Biofuel refinery location and supply chain planning under traffic congestion

This research focuses on planning biofuel refinery locations where the total system cost for refinery investment, feedstock and product transportation and public travel is minimized. Shipment routing of both feedstock and product in the biofuel supply chain and the resulting traffic congestion impact are incorporated into the model to decide optimal locations of biofuel refineries. A Lagrangian relaxation based heuristic algorithm is introduced to obtain near-optimum feasible solutions efficiently. To further improve optimality, a branch-and-bound framework (with linear programming relaxation and Lagrangian relaxation bounding procedures) is developed. Numerical experiments with several testing examples demonstrate that the proposed algorithms solve the problem effectively. An empirical Illinois case study and a series of sensitivity analyses are conducted to show the effects of highway congestion on refinery location design and total system costs.     

广州内河港总体规划

广州内河港属于典型的海运供应链末端型港口。以广州内河港总体规划研究为例,探讨末端型内河港空间布局特征及规划思路,为同类型港口的规划工作提供参考借鉴。     

浅谈城市滨水空间的开发利用——重庆江北城聚贤岩广场及道路工程

聚贤岩位于重庆市江北城,原规划该节点为Y型立体交叉,以交通功能为主,但设计遵循以人为本的理念,最终将车行交通引入地下空间,地面改为休闲文化广场,后期还在广场下方引入江水源热泵集中供冷供热项目。简要介绍重庆滨水空间的现状问题,阐述该项目对规划方案的调整、道路设计、结构设计和排水设计,并介绍了江水源集中供冷供热项目,对滨水空间的开发利用提出了新思路和解决办法。     

槽道滑行艇快速性数值仿真

滑行艇在滑行状态的航行往往伴随着浸湿面积及纵倾角的变化,导致其快速性性能难以评估。为此,本文基于全结构化动态重叠网格求解运动姿态,引入六自由度模块,采用Level-Set水平集方法求解自由液面,对某型槽道滑行艇进行快速性数值仿真。最终,得到了滑行艇在各个航速下对应的阻力、纵倾角、自由液面波形、中纵剖面气液分布以及船底压力分布。为验证可靠性,将纵倾角仿真结果与实船试验结果对比,结果吻合较好。     

多约束条件下无人艇和无人机集群协同航迹规划

[目的]为实现海上无人集群在执行任务过程中的安全航行和通信保持,开展无人艇(USV)和无人机(UAV)集群协同航迹规划问题的研究。[方法]采用禁入和禁出地理围栏进行场景建模,将规避威胁和障碍问题转化为地理围栏约束。针对平台之间的碰撞冲突和通信连接问题,提出基于时序检测的碰撞冲突和通信保持约束判断准则。以集群平均航行时间为航迹优化函数,将多约束条件转化为惩罚函数,采用自适应差分进化算法进行优化求解。[结果]仿真结果表明,所提方法能够在威胁和障碍环境中保持无人艇和无人机集群的安全航行和通信连接,并在满足多约束的条件下实现集群平均航行时间最短。[结论]该方法可用于海上无人集群面对威胁和障碍环境时的离线航迹规划,具有一定的应用价值。     

城市轨道交通企业多元化发展对比研究

城市轨道交通投资周期长、成本高、收益见效慢,许多轨道交通企业负债累累,但随着城市轨道交通在我国的高速发展,越来越多的轨道交通企业开启多元化经营进而实现多元化发展。本文列举了四个我国典型大都市的轨道交通多元化发展情况,结合政府颁布的土地政策,可以看出轨道交通多元化发展的重要性,以及政府将土地交由轨道交通企业自行开发、使用,政府在其中主要充当监管者的角色。这样有助于实现其多元化经营,进而带动多元化发展与城市可持续发展。     

改进深度确定性策略梯度的决策算法研究

为解决无人驾驶路径规划过程中的决策控制问题,文章针对深度确定性策略梯度算法在未知环境中随着搜索空间的增大,出现训练效率低、收敛不稳定等缺点,提出了基于奖励指导的改进算法。首先在每回合内采用基于奖励的优先级经验回放,减少深度确定性策略梯度算法随机探索的盲目性,提高智能车学习效率。然后在回合间基于奖励筛选优秀轨迹,便于指导智能车对复杂空间的探索,得到稳定的控制策略。最后,在开源智能驾驶仿真环境进行仿真。实验结果表明改进后的深度确定性策略梯度算法性能优于原来的算法,训练效率和收敛稳定性均得到有效提升。     

港产城融合发展关键问题研究

在建设世界一流港口及水运高质量发展背景下,港产城融合发展被提到新的高度,成为港口及所依托城市发展的重点。针对如何实现港产城融合发展这一难题,研究港产城融合发展的内涵及机理,分析促进融合发展的3个关键问题,给出解决思路,并结合规划实践,对不同类型港产城融合发展的关键问题进行分析。在新的国土空间规划体系下,研究思路及结论对促进港产城融合发展落地实施具有参考价值。     

大西安都市区轨道交通线网规划与优化思路分析

为探讨城市变化过程中线网规划的发展规律、变化趋势及优化方法,以大西安都市区为例,研究历次城市规划、线网规划主要技术指标特征,通过ROXY指数定量分析其变化特征并分析存在问题,结合研究对象特征提出未来线网优化思路并给出优化方案。经计算,ROXY指数在不同阶段的结果分别为161.75,16.20,-85.41,结果表明大西安都市区呈现集中-分散的发展趋势,需求特征日趋多样化。既有线网规划存在服务范围小、多网融合程度低、线网层次单一等问题,可通过发展多网融合、多层次多制式轨道交通来满足城市发展与交通需求的变化。     

Charging infrastructure demands of shared-use autonomous electric vehicles in urban areas

Ride-hailing is a clear initial market for autonomous electric vehicles (AEVs) because it features high vehicle utilization levels and strong incentive to cut down labor costs. An extensive and reliable network of recharging infrastructure is the prerequisite to launch a lucrative AEV ride-hailing fleet. Hence, it is necessary to estimate the charging infrastructure demands for an AEV fleet in advance. This study proposes a charging system planning framework for a shared-use AEV fleet providing ride-hailing services in urban area. We first adopt an agent-based simulation model, called BEAM, to describe the complex behaviors of both passengers and transportation systems in urban cities. BEAM simulates the driving, parking and charging behaviors of the AEV fleet with range constraints and identifies times and locations of their charging demands. Then, based on BEAM simulation outputs, we adopt a hybrid algorithm to site and size charging stations to satisfy the charging demands subject to quality of service requirements. Based on the proposed framework, we estimate the charging infrastructure demands and calculate the corresponding economics and carbon emission impacts of electrifying a ride-hailing AEV fleet in the San Francisco Bay Area. We also investigate the impacts of various AEV and charging system parameters, e.g., fleet size, vehicle battery capacity and rated power of chargers, on the ride-hailing system’s overall costs.