Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

Mining missing train logs from Smart Card data

This paper shows how to recover the arrival times of trains from the gate times of metro passengers from Smart Card data. Such technique is essential when a log, the set of records indicating the actual arrival and departure time of each bus or train at each station and also a critical component in reliability analysis of a transportation system, is missing partially or entirely. The procedure reconstructs each train as a sequence of the earliest exit times, called S-epochs, among its alighting passengers at each stations. The procedure first constructs a set of passengers, also known as reference passengers, whose routing choices are easily identifiable. The procedure then computes, from the exit times of the reference passengers, a set of tentative S-epochs based on a detection measure whose validity relies on an extreme-value characteristic of the platform-to-gate movement of alighting passengers. The tentative S-epochs are then finalized to be a true one, or rejected, based on their consistencies with bounds and/or interpolation from prescribed S-epochs of adjacent trains and stations. Tested on 12 daily sets of trains, with varying degrees of missing logs, from three entire metro lines, the method restored the arrival times of 95% of trains within the error of 24 s even when 100% of logs was missing. The mining procedure can also be applied to trains operating under special strategies such as short-turning and skip-stop. The recovered log seems precise enough for the current reliability analysis performed by the city of Seoul.     

道路桥梁检测数据结合BIM技术的应用研究

随着市政道路桥梁检测要求的不断提高,检测数据分析和应用深度也随之提高,如何让检测数据更加有效、更加生动的反应道路桥梁的技术状况,成为了越来越多使用单位的基本诉求.针对这一现象,论文介绍了道路桥梁检测数据与BIM技术结合的基本路径,分析阐述了道路桥梁检测数据的主要内容及应用方面的不足,重点研究了如何以BIM信息模型作为检测数据载体的实施方法,归纳了哪些检测数据更加迫切的需要导入到BIM模型中,并通过分析各方单位的不同需求,对使用BIM模型中检测数据的应用场景提出了实施方案,为BIM信息模型的建设和应用提供了新的思路.     

驾驶辅助技术在事故预防中的应用效果

越来越多的车型都搭载了驾驶辅助系统,部分驾驶人对这类系统的实际效果持怀疑态度。本文通过调研国内外相关研究,对几类主流驾驶辅助系统的事故预防效果进行综述。与相同条件下未安装驾驶辅助系统的车辆对比,研究人员发现多数驾驶辅助系统在事故预防方面发挥了明显的作用,降低了相关的事故率、保险索赔率,其中前向和后向的自动紧急制动系统效果最为显著。此外,驾驶人对驾驶辅助系统的接受度以及响应度均会影响事故预防效果。     

以数据为中心的舰载分布式系统

在分析传统的以处理为中心的舰载分布式系统存在的诸多瓶颈的基础上,研究了以数据为中心的设计方法。采用实时发布-订阅协议的数据分发服务,提供了1种以数据为中心的通信规范,定义了1个可扩展、与平台无关、与位置无关的基础服务模型。该模型在空间、时间、传输、功能方面都是松散耦合的。实时数据发布者和订阅者,通过设置和调整服务质量参数,可以实现不同的数据需求和传输方式。系统内的各种数据能轻松发送和接收,有利于系统灵活动态的扩展和升级,易于组建更大范围的应用系统。     

语料记忆在英语教学中的价值辩护与实施策略

外语学习离不开记忆，记忆的形式有多种多样。本文从多角度为语料记忆作价值辩护，阐述它的实施策略及要注意的问题，旨在说明语料记忆是在外语学习环境下提高英语学习效率的重要途径。     

Altimeter assimilation in the OCCAM global model: Part I: A twin experiment

An altimeter data assimilation scheme has been tested in the OCCAM (Ocean Circulation and Climate Advanced Modelling) global 1/4°, 36-level model using a twin experiment format. The Cooper and Haines displacement scheme is used. The method works well in most regions and depths. Currents and densities in the top 1000 m generally improve by over 50–70% after 5 months of sea level assimilation every 15 days. Below 1000 m, an error reduction of up to 50% is achieved. The errors remain low during a further 60-day run without assimilation. Diagnostics for the North Atlantic, the Tropical Pacific and the Antarctic Circumpolar Current are shown alongside the global averages.The main problems encountered were in weakly stratified regions of the Antarctic and Arctic seas. A scale selective filter is developed to avoid assimilating scales much larger than the local deformation radius, and this avoids the adverse assimilation effects in the southern oceans. A companion paper uses this scheme to assimilate TOPEX and ERS-1 altimeter maps.     

TMIS应用数据转换工具的设计

根据ＴＭＩＳ的需求，给出了应用数据转换工具系统的总体结构，并在应用环境中实现了该系统。本数据转换系统具有较好的通用性，实时性，可用性，完全可满足ＴＭＩＳ应用的要求。     

Shockwave-based queue estimation approach for undersaturated and oversaturated signalized intersections using multi-source detection data

With the progress of information and sensing technologies, estimating vehicular queue length at signalized intersections becomes feasible and has attracted considerable attention. The existing studies provided a solid theoretical foundation for the estimation; however, the studies have some restrictions or limitations more or less. This paper presents a new methodology for estimating vehicular queue length at signalized intersections using multi-source detection data under both undersaturated and oversaturated conditions. The methodology applies the shockwave theory to model queue dynamics. Using data from probe vehicles and point detectors, analytical formulations for calculating the maximum and minimum (residual) queue lengths of each cycle are developed. Ground truth data were collected from numerical experiments conducted at two intersections in Shanghai, China, to verify the proposed methodology. It is found that the methodology has mean absolute percentage errors of 17.09% and 12.28%, respectively, for maximum queue length estimation in two tests, which are reasonably effective. However, the methodology is unsatisfactory in estimating the residual queue length. Other limitations of the proposed models and algorithms are also discussed in the paper.     

对舰载信息系统数据融合攻击分析

针对对舰载信息系统的攻击，分析了系统的易损性，提出对数据融合系统的攻击类型，给出了攻击矩阵，并对攻击效果作了简单定性分析。