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The COVID-19 pandemic had a significant impact on container transportation. Accurate forecasting of container throughput is critical for policymakers and port authorities, especially in the context of the anomalous events of the COVID-19 pandemic. In this paper, we firstly proposed hybrid models for univariate time series forecasting to enhance prediction accuracy while eliminating the nonlinearity and multivariate limitations. Next, we compared the forecasting accuracy of different models with various training dataset extensions and forecasting horizons. Finally, we analysed the impact of the COVID-19 pandemic on container throughput forecasting and container transportation. An empirical analysis of container throughputs in the Yangtze River Delta region was performed for illustration and verification purposes. Error metrics analysis suggests that SARIMA-LSTM2 and SARIMA-SVR2 (configuration 2) have the best performance compared to other models and they can better predict the container traffic in the context of anomalous events such as the COVID-19 pandemic. The results also reveal that, with an increase in the training dataset extensions, the accuracy of the models is improved, particularly in comparison with standard statistical models (i.e. SARIMA model). An accurate prediction can help strategic management and policymakers to better respond to the negative impact of the COVID-19 pandemic.

The ship's routing was adopted to organise marine traffic flow and reduce the risk of collision between ships in crowded waters. With the expansion of the world's fleet, ship traffic in shipping bottleneck and chokepoint areas became more and more busy and complex creating serious challenges for navigational safety. Therefore, quantitative collision risk assessment is significantly important for the ships' routeing waters. In this paper, the information entropy method which integrates the K-means clustering based on Automatic Identification System (AIS) data is introduced to quantitatively evaluate the collision risks in the ships' routeing waters. As a case study, the information entropy of Courses Over Ground (COG) for Ningbo-Zhoushan Port (the largest port in the world since 2009) is calculated by using historical AIS data. Then the K-means clustering is used to group the bytes of information entropy of the different legs in the shipping route. We find that in Ningbo-Zhoushan port Precautionary Area (PA) 2, 4 and 7 are the highest risk legs; PA 1, 5 and 6, Traffic Separation Scheme (TSS) 16, and 17 are medium-high risk areas. Therefore, ship collision risk prevention measures should be prioritised in those legs. Our contributions provide a novel approach to quantitatively assess ship collision risks in busy waters.

Capacity drop at congested freeway bottlenecks is well known with a lot of field observations. This paper studies coordinated ramp metering (RM) and mainstream traffic flow control (MTFC) as well as their integration (RM+MTFC) for freeway traffic, with particular attention to effects of capacity drop on the performance of traffic control measures. Via mathematical analysis and comprehensive simulation studies under an optimal control framework, the work has revealed a capacity-drop-related mechanism that MTFC and ramp metering are based on to take effects, and obtained a number of important conclusions: (1) applications of any control measure (RM, MTFC, or RM+MTFC) in freeways are justified by the existence of capacity drop in field; (2) an appropriate usage of the control measures can effectively prevent the activation of potential bottlenecks on freeways and hence avoid capacity drop; (3) any control measure is beneficial for a large majority of the driver population in a freeway network if it can manage to increase the accumulated total network exit flow; (4) it is a common misconception that ramp metering would simply transfer traffic loads from the freeway mainstream to on-ramps. The work has also highlighted the strengths, weaknesses, and applicability of ramp metering and MTFC.

This study proposes an optimal control method for connected autonomous vehicles (CAVs) through signalized intersections to reduce the energy consumption of mixed human-driven vehicles (HDVs) and CAV traffic. A real-time optimal control model was developed to optimize the trajectory of each CAV by minimizing energy consumption during the control period while ensuring traffic efficiency and safety. The control conditions of the CAVs were analyzed under different driving scenarios considering the impact of signal phase timing and preceding vehicles. Additionally, a method is proposed for CAVs to guide other vehicles directly and reduce the energy consumption of the entire signalized intersection. Simulation experiments using MATLAB and SUMO were conducted to evaluate the performance of the proposed method under various traffic conditions, such as different levels of saturation, market penetration rates (MPRs), and the green ratio. The performance was measured using average energy consumption and an average time delay. The results show that the proposed method can effectively reduce vehicle energy consumption without compromising traffic efficiency under various conditions. Moreover, under traffic saturation, the proposed method performs better at a high MPR and green ratio, especially at 40–60% MPR.

