交通枢纽型商业中心的问题探讨及改善策略分析

城市商业中心区是城市中交通拥堵的多发地带,而交通枢纽及商业混合区域更是给交通带来了巨大的压力。文中结合镇江市城际枢纽站商业中心区的实例,对城市交通枢纽及商业混合区域的交通问题剖析,并对交通枢纽型商业中心区的交通改善策略进行了相应研究。通过采用公交优先、布置交通设施、构建先进的停车系统、合理组织交通流等措施,为该类地区的交通改善和治理提出对策,使得交通枢纽型商业中心的交通组织更加合理有序,尽可能地减少交通拥堵,促进城市和谐发展。     

施工项目HSE管理体系建设与思想工作研究

HSE的宗旨是对工程项目实行职业健康、安全、环境的全员、全方位、全过程的管理,并使其对项目建设本身的危险、对社会的危害、对环境的破坏降到最低点,是实现工程项目建设目标的需要。该文首先介绍了研究HSE的意义,然后分析了现阶段我国HSE管理存在的问题,最后提出了HSE管理体系该怎样建设。     

驾驶过程中手机使用行为数据采集及分析

驾驶过程中使用手机的行为存在安全隐患,是当今导致交通安全事故的原因之一.文章借助于信息技术开发出采集使用手机行为数据的App,从真实数据出发,客观地分析驾驶员在开车过程中使用手机这一不良驾驶行为的覆盖程度及危险程度,并提出将手机使用行为作为驾驶风险评价因子.     

基于数据挖掘的城市机动车停车调查分析研究

进行城市机动车停车场规划前需要开展停车场调查,以得到停车场的基本特征参数、机动车进出记录和停车者的停车行为等信息。通过数据挖掘中的一些方法可以在数据库中挖掘出有用的信息。根据机动车进出停车场的记录计算一系列的停车指标,并借助多元统计分析中典型相关分析的方法,分析停车指标与停车场基本特征参数之间的关系。然后利用关联规则挖掘停车问询调查数据中的潜在信息,分析停车者的行为特征,以掌握更多的停车信息。     

跟驰过程中驾驶员认知结构模型的建立

在道路交通4要素中(人、车、路、环境),人以其主动性和智慧性起着支配作用,是其中的主体要素。基于认知心理学的有关知识,论文采用因子分析法对五轮仪实验系统观测到的车辆跟驰数据进行分析,确定了对车辆跟驰信息提取过程有独立作用的4个因素,相应地将驾驶员认知过程划分为4个阶段,建立了车辆跟驰过程的驾驶员认知结构模型。为驾驶行为研究和车辆跟驰模型的建立提供了理论基础。     

预制UHPC组合梁槽口式连接界面抗剪性能研究

相比现浇混凝土桥面板,全预制混凝土桥面板有诸多优势,能够提高桥梁工程质量、加快桥梁施工速度和降低成本.预制超高性能混凝土(Ultra-high Performance Concrete,UHPC)梁和预制UHPC桥面板通过槽口连接形成组合梁是一种新的结构形式,这种槽口式连接的界面抗剪性能会影响全梁整体承载力.通过16个...     

Travel time estimation oriented freeway sensor placement problem considering sensor failure

Optimal sensor placement on freeway corridor is of great interest to transportation authorities. However, current traffic sensors are easily subject to various failures. Therefore, it is necessary to incorporate sensor failure into the optimal sensor placement model. In this article, a two-stage stochastic model is proposed for the purpose of travel time estimation on freeway corridor. To balance the effectiveness and reliability, a stochastic conditional value at risk (CVaR) model is also proposed. Since both models are too complicated, a customized genetic algorithm is developed. Numerical experiments show that considering sensor failure makes a significant performance improvement in the sensor placement pattern. Sensitivity analysis is also applied to investigate the impact of a number of allowable sensors and different traffic sensor failure probability.     

Classification of Livebus arrivals user behavior

With the increasing use of Intelligent Transport Systems, large amounts of data are created. Innovative information services are introduced and new forms of data are available, which could be used to understand the behavior of travelers and the dynamics of people flows. This work analyzes the requests for real-time arrivals of bus routes at stops in London made by travelers using Transport for London's LiveBus Arrivals system. The available dataset consists of about one million requests for real-time arrivals for each of the 28 days under observation. These data are analyzed for different purposes. LiveBus Arrivals users are classified based on a set of features and using K-Means, Expectation Maximization, Logistic regression, One-level decision tree, Decision Tree, Random Forest, and Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO). The results of the study indicate that the LiveBus Arrivals requests can be classified into six main behaviors. It was found that the classification-based approaches produce better results than the clustering-based ones. The most accurate results were obtained with the SVM-SMO methodology (Precision of 97%). Furthermore, the behavior within the six classes of users is analyzed to better understand how users take advantage of the LiveBus Arrivals service. It was found that the 37% of users can be classified as interchange users. This classification could form the basis of a more personalized LiveBus Arrivals application in future, which could support management and planning by revealing how public transport and related services are actually used or update information on commuters.     

Plate scanning tools to obtain travel times in traffic networks

The management of vehicle travel times has been shown to be fundamental to traffic network analysis. To collect travel time measurement, some methods focus solely on isolated links or highway segments, and where two measurement points, at the beginning and at the end of a section, are deemed sufficient to evaluate users' travel time. However, in many cases, transport studies involve networks in which the problem is more complex. This article takes advantage of the plate scanning technique to propose an algorithm that minimizes the required number of registering devices and their location in order to identify vehicles candidates to compute the travel times of a given set of routes (or subroutes). The merits of the proposed method are explained using simple examples and are illustrated by its application to the real network of Ciudad Real.     

基于社会力模型的地铁站乘客上下车行为影响因素分析

在上下班高峰期,缩短乘客的上下车时间,更多乘客将会被送达目的地,从而可以提高现有地铁系统的载客能力.现有的国内外研究乘客上下车行为的文献,大多以全部乘客下车为前提进行研究,这与现实生活中只有部分乘客下车的现象不符.因此在社会力模型的基础上加入规定未下车乘客行为的等待模型,在等待模型中,未下车乘客倾向于停留在初始位置.模拟了部分乘客下车行为,并研究了乘客初始位置分布、上下车乘客比例、门的宽度等因素对部分上下车乘客所需上下车时间的影响.结果表明,车门宽度增加23.08%,最多可节约39.7%的上车时间和39.62%的下车时间;随着上下车人数比例不断上升,平均上车时间不断下降,平均下车时间不断上升;通过改变下车乘客初始分布位置,可以将乘客下车时间缩短至原来的72.16%.