公交专用道设置标准研究——以南昌市为例

对公交专用道设置的重要影响因素进行交通工程学分析,确定各因素基本阈值。以设置规模为约束条件.建立南昌市交通模型,对各影响因素进行组合分析,以此确定各类组合条件阈值.形成南昌市公交专用道设置标准。     

上海松江区现代有轨电车规划研究

现代有轨电车因其环保、舒适、高效、低造价、高品质等特点,在国际上得到广泛应用,我国多个城市也在规划和建设之中。但是,由于其在国内尚为一种全新的交通方式,没有标准或规范予以指导,规划及设计体系不完善,有些已建项目已呈现问题。文章结合上海松江区规划背景及交通特征,阐述有轨电车示范线T1、T2线的布局规划、建设的必要性及适应性,为即将开展有轨电车规划及建设的城市提供借鉴与参考。     

基于改进TOPSIS的城市客运综合交通枢纽评价

城市客运综合交通枢纽规划方案的优劣对城市综合交通系统的建设发展具有重大影响。为了提高城市客运综合交通枢纽规划方案评价的客观性和准确性,结合城市客运综合交通枢纽客流衔接换乘的系统分析,从多维角度出发构建了城市客运综合交通枢纽规划方案评价的多层次指标体系,在遵循方案评价基本原理及城市客运综合交通枢纽特点的基础上,提出了基于改进逼近理想点排序法的评价方法,用于城市客运综合交通枢纽方案的评价,建立城市客运综合交通枢纽综合评价模型。     

Towards a model of hydrodynamic damping for a circular cylinder with helical strakes at low KC

This paper investigates the hydrodynamic damping of a circular cylinder with helical strakes at Keulegan-Carpenter (KC) number from 0.07 to 3 in the presence of steady currents. Experiments were performed with a straked cylinder oscillating in either in-line or cross currents over Reynolds number (based on the oscillating velocity amplitude) varying from 1260 to 54,000. With in-line current being present, the measured drag coefficients of the straked cylinder are found to depend on the ratio between the oscillating velocity amplitude and the steady current velocity. This phenomenon is further confirmed by computational fluid dynamics using large-eddy simulations. The drag coefficients obtained from the numerical simulations agree well with the experimentally determined values. Similar phenomenon is observed for the cases with cross background current. Based on the experimental data, empirical formulae are proposed to evaluate drag coefficients. These results are of importance in estimating the resonant motion and the fatigue life of risers, e.g. water intake risers, in the flow regime of low KC. Finally, recommendations are provided for fatigue analysis of risers with helical strakes from the perspective of engineering practice.     

基于ADINA的水润滑轴承结构改进研究

应用有限元软件ADINA分别建立了传统水润滑橡胶轴承及其改进结构的CFD和FSI模型,分析比较水流轴向流速、轴的周向转速等参数对轴瓦变形和水膜压力分布的影响规律。计算结果表明：本文提出的轴承改进结构承载力较传统的水润滑轴承提高20%。具有较好的工程应用前景。     

水流速度对内河船舶营运能效影响分析

本文在能源消耗和环境污染的危机下,致力于内河船舶能源消耗和温室气体排放的研究,分析了水流速度对内河船舶营运能效的影响。本分析是基于一艘客船的数据进行的,分别对不同航段下水流速度,主机转速,燃油消耗进行测量,并计算得到内河船舶营运能效。根据测量结果提出内河船舶营运能效提升策略,为内河航运业的节能减排提供帮助。     

The Storage Space Allocation Problem at a Container Terminal

Due to the asymmetry between the time required to retrieve a container to vessel and the time required to register a container at the gate, the storage space allocation problem solves the best allocation policy of these containers that minimizes the potential number of gantry movements during the assigning process and the future retrieval process. A decision rule-based heuristic is proposed, and three properties are discovered in this research. Comprehensive numerical experiments show that the proposed heuristic can solve the real-sized instances within a second, making it possible to account for uncertainty because the re-optimization effort is negligible. Moreover, when applying the proposed heuristic to a container yard at the Port of Kaohsiung in Taiwan and considering the extreme cases, the heuristic performs even better when the number of containers increases and still requires less than 1 s to solve these extreme cases. Finally, the proposed heuristic can be applied to different layouts of storage space and various material handling machines.     

多智能体仿真在LNG码头选址及港口规划中的应用

液化天然气(LNG)船舶进出港进行的严格交通管制会影响港口的运营,因此在港口规划阶段须审慎决策LNG码头选址及布局问题。应用自主开发的《基于多智能体信息交互的港口运营系统仿真软件(简称:APSS)V1.0》,建立可模拟涵盖LNG船舶通航影响机制的港口运营系统仿真模型;以实际工程为例,系统探讨LNG船舶通航对近、远期港口运营影响问题的思路和方法;结合水域通航环境特点提出远期优化措施。研究结果可辅助港口规划制定。     

船舶压载水处理技术和监督管理研究

船舶压载水不仅危害海洋环境还会对人类健康、当地经济造成影响,《国际船舶压载水和沉积物控制和管理公约》实施即说明了国际社会对此的重视。压载水处理系统的使用,有效地减小了压载水对环境的危害。然而在实际应用中,仍需要通过提高和改进压载水监控技术,使处理后的压载水进一步符合公约标准。本研究根据我国压载水监控现状,提出完善压载水监督管理的措施,促进我国环境、经济和资源的可持续发展。     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.