Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic.

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.

Evaluation of the Effectiveness of Active and Passive Safety Measures in Preventing Ship-Bridge Collision.

The risk of ship-bridge collisions should be evaluated using advanced models to consider different anti-collision and bridge-protection measures. This study aimed to propose a method to evaluate the effectiveness of active and passive safety measures in preventing ship-bridge collision. A novel ship-bridge collision probability formulation taking into consideration different safety measures was proposed. The model was applied at Jintang Bridge in China where the surrounding vessel traffic is ultra-crowded. We calculated the collision probability between the bridge and passing traffic using automatic identification system (AIS) data, Monte Carlo simulation, and Bayesian networks. Results under four different safety measures (i.e., active measures, passive measures, both measures and none) were analyzed and compared. The analysis concluded that both active and passive safety measures are effective in reducing the ship-bridge collision probability. Active measures, if deployed properly, can provide protection at an equivalent level than passive measures against collision risks. However, passive measures, such as setting arresting cables, are necessary in cases where the response time of the active measures is long. The proposed method and the results obtained from the case study may be useful for robust and systematic effectiveness evaluation of safety measures in other cases worldwide.

A Study on the Decay Model of Multi-Block Taxi Travel Demand under the Influence of Major Urban Public Health Events.

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.

Network capacity with probit-based stochastic user equilibrium problem.

Among different stochastic user equilibrium (SUE) traffic assignment models, the Logit-based stochastic user equilibrium (SUE) is extensively investigated by researchers. It is constantly formulated as the low-level problem to describe the drivers' route choice behavior in bi-level problems such as network design, toll optimization et al. The Probit-based SUE model receives far less attention compared with Logit-based model albeit the assignment result is more consistent with drivers' behavior. It is well-known that due to the identical and irrelevant alternative (IIA) assumption, the Logit-based SUE model is incapable to deal with route overlapping problem and cannot account for perception variance with respect to trips. This paper aims to explore the network capacity with Probit-based traffic assignment model and investigate the differences of it is with Logit-based SUE traffic assignment models. The network capacity is formulated as a bi-level programming where the up-level program is to maximize the network capacity through optimizing input parameters (O-D multiplies and signal splits) while the low-level program is the Logit-based or Probit-based SUE problem formulated to model the drivers' route choice. A heuristic algorithm based on sensitivity analysis of SUE problem is detailed presented to solve the proposed bi-level program. Three numerical example networks are used to discuss the differences of network capacity between Logit-based SUE constraint and Probit-based SUE constraint. This study finds that while the network capacity show different results between Probit-based SUE and Logit-based SUE constraints, the variation pattern of network capacity with respect to increased level of travelers' information for general network under the two type of SUE problems is the same, and with certain level of travelers' information, both of them can achieve the same maximum network capacity.

A simulation study of the impact of the public-private partnership strategy on the performance of transport infrastructure.

The choice of investment strategy has a great impact on the performance of transport infrastructure. Positive projects such as the "Subway plus Property" model in Hong Kong have created sustainable financial profits for the public transport projects. Owing to a series of public debt and other constraints, public-private partnership (PPP) was introduced as an innovative investment model to address this issue and help develop transport infrastructure. Yet, few studies provide a deeper understanding of relationships between PPP strategy and the performance of such transport projects (particularly the whole transport system). This paper defines the research scope as a regional network of freeway. With a popular PPP model, travel demand prediction method, and relevant parameters as input, agents in a simulation framework can simulate the choice of PPP freeway over time. The simulation framework can be used to analyze the relationship between the PPP strategy and performance of the regional freeway network. This study uses the Freeway Network of Yangtze River Delta (FN-YRD) in China as the context. The results demonstrate the value of using simulation models of complex transportation systems to help decision makers choose the right PPP projects. Such a tool is viewed as particularly important given the ongoing transformation of functions of the Chinese transportation sector, including franchise rights of transport projects, and freeway charging mechanism.

To twist or poke? A method for identifying usability issues with the rotary controller and touch screen for control of in-vehicle information systems.

In-vehicle information systems (IVIS) can be controlled by the user via direct or indirect input devices. In order to develop the next generation of usable IVIS, designers need to be able to evaluate and understand the usability issues associated with these two input types. The aim of this study was to investigate the effectiveness of a set of empirical usability evaluation methods for identifying important usability issues and distinguishing between the IVIS input devices. A number of usability issues were identified and their causal factors have been explored. These were related to the input type, the structure of the menu/tasks and hardware issues. In particular, the translation between inputs and on-screen actions and a lack of visual feedback for menu navigation resulted in lower levels of usability for the indirect device. This information will be useful in informing the design of new IVIS, with improved usability. STATEMENT OF RELEVANCE: This paper examines the use of empirical methods for distinguishing between direct and indirect IVIS input devices and identifying usability issues. Results have shown that the characteristics of indirect input devices produce more serious usability issues, compared with direct devices and can have a negative effect on the driver-vehicle interaction.

A usability evaluation toolkit for In-Vehicle Information Systems (IVISs).

Usability must be defined specifically for the context of use of the particular system under investigation. This specific context of use should also be used to guide the definition of specific usability criteria and the selection of appropriate evaluation methods. There are four principles which can guide the selection of evaluation methods, relating to the information required in the evaluation, the stage at which to apply methods, the resources required and the people involved in the evaluation. This paper presents a framework for the evaluation of usability in the context of In-Vehicle Information Systems (IVISs). This framework guides designers through defining usability criteria for an evaluation, selecting appropriate evaluation methods and applying those methods. These stages form an iterative process of design-evaluation-redesign with the overall aim of improving the usability of IVISs and enhancing the driving experience, without compromising the safety of the driver.