基于GPS技术的运输管理系统在钢铁企业中应用研究

如何应用基于GPS技术的运输管理系统对物流运输车辆进行有效指挥、协调控制和管理已经成为现代物流企业面临的一个重要课题。本文介绍了GPS基本组成与功能,分析了基于GPS技术的运输管理监控系统的原理,结合实际案例阐述了基于GPS技术的运输管理系统在现代物流企业中的应用及效果评价。     

基于卫星定位系统的相对位置确定船舶航向的研究

利用卫星导航系统能够全天候实时地得到较为准确的位置,通过得到的相对位置可计算出船舶航向,为船舶提供一种获得航向的新方法。实验证明,利用实时的相对位置求解船舶航向是可行的。     

浅谈档案有效利用在服务交通事业发展中的作用

阐述了科学管理是档案工作服务交通事业的前提,信息收集贮备是服务交通事业的基础,有效利用档案在交通事业发展中起着重要的作用。     

基于浮动车数据的交通事件参数特性研究

利用微观交通仿真软件Vissim搭建交通事件环境,对在不同堵塞程度和不同交通流量的交通事件情况下,浮动车采集的浮动车速、行程时间、浮动车位置等移动检测交通参数变化特性进行了分析比较,总结出交通事件情况下的浮动车移动检测各交通参数变化规律,并以此为基础对模式识别法、统计预测法、时间序列和智能算法等4种基于浮动车数据的交通事件检测算法进行了探讨,具有覆盖面广、成本低等特点。     

公车动态资讯系统关键绩效指标评估之研究

公车动态资讯系统能有效整合客运业者营运流程及资源,提供即时而正确的资讯,且能缩短营运需求的反应时间,提昇乘客满意度,因此有助於客运业在营运竞争上的优势,达成永续发展大众运输的目标。论文研究经由从文献理论的探讨,并进行专家访谈,同时依据客运产业特性、管理目标与企业愿景,分两阶段进行问卷调查,再运用层级分析法（AHP）进行问卷分析,期望能筛选出公车动态资讯系统关键绩效评估的重要指标。     

台湾地区高快速公路资讯可变标志最适显示运用之研析

先进交通管理系统（advancedtrafficmanagementsystems,ATMS）所蒐集之交通资讯及交通管理策略,需经由先进用路人资讯系统（advancedtravelerinformationsystems,ATIS）发布後方显现效用,其中资讯可变标志（changeablemessagesign,CMS）为驾驶者在旅途中最重要的讯息来源。CMS主要显示内容包括交通宣导讯息、即时路段旅行时间及透过事件反应计画自动显示之路况资讯等,本研究针对CMS显示幕显示字体大小、辉度、显示时间,以及显示内容研提建议。依前述原则执行後,台湾地区高速公路用路人对CMS资讯内容的满意度达86．8,〈（2010）。本研究并分析台湾地区CMS中英文双语化之可行性,受限於版面空间、反应时间、系统转换时间及无法自由选择语言之特性,无法同时显示中英文资讯,故建议以使用中文之用路人为主;惟国外旅客较多之路段,建议挑选部分CMS显示英文旅行讯息,其余路段则维持发布中文资讯。     

动力定位系统的最新技术进展分析

随着深海技术的不断发展和推进,动力定位系统的应用越来越广泛,已成为船舶自动化技术中的一个热点问题。从动力定位系统的3个组成部分——位置测量系统、控制系统和推力系统分析了动力定位系统的现状和相关技术的最新进展。     

A porous flow approach to modeling heterogeneous traffic in disordered systems

A continuum model that describes a disordered, heterogeneous traffic stream is presented. Such systems are widely prevalent in developing countries where classical traffic models cannot be readily applied. The characteristics of such systems are unique since drivers of smaller vehicles exploit their maneuverability to move ahead through lateral gaps at lower speeds. At higher speeds, larger vehicles press their advantage of greater motive power. The traffic stream at the microscopic level is disordered and defines a porous medium. Each vehicle is considered to move through a series of pores defined by other vehicles. A speed-density relationship that explicitly considers the pore space distribution is presented. This captures the considerable dynamics between vehicle classes that are overlooked when all classes are converted to a reference class (usually Passenger Car Equivalents) as is traditionally done. Using a finite difference approximation scheme, traffic evolution for a two-class traffic stream is shown.     

Quantifying the benefit of responsive pricing and travel information in the stochastic congestion pricing problem

This paper is concerned with roadway pricing amidst the uncertainty which characterizes long-term transportation planning. Uncertainty is considered both on the supply-side (e.g., the effect of incidents on habitual route choice behavior) and on the demand-side (e.g., due to prediction errors in demand forecasting). The framework developed in this paper also allows the benefits of real-time travel information to be compared directly against the benefits of responsive pricing, allowing planning agencies to identify the value of these policy options or contract terms in publicly-operated toll roads. Specifically, six scenarios reflect different combinations of policy options, and correspond to different solution methods for optimal tolls. Demonstrations are provided on both the Sioux falls and Anaheim networks. Results indicate that providing information to drivers implemented alongside responsive tolling may reduce expected total system travel time by over 9%, though more than 8% of the improvement is due to providing information, with the remaining 1% improvement gained from responsive tolling.     

A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.