Ship-related marine oil spills pose a significant threat to the environment, and while it may not be possible to prevent such incidents entirely, effective clean-up efforts can minimize their impact on the environment. The success of these clean-up efforts is influenced by various factors, including accident-related factors such as the type of accident, location, and environmental weather conditions, as well as emergency response-related factors such as available resources and response actions. To improve targeted and effective responses to oil spills resulting from ship accidents and enhance oil spill emergency response methods, it is essential to understand the factors that affect their effectiveness. In this study, a data-driven Bayesian network (TAN) analysis approach was used with data from the U.S. Coast Guard (USCG) to identify the key accident-related factors that impact oil spill clean-up performance. The analysis found that the amount of discharge, severity, and the location of the accident are the most critical factors affecting the clean-up ratio. These findings are significant for emergency management and planning oil spill clean-up efforts.

This paper explores the factors influencing taxi travel in the context of COVID-19 from both demand and supply sides and provides a quantitative comparison of taxi travel characteristics and taxi industry operations before and after the epidemic. A model was established using system dynamics to simulate a taxi travel system, which was used to analyze the changes in demand and supply of taxi travel under scenarios such as closedowns, travel restrictions, etc. The analysis is based on a typical middle-sized city in China, Ningbo in Zhejiang Province, revealing factors leading to the significant drop in the amount of taxi travel due to the epidemic. The study can provide insights into impacts of public (or similar anomalous or catastrophic) events on taxi travel systems and could be useful for urban transport planning and management.

A sudden major public health event is likely to have a negative impact on public transport travel for residents, with public travel modes such as the metro and conventional buses experiencing varying degrees of decline in patronage. As a complement to public transport, taxi travel will suffer the same impact. Land use and population density among various functional blocks in a city are different, and therefore their changing rates in taxi travel demand are varied. This paper reveals the taxi travel demand correlations between urban blocks and then constructs a taxi travel demand decay model based on the Dynamic Input-Output Inoperability Model (DIIM) to simulate the decay degree of taxi travel demand in each block. When a major public health event occurs, the residential panic levels in different functional blocks may vary. It results in variable changing speeds of residential travel demand in each block. Based on this assumption, we use the intensity of travel demand as a correlation strength factor between blocks, and equate it with the technical coefficient in the DIIM model. We also define other variables to serve in model construction. These variables include the decay degree of travel demand intensity, residential travel willingness, coefficient of travel demand decay, derivative coefficient of travel demand interdependency, and demand perturbation coefficient. Lastly, we select a central area of Ningbo as the study area, and use taxi travel data in Ningbo during the COVID-19 pandemic of 2020 as input, simulate taxi travel demand dynamics, and analyze the accuracy and sensitivity of the model parameters. The relative errors between the five types of blocks and the actual decay of travel demand intensity are 8.3%, 3.8%, 8.7%, 5.5%, and 5.3%, respectively, which can basically match the actual situation, proving the validity of the model. The results of the study reveal the pattern of taxi travel demand decay among various blocks after major public health events. It provides methodological reference for decision makers to understand the development trend of multi-block taxi travel demand, so as to help form effective emergency plans for different blocks.

Customer demand is dynamic and changeable; thus, optimality of the enterprise’s initial location cannot be guaranteed throughout the planning period in order to minimize site selection cost and maximize service reliability in the whole operation cycle. The enterprise planning period is divided into different stages, and a static location model is established at the fixed stage. In addition, a multi-stage dynamic location model is established by introducing the transfer cost between adjacent stages. To reduce the difficulty of solving the dynamic location model, first, we determined the optimal site selection and allocation strategy for each stage. Second, we designed a novel method that transforms the multi-stage dynamic location problem into the shortest path problem in graph theory. Finally, the Dijkstra algorithm was used to find the optimal dynamic location sequence so that its cumulative cost was the lowest in the whole planning period. Through a case study in China, we compare the costs of static and dynamic locations and the location cost under different objectives. The results show that this dynamic location generates more income (as it reduces cost) in comparison to the previous static location, and different location objectives have a substantial influence on location results. At the same time, the findings indicate that exploring the problem of enterprise location from a dynamic perspective could help reduce the operating cost and resources from a sustainable development perspective.

