It is demonstrated that, by joint scheduling barges and tugboats, transfer efficiency can be significantly improved under limited tugboat resources and shorter tidal windows. The results also provide managerial insights on schedule and valuable conclusions about the resource allocation in the port. • Introduce an integrated barge-tugboat scheduling problem for river–sea transport. • Develop a state-space-time network and an integer programming model. • Design a variable neighborhood search algorithm. • Verify the performance of the proposed algorithm via computational experiments. [ABSTRACT FROM AUTHOR]

This paper advances the field of network interdiction analysis by introducing an application to the urban rail transit network, deploying protective resources against intentional attacks. The resource allocation problem for urban rail transit systems is considered as a game between two players, the attacker interdicting certain rail stations to generate greatest disruption impact and the system defender fortifying the network to maximize the system’s robustness to external interdictions. This paper introduces a game-theoretic approach for enhancing urban transit networks’ robustness to intentional disruptions via optimally allocating protection resources. A tri-level defender–attacker–user game-theoretic model is developed to allocate protective resources among rail stations in the rail transit network. This paper is distinguished with previous studies in that more sophisticated interdiction behaviors by the attacker, such as coordinated attack on multiple locations and various attacking intensities, are specifically considered. Besides, a more complex multi-commodity network flow model is employed to model the commuter travel pattern in the degraded rail network after interdiction. An effective nested variable neighborhood search method is devised to obtain the solution to the game in an efficient manner. A case study based on the Singapore rail transit system and actual travel demand data is finally carried out to assess the protective resources’ effectiveness against intentional attacks. [ABSTRACT FROM AUTHOR]

Quay-side berthing congestion is an emerging challenging issue that arises in busy container transshipment terminals and calls for effective management of terminal operations. This paper tackles the berthing congestion problem by introducing a proactive management strategy from the terminal’s perspective that adjusts the calling schedule of feeder vessels in such a way that the quay-side workload distribution in the temporal dimension can be balanced. Such a schedule template design problem is considered simultaneously with another two tactical level decision problems, berth template design (i.e., determining preferred berthing positions for vessels) and yard template design (i.e., allocating storage yard space to transshipment flows). This highly integrated problem is formulated as a set covering model. Heuristic methods based on column generation are developed to obtain near-optimal solutions in an efficient way. Computational experiments on real-world sized test instances demonstrate the efficiency and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Unplanned disruptions to urban rail systems can result in widespread confusion and severe delays, and running bus bridging service is a common solution. Currently, transit operators usually adopt experience-based response strategy in such a way that spare buses from bus terminals and parking lots are used to run bus bridging services. Such a response approach can be effective when there are spare buses available at nearby terminals/parking lots. However, in case spare buses are not available or enough, it could be beneficial to dispatch running buses from nearby operating bus lines to ensure efficient response while incurring minimal inconvenience to on-board passengers. While this approach could reduce metro users' delays, it causes extra delays and dissatisfaction to affected bus riders. In this study, we investigate the optimal bus dispatching problem for responding to unplanned disruptions of urban rail transit system with two bus fleet sources: (1) spare buses at terminals and parking lots, and (2) running buses from nearby operating bus lines. To obtain the optimal dispatching scheme, we develop an integer programming model, which achieves a balance between the delays of metro users and that of bus riders. Considering of the high uncertainty of bus headways which could affect the performance of the above deterministic model, we further develop a robust optimization model to obtain more reliable bus dispatching schemes. Case studies based on the data sets in Shanghai are conducted. The results demonstrate that our approach can be carried out in real-time, and employing running buses from operating bus lines shows great potential in reducing affected users' delays. In addition, the robust model offers a more reliable and competitive solution when the headway varies widely. • A bus dispatching problem aiming at unplanned disruptions of the metro is introduced. • Two bus fleet sources including spare buses at terminals and operating buses are considered. • An integer programming model minimizing the delays of affected users is proposed. • A robust optimization model considering the uncertainty of headway is developed. • The proposed method efficiently solves the real problem in a short time. [ABSTRACT FROM AUTHOR]

Timetable design is crucial to the metro service reliability. A straightforward and commonly adopted strategy in daily operation is a peak/off-peak-based schedule. However, such a strategy may fail to meet dynamic temporal passenger demand, resulting in long passenger waiting time at platforms and over-crowding in trains. Thanks to the emergence of smart card-based automated fare collection systems, we can now better quantify spatial–temporal demand on a microscopic level. In this paper, we formulate three optimization models to design demand-sensitive timetables by demonstrating train operation using equivalent time (interval). The first model aims at making the timetable more dynamic; the second model is an extension allowing for capacity constraints. The third model aims at designing a capacitated demand-sensitive peak/off-peak timetable. We assessed the performance of these three models and conducted sensitivity analyzes on different parameters on a metro line in Singapore, finding that dynamical timetable built with capacity constraints is most advantageous. Finally, we conclude our study and discuss the implications of the three models: the capacitated model provides a timetable which shows best performance under fixed capacity constraints, while the uncapacitated model may offer optimal temporal train configuration. Although we imposed capacity constraints when designing the optimal peak/off-peak timetable, its performance is not as good as models with dynamical headways. However, it shows advantages such as being easier to operate and more understandable to the passengers. [ABSTRACT FROM AUTHOR]

