关于天津临港产业区总体规划的构想

从规划选址和建设规模、功能定位、平面布局、规划中的重点问题等四个方面,对临港产业区的总体规划构想进行了阐述。     

城市轨道交通运行服务效果策划与应用

在分析城市轨道交通发展现有问题和面临挑战的基础上,指出城市轨道交通需要充分考虑轨道交通出行者个性需求和更多相关利益者的服务需求,才能实现轨道交通与城市融合发展,提升城市轨道交通服务水平。通过分析现有城市轨道交通发展过程中存在的问题和不足,论述开展城市轨道交通运行服务效果策划工作的必要性,阐述城市轨道交通运行服务效果策划工作的内容和方法,并在乌鲁木齐城市轨道交通1号线中成功应用,可为其他城市轨道交通发展所借鉴和参考。     

城市重大枢纽工程施工期交通组织方案研究

施工期间交通组织方案的研究和制定对城市综合枢纽的建设而言意义重大。以南京市浦口区浦泗综合枢纽工程施工期交通组织方案研究为例,阐述了城市枢纽工程施工期交通组织规划的方法和措施。利用工程实践证明,合理规划交通组织方案对降低工程施工成本、减小城市交通的影响、保证工程建设和城市交通安全的效果。     

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。     

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。     

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.     

Integrating mesoscopic dynamic traffic assignment with agent-based travel behavior models for cumulative land development impact analysis

A number of approaches have been developed to evaluate the impact of land development on transportation infrastructure. While traditional approaches are either limited to static modeling of traffic performance or lack a strong travel behavior foundation, today’s advanced computational technology makes it feasible to model an individual traveler’s response to land development. This study integrates dynamic traffic assignment (DTA) with a positive agent-based microsimulation travel behavior model for cumulative land development impact studies. The integrated model not only enhances the behavioral implementation of DTA, but also captures traffic dynamics. It provides an advanced yet practical approach to understanding the impact of a single or series of land development projects on an individual driver’s behavior, as well as the aggregated impacts on the demand pattern and time-dependent traffic conditions. A simulation-based optimization (SBO) approach is proposed for the calibration of the modeling system. The SBO calibration approach enhances the transferability of this integrated model to other study areas. Using a case study that focuses on the cumulative land development impact along a congested corridor in Maryland, various regional and local travel behavior changes are discussed to show the capability of this tool for behavior side estimations and the corresponding traffic impacts.