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Maritime Policy & Management on Published online: 12 Sep 2021, available online at: http://www.tandfonline.com/10.1080/03088839.2021.1972175. The Yangtze River Delta multi-port system (YMPS) receives increasing attention due to its relevance in world trade and excellent competitiveness in the container traffic market. To insight into the development pattern of YMPS, this paper proposes a method that combines the Hierarchical Clustering with compositional data (CoDa) exploratory tools (i.e., biplot and dendrogram) to explore the temporal and spatial evolution of the YMPS from 1992 to 2019. CoDa describes parts of some whole (i.e., frequency and percentage), conveying relative information in the ratios between its components. Container traffic share in a multi-port region is typical CoDa. Traditional statistical approaches to CoDa could lead to spurious correlations and erroneous conclusions. However, using suitable CoDa techniques, such as the centered log-ratio (clr) transformation, can effectively avoid these misinterpretations. The novel method can simultaneously find the temporal and spatial characteristics. The findings indicate that the development of the YMPS has gone through four stages and the evolution of the YMPS is characterized by a tendency towards a ‘multi-core development’ and faces a differentiated pattern of ‘peripheral port challenges’. The analysis further improves the port system’s evolutionary model and explains the underlying reason for the YMPS development. CoDa techniques also provide a new perspective for the temporal and spatial evolution of the transport discipline. This work was supported by the Ministerio de Ciencia, Innovación y Universidades under project Methods for Compositional analysis of Data (CODAMET) [grant number RTI2018-095518-B-C22, 2019-2021] and 111 Project [grant number D21013]

The investment environment in port infrastructure appeared fluctuating in recent years, which threw more uncertainties and challenges in port Public-Private Partnership (PPP). Among ports in PPP, Dandong Port is the first case for port bankruptcy in China. In this contribution, the Interpretative Structural Model (ISM) is employed to investigate why the bankruptcy of Dandong Port happened and analyze the causes from different perspectives. The ISM-based model interprets the interrelationships among those causes and their positions in the structural hierarchy from high- to low-level. Results reveal that: (i) these factors ( i.e. the economy slowing down in the hinterland, ambitious port plan of local government and unprofessional top management) were the most direct factors to Dandong Port’s bankruptcy; (ii) these factors ( i.e. the economy slowing down in the hinterland, ambitious port plan of local government and unprofessional top management) were the most fundamental causes to this failure; (iii) these factors ( i.e. the ambitious port plan of local government, inappropriate strategic positioning, continuous expansion in port investment and unprofessional top management) were the most core causes for this collapse. Findings provide lessons to be learned from the bankruptcy for port PPP and offer managerial insight into port management policy.

The highway on-ramp merging area is one of the major sections that form traffic bottlenecks. In a connected vehicle environment, V2V and V2I technologies enable real-time exchange of information, including position, speed, and acceleration. To improve the efficiency of vehicle merging at the on-ramp, this study proposes a cooperative merging control strategy for network-connected autonomous vehicles. First, the central controller designs the merging sequence and safety space for vehicles passing through the confluence point. Then, a trajectory optimization model was constructed based on vehicle longitudinal dynamics, and the PMP algorithm was used to determine the optimal control input. Finally, all vehicles follow the optimal trajectory so that the ramp vehicles merge smoothly into the mainline. Simulations verify that the proposed algorithm performs better than FIFO, with 13.2% energy savings, 41.4% increase in average speed, and 50.4% reduction in travel time over the uncontrolled merging scenario. The method is further applied to different traffic flow conditions and the results show that it can significantly improve traffic safety and mobility, while effectively reducing vehicle energy consumption. However, the traffic operation improvement is not satisfactory under low traffic demand.

Non-coordinated first-train timetables can result in unfavorable train connections among different subway lines at transfer stations and generate rather long waiting time for transfer passengers. This paper aims at optimizing the first train originating times of different transit lines at the network scale. The cost function of transfer waiting time is formulated to evaluate the perceived transfer quality with the consideration of passengers' psychological feelings. The first-train timetable coordination model is then developed by minimizing the total waiting cost of passengers transferring between two first trains of different lines. The genetic algorithm is applied to solve the model. Finally, a case study from part of the Beijing subway network is conducted to verify the method. The results show that the total waiting cost of first-train transfer passengers is reduced by 49.67% with the application of our proposed model. Meanwhile, the number of super-long waiting is significantly reduced.

Determining the required probe vehicle size for real-time travel time estimation is an important issue in probe-data-based applications, such as traffic monitoring on a roadway network. This paper provides an innovative approach for the determination of proper sample sizes with the aid of a simulation model. The model, which was built for El Camino Real, a major arterial in the California San Francisco Bay Area, helps to provide the necessary detailed data to verify the proposed methodology. Our proposed approach is based on the findings that the distribution of travel time estimate error can be constructed with the generalized Pareto distribution function. Considering that travel times vary within a signal cycle and among different movements (through, left-turning and right-turning) at intersections, our study uses data in 15-s intervals to examine the travel time variations associated with different intersection movements. Our findings show that when the penetration rate of probe vehicles is 40%, the probability that the relative error will be less than 0.10 within the time period is 90%, whereas a penetration rate greater than 60% is needed to attain a 0.95 confidence level at the same error rate. For left-turning vehicles, a 10% penetration of probe vehicles is sufficient for estimating their travel times with relative errors less than 0.10 at a 90% confidence level, whereas through vehicles and right-turning vehicles require substantially higher penetration rates of approximately 50% and 30%, respectively, to attain the same confidence level.

