Competitiveness of container terminal operating companies in South Korea and the industry–university–government network

Burgeoning container port facilities have fostered intensified competition among container terminal operating companies (CTOCs). However, despite research into their survival strategies which identified antecedents of competitiveness including hard factors such as facilities, available cargo and cargo processing ability, softer factors spanning human resource management, networks and strategic alliances with universities and government agencies in industry–university–government (I–U–G) networks have been overlooked. This study aims to examine both hard and softer antecedents of competitiveness as perceived by 152 professionals in South Korean CTOCs; empirical relationships among these antecedents, I–U–G networks, and competitiveness itself; and the significance of the I–U–G network in establishing and improving competitiveness. Posited antecedents of competitiveness included human resources, facilities, service quality, customer orientation, reputation, and government support policy as independent variables; the I–U–G network as a moderating variable; and competitiveness as a dependent variable. Empirical structural relationships revealed that excepting government support policy, each variable significantly affected CTOC competitiveness. Further, the I–U–G network moderated the relationships between the antecedents of competitiveness and competitiveness. Because an effective I–U–G network was pivotal in controlling CTOC competitiveness, improved competitiveness requires not only differentiation of human resources, facilities, service quality, customer orientation, and reputation factors but also I–U–G network developments.     

A modified reinforcement learning algorithm for solving coordinated signalized networks

This study proposes Reinforcement Learning (RL) based algorithm for finding optimum signal timings in Coordinated Signalized Networks (CSN) for fixed set of link flows. For this purpose, MOdified REinforcement Learning algorithm with TRANSYT-7F (MORELTRANS) model is proposed by way of combining RL algorithm and TRANSYT-7F. The modified RL differs from other RL algorithms since it takes advantage of the best solution obtained from the previous learning episode by generating a sub-environment at each learning episode as the same size of original environment. On the other hand, TRANSYT-7F traffic model is used in order to determine network performance index, namely disutility index. Numerical application is conducted on medium sized coordinated signalized road network. Results indicated that the MORELTRANS produced slightly better results than the GA in signal timing optimization in terms of objective function value while it outperformed than the HC. In order to show the capability of the proposed model for heavy demand condition, two cases in which link flows are increased by 20% and 50% with respect to the base case are considered. It is found that the MORELTRANS is able to reach good solutions for signal timing optimization even if demand became increased.     

Large-scale evacuation network optimization: a bi-level control method with uncertain arterial demand

To improve the efficiency of large-scale evacuations, a network aggregation method and a bi-level optimization control method are proposed in this paper. The network aggregation method indicates the uncertain evacuation demand on the arterial sub-network and balances accuracy and efficiency by refining local road sub-networks. The bi-level optimization control method is developed to reconfigure the aggregated network from both supply and demand sides with contraflow and conflict elimination. The main purpose of this control method is to make the arterial sub-network to be served without congestion and interruption. Then, a corresponding bi-objective network flow model is presented in a static manner for an oversaturated network, and a Genetic Algorithm-based solution method is used to solve the evacuation problem. The numerical results from optimizing a city-scale evacuation network for a super typhoon justify the validity and usefulness of the network aggregation and optimization control methods.     

基于OpenStreetMap的城市自行车网建模与多判据路径规划

自行车交通是一种健康、绿色、环保的交通出行方式,合理地规划自行车出行路线对于满足骑行者多元化出行需求、构建安全规范的城市交通出行环境具有重要的意义.在对真实骑行者路径选择行为深入分析的基础上,提出了基于Open Street Map的城市自行车网络的构建方法及基于其上的多判据自行车路径优化的数学模型,并给出了求解该模型的一种基于聚类的最优多判据路径规划方法.实验仿真结果表明,该模型及方法能够生成满足骑行者真实需求的多判据路线方案.     

Vulnerability analysis for large-scale and congested road networks with demand uncertainty

To assess the vulnerability of congested road networks, the commonly used full network scan approach is to evaluate all possible scenarios of link closure using a form of traffic assignment. This approach can be computationally burdensome and may not be viable for identifying the most critical links in large-scale networks. In this study, an “impact area” vulnerability analysis approach is proposed to evaluate the consequences of a link closure within its impact area instead of the whole network. The proposed approach can significantly reduce the search space for determining the most critical links in large-scale networks. In addition, a new vulnerability index is introduced to examine properly the consequences of a link closure. The effects of demand uncertainty and heterogeneous travellers’ risk-taking behaviour are explicitly considered. Numerical results for two different road networks show that in practice the proposed approach is more efficient than traditional full scan approach for identifying the same set of critical links. Numerical results also demonstrate that both stochastic demand and travellers’ risk-taking behaviour have significant impacts on network vulnerability analysis, especially under high network congestion and large demand variations. Ignoring their impacts can underestimate the consequences of link closures and misidentify the most critical links.     

基于BP神经网络基坑支护结构变形预测研究

为了精确的预测佛山某建筑基坑变形情况,提出了一种BP神经网络多数据纵向预报方法,选取前三次的多个测点数据进行预处理,然后再对BP神经网络分析、训练、测试,并分析隐含层数对预测精度的影响,通过对比试算确定隐含层数目,最终达到能够高精度预测基坑支护结构水平位移和沉降。结果表明:所提出的方法可以精确地预测基坑变形,对工程建设具有一定的参考价值。     

岩溶隧道风险等级模糊综合评价

为了获得已探明有溶洞存在时的隧道发生涌水突泥的风险等级,针对叙大铁路有溶洞存在的中坝隧道,选取了针对性的指标体系:溶洞规模,建立了相应的模糊综合评价模型,并采用层次分析法计算各项风险指标的权重,从而得出在有溶洞存在时这一特定条件下,隧道发生涌水突泥风险级别。     

某款纯电动客车系统匹配和方案设计

结合某款纯电动客车的研发与应用,介绍纯电动系统的匹配设计和整车CAN网络架构,并提出合理的纯电动系统高压配电管理方案。     

基于模糊神经网络的智能车辆循迹控制

为提高智能车辆自主循迹控制的精度,提出了一种基于模糊神经网络控制( FNNC)和神经网络预测( NNP)的智能循迹控制策略。转向控制器的输入量有3个：预瞄点处的横向循迹误差、汽车横摆角速度和侧向加速度。车速控制器的输入量有4个：预瞄点处的面积误差、侧向加速度、汽车侧偏角和转向盘转角。网络训练采用误差反向传播法。仿真与试验结果表明,所设计的循迹控制器通过对驾驶员操作样本的训练,能实现对车辆的车速与转向控制,横向循迹误差和目标车速均比较理想。     

基于多目标优化的兴趣点指路标志布设模型

在指路标志的工程应用中,常要为兴趣点规划一条布设指路标志的指引路径.首先,基于道路网络的拓扑表达,考虑指引路径的长度、交通量和设置指路标志的数量,定义了衡量指引路径优劣的多目标搜索路径度量值.其次,以网络分析为基础,建立了搜寻最优指引路径的模型,从给定入口开始,以广度搜索算法在路网上搜索,并动态更新搜索路径的度量值,直至获取优化的指引路径.最后,以广州大学城为试验区域,利用上述模型计算得到了从南沙港快线出口到中山大学的指路标志布设的规划路径.对比规划路径与最短路径的多目标优化度量值表明:规划路径兼顾了布设路径的便捷性和指路标志牌布设成本的特点,模型是可行有效的.