Highlights: [•] Introduce localized integration between public bus services with metro system. [•] Develop a two-stage stochastic program for enhancing the resilience of metro networks by leveraging on public bus services. [•] Conduct a case study based on the Singapore metro system. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the Train Network Design Optimization problem arising from railroad industry which involves the integration of three inter-related decision sub-problems: train routing which is to identify origin, destination and itinerary for individual trains; block-to-train assignment detailing the block movements with trains and swaps between trains; and crew-to-train assignment specifying the crew services for train routes. A column generation based hierarchical approach with two stages is designed: the first stage generates a pool of promising train routes iteratively based on the crew segments by the column generation technique; and the second stage develops an integer linear programming model for the subsequent decisions including train route selection and block-to-train assignment. Numerical experiments with realistic test instances are conducted and the outcome demonstrates the capability of the proposed approach in solving the Train Network Design Optimization problem competently. [Copyright &y& Elsevier]

Abstract: The feeder vessel management problem consists of designing preferred berthing positions and service time for cyclically visiting feeders, and allocating storage yard space to the transshipment flows between mother vessels and feeders. We consider the above three tactical decision problems simultaneously for a container transshipment terminal with an eye toward the quayside congestion and the housekeeping cost of container movements. The integrated problem is formulated as a mixed integer programming model and solved by a memetic heuristic approach. A comprehensive computational experiment is conducted to show the effectiveness of the heuristic and the improvement upon real-world terminal operations. [Copyright &y& Elsevier]

Abstract: This paper presents an integer programming model for the terminal and yard allocation problem in a large container transshipment hub with multiple terminals. The model integrates two decisions: terminal allocation for vessels and yard allocation for transshipment container movements within a terminal as well as between terminals. The objective function aims to minimize the total inter-terminal and intra-terminal handling costs generated by transshipment flows. To solve the problem, we develop a 2-level heuristic algorithm to obtain high quality solutions in an efficient way. Computational experiments show the effectiveness of the proposed approach. [Copyright &y& Elsevier]

• The train schedule template and transshipment plan of intermodal containers are jointly optimized. • Various time window constraints related to the transshipment operation are considered. • The model is enhanced by valid inequalities and relaxations. • A critical value of handling capacity could be effectively identified. • Increasing the unit storage cost is in favor of a better transfer plan. • The overestimate of the indirect-transshipment penalty should be avoided. This paper investigates the transshipment operations between vessels and trains in seaport rail terminals. In order to get a more effective transshipment plan, the schedule plan of trains and the transshipment plan of containers among vessels, yards, and trains are determined simultaneously. To this end, a mixed integer programming (MIP) model is proposed to integrate the two sub-problems with the objective of minimizing the total operation cost, in which the service time window and unloading time requirement of trains are considered. It is demonstrated that some variables in the model can be relaxed without influencing the optimal result. Moreover, a set of valid inequalities are introduced to tighten the constraints. The result experiments show that the enhanced model can be solved more effectively. Then, extensive experiments have been conducted to analyze the influence of several factors, including the handling capacity, the yard capacity, and the value of unit cost. The results report that the handling capacity shows a significant effect on the performance of the transfer plan, and increasing the storage cost of import containers leads to a more effective transshipment plan. [ABSTRACT FROM AUTHOR]

This paper introduces a vessel voyage schedule planning problem for maritime ore transportation arising from a real-world project in Boffa, Guinea. In this problem, delay of vessel arrivals at the loading port often occurs which interrupts the ore loading operation and further impacts the yearly throughput of ore transportation, which dues to variable weather conditions at sea and changing loading/discharging time at ports. Thus, factors regarding the uncertainty of vessel voyages must be considered in order to ensure continuous vessel arrivals and uninterrupted loading operations at the original port, leading to an increase in the overall productivity of the project. And at the same time, since every voyage entails an increase in costs, cost control is necessary to be accounted for, so there is also a need to consider minimizing operating costs. In this study, to address the uncertainty in voyage duration, a chance-constrained model is introduced to obtain sailing schedules as well as the associated costs within a given confidence level. And then, for the multi-objective requirements, we develop a multi-objective planning model and the actual system operating capacity limitation constraints are considered. After that, considering that the multi-objective model cannot be solved by the solver directly, the weighting method is introduced. Afterwards, since the direct solving of the model is rather time-consuming, valid cuts are designed to accelerate it. Finally, considering that the Pareto solutions of multi-objective problems are numerous, a nondominated sorting genetic algorithm II (NSGA-II) is launched with the aim of providing more solutions to the manager in a shorter time. In terms of experiments, each of the aforementioned issues and proposed remedies are verified. The results confirm the effectiveness of the valid cuts and NSGA-II, and demonstrate that the obtained vessel voyage schedule is more robust and outperforms manual decisions. • Introduce a real-world vessel voyage planning problem with uncertain conditions. • Establish a bi-objective mixed integer linear programming model and a chance-constrained sub-model. • Develop a series of valid cuts and a nondominated sorting genetic algorithm II. • Verify the performance of the proposed model and algorithm via computational experiments. • Assist managers in making reliable voyage plans. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated Bayesian statistical inference framework to characterize passenger flow assignment model in a complex metro network. In doing so, we combine network cost attribute estimation and passenger route choice modeling using Bayesian inference. We build the posterior density by taking the likelihood of observing passenger travel times provided by smart card data and our prior knowledge about the studied metro network. Given the high-dimensional nature of parameters in this framework, we apply the variable-at-a-time Metropolis sampling algorithm to estimate the mean and Bayesian confidence interval for each parameter in turn. As a numerical example, this integrated approach is applied on the metro network in Singapore. Our result shows that link travel time exhibits a considerable coefficient of variation about 0.17, suggesting that travel time reliability is of high importance to metro operation. The estimation of route choice parameters conforms with previous survey-based studies, showing that the disutility of transfer time is about twice of that of in-vehicle travel time in Singapore metro system. [ABSTRACT FROM AUTHOR]

