Effects of the ‘Belt and Road’ initiative on the wine import logistics of China

China’s Belt and Road (BR) initiative is expected to facilitate international trade between the countries involved. In this study, we review the special requirements of wine logistics and the market growth potential of wine consumption in China. A model minimizing generalized logistical costs is developed and applied to wine imports in China, so that the best candidate cities in which to locate transport gateways and distribution centers can be identified. Our analysis suggests that the most preferred gateways are Shanghai, Tianjin, Guangzhou, and Hong Kong, which all have similar delivery costs. In comparison, Beijing and Chongqing have much higher delivery costs, mainly because they do not have good access to marine transport and/or efficient domestic distribution networks. For long-distance intercontinental transport of large volumes of wine, marine shipping remains the only viable delivery mode. However, wine distribution within China extensively uses air, road, and water transport. Therefore, cities with excellent multi-modal transport services are better positioned to become wine logistics gateways. Our study also highlights the importance of value-added services and good government support, which are important factors that influence distribution costs and quality.     

新型四冲程发动机液压可变配气机构的动态仿真与分析

提出一种新型的四冲程发动机液压可变配气机构。建立系统非线性数学模型,并分析配气机构的动态特性。仿真和试验结果表明,新的液压配气机构能实现可变气门正时及开度的控制,可适用于高转速发动机,具有一定的实用性。     

Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region.     

Bus arrival time prediction at bus stop with multiple routes

Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. This paper proposes models to predict bus arrival times at the same bus stop but with different routes. In the proposed models, bus running times of multiple routes are used for predicting the bus arrival time of each of these bus routes. Several methods, which include support vector machine (SVM), artificial neural network (ANN), k nearest neighbours algorithm (k-NN) and linear regression (LR), are adopted for the bus arrival time prediction. Observation surveys are conducted to collect bus running and arrival time data for validation of the proposed models. The results show that the proposed models are more accurate than the models based on the bus running times of single route. Moreover, it is found that the SVM model performs the best among the four proposed models for predicting the bus arrival times at bus stop with multiple routes.     

Path-planning algorithm for ships in close-range encounters

Efficient maritime navigation through obstructions at close range is still one of the many problems faced by mariners. The increasing traffic densities and average cruise speed of ships also impede the collision avoidance process by reducing the time available for decision making. This study develops a path-planning algorithm that determines an optimal navigation path for ships in close-range encounters, using known and predicted data about the traffic and environment. An improved ship dynamic model has also been developed to better approximate the ship movement under external influences, and the algorithm was evaluated with a set of test cases simulating various traffic scenarios typically encountered at close range. Since the method is based on evolutionary algorithm, the algorithm behaviours were evaluated statistically, where properties such as consistency and practicality of the algorithm outputs are discussed.     

Nonlinear gating control for urban road traffic network using the network fundamental diagram

This work proposes a nonlinear model predictive controller for the urban gating problem. The system model is formalized based on a research on existing models of the network fundamental diagram and the perimeter control systems. For the existing models, modifications are suggested: additional state variables are allocated to describe the queue dynamics at the network gates. Using the extended model, a nonlinear model predictive controller is designed offering a ‘non‐greedy’ policy compared with previous, ‘greedy’ gating control designs. The greedy and non‐greedy nonlinear model predictive control (NMPC) controllers are compared with a greedy linear feedback proportional‐integral‐derivative (PID) controller in different traffic situations. The proposed non‐greedy NMPC controller outperforms the other two approaches in terms of travel distance performance and queue lengths. The performance results justify the consideration of queue lengths in dynamic modeling, and the use of NMPC approach for controller design. Copyright © 2014 John Wiley & Sons, Ltd.     

Modelling rain effects on risk‐taking behaviours of multi‐user classes in road networks with uncertainty

This paper proposes a new travel time reliability‐based traffic assignment model to investigate the rain effects on risk‐taking behaviours of different road users in networks with day‐to‐day demand fluctuations and variations in travel time. A generalized link travel time function is used to capture the rain effects on vehicle travel times and road conditions. This function is further incorporated into daily demand variations to investigate those travel time variations arising from demand uncertainty and rain condition. In view of these rain effects, road users' perception errors on travel times and risk‐taking behaviours on path choices are incorporated in the proposed model with the use of a logit‐based stochastic user equilibrium framework. This new model is formulated as a variational inequality problem in terms of path flows. A numerical example is used to illustrate the application of the proposed model for assessment of the rain effects on road networks with uncertainty.     

Collision risk assessment for ships

Efficient maritime navigation through dynamic obstructions at close range is still a serious issue faced by mariners. There have been studies focusing on collision risk assessment in the past, but the majority were based on the first person perspective, with area-based ship domain concepts that are defined around either the ownship or the obstacle. Such methods are acceptable for encounters where the ownship is required to manoeuvre according to the collision regulations (COLREGs), but they will not work correctly if the ownship is the stay-on party. This article presents an alternative method of assessing the collision risk for surface ships in close-range encounters that is compliant with the COLREGs as well as other ships from different perspectives.     

Short‐term travel time forecasts for transport information system in hong kong

In this paper, a case study is carried out in Hong Kong for demonstration of the Transport Information System (TIS) prototype. A traffic flow simulator (TFS) is presented to forecast the short‐term travel times that can be served as a predicted travel time database for the TIS in Hong Kong. In the TFS, a stochastic deviation coefficient is incorporated to simulate the minute‐by‐minute fluctuation of traffic flows within the peak hour period. The purposes of the case study are: 1) to show the applicability of the TFS for larger‐scale road network; and 2) to illustrate the short‐term forecasting of path travel times in practice. The results of the case study show that the TFS can be applied to real network effectively. The predicted travel times are compared with the observed travel times on the selected paths for an OD pair. The results show that the observed path travel times fall in the 90% confidence interval of the predicted path travel times.     

Modeling and simulation of a dual-mode electrohydraulic fully variable valve train for four-stroke engines

In modern four-stroke automotive engine technology, variable valve timing and lift control offer potential benefits for making a high-performance engine. In this paper, a novel design named dual-mode electrohydraulic fully variable valve train (EHFVVT) for both engine intake and exhaust valves is introduced. The system is mainly controlled by either proportional flow control valves or proportional pressure relief valves, and hence two different families of valve displacement patterns can be achieved. The construction of the mathematical model of the valve train system and its dynamic analysis are also presented in this paper. Experimental and simulation results show that the dual-mode electrohydraulic variable valve train can achieve fully variable valve timing and lift control, and has the potential to eliminate the traditional throttle valve in the gasoline engines. With the proposed system, the engine performance at various speeds and loads will be significantly improved.