With the goal of improving the real-time automatic generation of water routes and obtaining the optimal water routes, the study is based on automatic identification system (AIS) big data and electronic chart display and information system (ECDIS) water route automatic generation. The parallel AIS data processing technology of the distributed storage system is selected to realize the AIS big data processing that is automatically generated by the water route, the geodetic coordinates of the AIS information after the big data processing are transformed to Mercator projection, and the obtained Mercator coordinates are converted to the latitude and longitude of the sampling point to obtain the final sampling point. The Douglas-Peucker algorithm is selected to compress the sampling points and select the turning points from the compressed sampling points according to the fixed threshold set. The connection of the obtained turning points is the automatically generated initial water route. The ECDIS electronic chart is used to detect the generated route, and the final feasible route is the generated final water route. The experimental results show that the automatically generated water route mileage of this method is shorter, and the automatic generation time is within 300 ms, which has higher water route generation efficiency.

In order to improve the availability of taxi GPS big data, we restore the chosen route for the sparse taxi GPS trajectory in this work. A trajectory restoration method based on Bayesian network is proposed. Compared with the traditional research solely based on time-spatial variables, this method additionally considers the characteristics of empty/heavy taxi status, weather conditions, drivers, vehicle running and other factors to carry out route restoration. A field case of grid network in Ningbo is taken to verify the applicability of the method, using the taxi GPS trajectory data from Ningbo Taxi Information Management Platform. The case results show that the accuracy of Bayesian network method based on multiple factors reaches 91.4%. Its performance is superior to the Multivariate logistic regression model. In addition, the proposed method is especially suitable for scenarios with a high missing rate of track data, such as a scene with timespan of about 5 min between neighbour trajectories.

This paper analyses the spatio-temporal evolution of Ningbo-Zhoushan port growing from a feeder port to a hub port finding the historical pathways followed by its expanding in terms of container throughput capacity and total traffic. The dynamic mechanism of evolution is the results of economic globalization, containerization and its natural endowments in channel and terminal depths. Analysis of the traffic evolution and its underlying dynamics suggest 3 periods in the development processes of container transport in Ningbo-Zhoushan: (1) adoption period (1986-2000) in which the main dynamics is the impact of the Chinese ‘Open Door policy’; (2) acceleration period (2001-2008) in which the dynamics is related of the mainland China’s entry into the WTO; (3) peak growth period (2009-now) in which the dynamics is impacted by the anti-crisis strategy against the financial and economic crisis in 2008. We analyse the perspectives of Ningbo-Zhoushan port. ARIMA model is employed to forecast the container traffic in the coming future; about after 2026, the throughput in Ningbo-Zhoushan port would reach about 49 million TEU which would be approximately equal to that of Shanghai port. The resultant port development would exemplify a model of spatial distribution such as a multi-layered gateway hub. In the respect of growth potential, Ningbo-Zhoushan port possesses excellent coastline resource suitable for deep-water berthing, bonded port policy and free trade zone policy. Geographical position, service level, hinterland economic level and government will support its perspectives.

The Yangtze River Delta (YRD) is the most developed area in China attracting global attention. Noting that the concentration ratio (CR(n)), Herfindahl-Hirschman Index (HHI) (also including Gini coefficient and Aitchison distance) and shift-share analysis (SSA) are only allowed to describe the concentration, inequality and competition of a multi-port system respectively. A novel ternary diagram method is proposed to include these five methods together and provide a comprehensive and visual tool to conveniently investigate the concentration gradient, inequality and competition of the YRD multi-port system simultaneously. The ternary diagram also permit to substitute individually for the CR(n), HHI (also including Gini coefficient and Aitchison distance) and SSA as a tool to investigate the concentration, inequality and competition of a multi-port system. This method provides a new perspective for better interpretation of the spatial and temporal evolution of shipping and multi-port systems.