The scheduling synchronization problem in this paper is to obtain an optimal schedule by optimizing running time, departure time, dwell time and arrival time of the last train in urban rail transit networks. Operators are often faced with multiple conflicting requirements simultaneously such as high passenger's service quality and less cost, smooth transfer events and less passenger's travel time, and high accessibility and less operation time. Thus, researchers in this industry concentrate on utilizing operations and management decisions to solve the scheduling problem while balancing the service trade-offs. In this paper, we formulate a mixed integer programming approach for the last train schedule planning, and passengers benefit from smoother transfer in the form of maximizing the transfer synchronization events, while operators simultaneously can benefit with lower operation costs by minimizing the worst big difference between last trains. We propose an improved non-dominated sorting approach embedded in a genetic algorithm to obtain close-to-optimal solutions in a much shorter time for a sophisticated, real-world and large-scale Beijing subway network. Results demonstrate that significant service performance gains (76.33% for Just-missed, 32.01% for successful transfer, 15.39% for non-equity for last trains and 45.25% for variance indicators, etc.), which indicate the effectiveness of the proposed modeling framework and solution algorithm. The operator formulates an efficient schedule for the actual operation of the urban rail transit network by the proposed application method, and it also would be applicable to solving schedule problems among a large-scale network in other industries. [ABSTRACT FROM AUTHOR]

• A new concept to understand the mobility and location features of subway stations. • Transplant Language Processing to understand subway stations as compound words. • Obtain mobility and location semantics from neural net and affinity propagation. • Interpretsemantic results in an urban planning aspect. Rapid urbanization and modern civilization require sound integration with public transportation systems. In the same time, the volume and complexity of public transportation network are increasing, making it harder to understand the public transportation dynamics. As a first step, understanding the similarity among subway stations is imperative. In this paper, we proposed a semantic framework inspired from natural language processing (NLP) to interpret subway stations as compound words. Specifically, we transplanted context and literal meaning of compound words into mobility and location attributes of stations. Using smart card data, we trained stacked autoencoders (SAE) with designed flow matrices as an embedding method to learn the mobility attributes. Subsequently, to discover the location attributes, we have applied affinity propagation clustering to classify 9 point of interest (POI) categories. Combined with urban planning knowledge, we manage to comprehend the land use meanings of 9 POI clusters. The location semantics is chosen from those categories reflecting its urban land use pattern. By choose meaningful combination of mobility and location semantics for stations' similarity case studies, we summarized potential applications of this semantic framework. [ABSTRACT FROM AUTHOR]

With the growing urban population and its rapid growth of mobility needs, metro systems often suffer from congestion in peak hours in many mega-cities over the world. This incurs severe travel delays for commuters and safety risks for metro operators. Hence, passenger flow management and control becomes an essential way to reduce station congestion during high-peak hours. This paper investigates the passenger flow control problem with the objective of increasing the number of boarding passengers. Considering the scenario that the destination of each passenger entering the station is unknown, a flow control problem with dynamic and station-based constraints is proposed to dynamically determine the number of passengers boarding each train at each station. Compared with existing flow control strategies, this model can improve the equity for boarding passengers of different OD pairs. The station-based flow control problem is formulated as a complicated nonlinear nonconvex quadratic programming model. To solve the intractable nonlinear programming model, we reformulate it into the dynamic programming formation and develop two efficient heuristic algorithms to solve it. We carry out two sets of numerical experiments, including the small-scale case with synthetic data and the real-world case with the operation data of Beijing metro system, to evaluate the performance of our model and algorithms. Several performance indicators, e.g. average waiting time and Gini coefficient, are presented to verify the efficiency and fairness of proposed model. The numerical results applied to Beijing urban subway network indicate that our approach can reduce the passengers' waiting time and the line-level Gini coefficient by 5.21% and 23.52% compared with the benchmark flow control strategy with maximum loading and station-based constraints. • A nonconvex model is developed to describe the station-based flow control problem. • The connection between the station-based and OD-based problems is established. • Two algorithms based on dynamic programming are designed to solve the model. • The upper bound and benchmark are analyzed to evaluate our algorithms. [ABSTRACT FROM AUTHOR]