网络计划技术在船舶修造中的应用

文章介绍网络计划技术方法,并将其应用于船舶修造的生产管理,可以克服横道图的缺点,实现利用计算机在船舶修造生产中的现代化管理。     

盾构施工筹划原则及影响因素分析

以北京地铁4号线11合同段(灵境胡同-新街口)四站三区间的盾构施工筹划为例,重点分析影响盾构施工筹划的因素;以方案比较的方法,介绍了确定三区间总体筹划方案的详细过程.     

路面铣刨刀具失效机理与形式分析

对路面铣刨刀具结构组成、切削时象、工作状态和失效机理的进行了简洁的论述。着重分析了刀具正常失效和非正常失效产生的原因．提出了保证刀具正常失效的措施。     

Physical environments influencing bicyclists’ perception of comfort on separated and on-street bicycle facilities

This study investigates the impacts of physical environments on bicyclists’ perceptions of comfort on separated and on-street bicycle facilities. Based on a field investigation conducted in Nanjing, China, we find that physical environmental factors significantly influencing bicyclists’ perception of comfort on the two types of facility. Cyclists’ comfort is mainly influenced by the road geometry and surrounding conditions on physically separated paths while they pay attention to the effective riding space and traffic situations on on-street bicycle lanes.     

盐田港总体规划实施的环境影响研究

在对盐田港及附近区域环境质量现状调查、监测与评价的基础上,依据相关的环保法规与标准,集合区域社会、经济发展的规划目标,识别规划环境影响的主要因子,提出盐田港区总体规划开发过程中的主要环境问题,从而提出合理开发利用资源、调整经济发展格局及环保综合治理的措施和建议,用以指导建设生态型现代化的盐田港区。     

基于MapBasic的战时运输最优路径问题研究

战时运输最优路径问题是一个多目标多约束随机动态路网寻优问题。在分析战时运输最优路径问题特性前提下,着重研究战时运输路阻函数模型,求出时间阻抗、风险阻抗和费用阻抗,标定阻抗参数μ1,μ2和μ3,及确定函数模型的MapBasic表达,在给出最优路径模型基础上,利用改进的Dijkstra算法求解。实例验证表明研究成果满足实用要求。     

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。     

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。     

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.