Scheduling the wheel inspection is critical to ensure the safety and sustainability of urban rail transit (URT) operation. The common wheel inspection is conducted on a fixed-interval basis, determined by empirical practices. However, the relationship between the distance of wheel travel and wheel wearing condition subject to track alignment is uncertain. A Bayesian model is developed to schedule the timings of wheel inspections which meet the safety thresholds for sustainable train operation. In the case study, the historic wheel inspection data of a real-world URT line was collected and analyzed, which indicates that wheel reprofiling follows a Weibull distribution. The suggested wheel inspection plan by the proposed model is compared with fix-interval inspection. The results show that the inspection frequency can be significantly reduced before yielding 180,900 km wheel travel, which satisfies the wheel reliability as 0.95.

Taking cold-chain logistics as the research background and combining with the overall optimisation of logistics distribution networks, we develop two-stage distribution location-routing model with the minimum total cost as the objective function and varying vehicle capacity in different delivery stages. A hybrid genetic algorithm is designed based on coupling and collaboration of the two-stage routing and transfer stations. The validity and feasibility of the model and algorithm are verified by conducting a randomly generated test. The optimal solutions for different objective functions of two-stage distribution location-routing are compared and analysed. Results turn out that for different distribution objectives, different distribution schemes should be employed. Finally, we compare the two-stage distribution location-routing to single-stage vehicle routing problems. It is found that a two-stage distribution location-routing system is feasible and effective for the cold-chain logistics network, and can decrease distribution cost for cold-chain logistics enterprises.

To improve the performance of lithium-ion power battery thermal management system, multi-walled carbon nanotubes (MWCNT)-based, graphene-based and MWCNT/graphene-based composite phase change materials (PCM) were prepared and experimentally studied. MWCNT-based and graphene-based composite PCM were studied to obtain the optimal addition amounts which could not only improve thermal conductivity but also restrain a rapid temperature rise in liquid PCM. Based on that, composite PCM with various proportion of graphene and MWCNT have been prepared and characterized under the optimal addition amount. In comparison, results showed that composite PCM at the MWCNT/graphene mass ratio of 3/7 could exhibit the best synergistic enhancement heat transfer effect, which the thermal conductivity was increased by 31.8%, 55.4% and 124% compared to graphene-based composite PCM, MWCNT-based composite PCM and pure PCM respectively. In addition, this composite PCM has the highest increase/decrement rate of temperature which can be shortened by 63.3% and 50.0% compared to pure PCM. Finally, the phase change properties and thermal properties of the MWCNT/graphene-based PCM were compared with those of pure PCM. The composite PCM showed great potential in lithium-ion power battery thermal management.

With the improvement of energy saving and environmental protection requirements, the market share of heat pump water heater (HPWH) is gradually increasing. In order to increase the heat storage capacity and improve the performance of the system, an air-source HPWH with storage tank using water and finned phase change material (PCM) for heat storage is designed and experimentally studied. Performances of the HPWH for the first operation when the PCM is in heat storage and the second operation when the PCM is in heat discharge were compared and discussed. Furthermore, performances between the HPWH using water-PCM for heat storage and conventional HPWH with standard water tank were compared. The results show that the volume of the storage tank has little increase, but the heat storage is increased by 14%. Besides, when water is heated from 15 to 55 °C, the first operation time for the HPWH is almost the same as the normal operation time of the conventional HPWH, but the second operation time is shortened by13%.The results also show that the HPWH has good water temperature uniformity and its COP was improved.

Flow around an oscillating cylinder in a subcritical region are numerically studied with a lattice Boltzmann method (LBM). The effects of the Reynolds number, oscillation amplitude and frequency on the vortex wake modes and hydrodynamics forces on the cylinder surface are systematically investigated. Special attention is paid to the phenomenon of resonance induced by the cylinder oscillation. The results demonstrate that vortex shedding can be excited extensively under subcritical conditions, and the response region of vibration frequency broadens with increasing Reynolds number and oscillation amplitude. Two distinct types of vortex shedding regimes are observed. The first type of vortex shedding regime (VSR I) is excited at low frequencies close to the intrinsic frequency of flow, and the second type of vortex shedding regime (VSR II) occurs at high frequencies with the Reynolds number close to the critical value. In the VSR I, a pair of alternately rotating vortices are shed in the wake per oscillation cycle, and lock-in/synchronization occurs, while in the VSR II, two alternately rotating vortices are shed for several oscillation cycles, and the vortex shedding frequency is close to that of a stationary cylinder under the critical condition. The excitation mechanisms of the two types of vortex shedding modes are analyzed separately.