基于数据驱动的隧道地质信息精细化管理系统研发与应用

针对现阶段隧道超前地质预报系统管理粗放、数据利用率低等问题，结合数据驱动原理重塑业务逻辑，研发新的隧道地质信息系统TGIS。通过高度集成隧道勘察设计阶段地质、施工阶段超前预报地质及掌子面开挖揭示地质，实现基于数据驱动的预报方法优选、临近预报提醒、分级实时预警与有效处置、掌子面自动素描、预报成果辅助判识等功能，达到隧道地质信息精细化管理的目的。经现场使用表明： 基于数据驱动的隧道地质信息精细化管理系统界面友好、运行稳定、效果良好，各项功能达到设计要求，相对于传统超前地质预报系统具有明显的优势，可普遍服务于隧道建设。     

用于智能驾驶系统评价的乘员损伤模型

只有较少的交通事故数据资源被用于建立基于碰撞速度信息的乘员损伤模型，致使所得到的模型精度差。为此，提出了基于车辆变形深度的乘员损伤模型。对美国不同制造年代和车辆级别的事故数据进行聚类分析，论证出车辆变形深度与乘员损伤风险具有相关性。以车辆变形深度为自变量，通过回归分析得到乘员损伤模型。不同种类车辆的乘员损伤模型拟合精度R²约为0.9，证明了该模型的正确性。为进一步验证，以此模型为基础，评价智能驾驶系统的有效性。以自动紧急制动系统为例，对比基于变形深度和速度变化量信息2种方法的有效性计算结果。结果表明：2组结果的平均误差不超过1%，验证了基于变形深度的乘员损伤模型的准确性。该模型仅需要事故数据库中准确的变形深度信息，能够获得更多的事故数据支持，从而可以更好地适应于不同类别智能驾驶系统的评价需求。     

Lights,camera, legal action! The effectiveness of red light cameras on collisions in Los Angeles

This study estimates the effect of red light cameras (henceforth cameras) on collisions under the Los Angeles Automated Photo Enforcement Program that ran from 2006 to 2011. To control for selection bias and unobservables, a data set is constructed such that intersections with cameras are compared to control groups of nearby intersections without cameras, matched on observable characteristics. To capture potential spillover effects of cameras, control groups at various distances from the intersections with cameras are considered. A Poisson panel data model with random coefficients is applied to these data and estimated using Bayesian methods. The program suffered from weaknesses in enforcement. The city’s courts did not uphold citations and this dampened the effect cameras had on drivers. These problems are accounted for in modeling. Controlling for these concerns, results indicate that the cameras decreased red light running related collisions, but increased right-angle and injury collisions, as well as collisions overall.     

船用电子设备现场可靠性维修性数据分析的需求与方法

从分析船用电子设备现场可靠性数据收集和分析处理工作的概念、方法和目的入手，明确了现场数据收集的主要内容，从定性和定量的角度，阐述了现场数据分析处理和利用的主要方法和步骤，并对部分维修决策参数的计算方法进行了探讨。     

模块化数据采集和显示报警系统

用PLC、单片微机和IPC组成的模块化数据采集和显示报警系统，将现代自动化控制的主要技术优化组合起来，具有开发周期短、可靠性高、功能强、成本低，故障修复快，可扩展性好等特点，虹桥机场油库的数据采集和显示报警系统就是这类系统的一个实例。     

一种高效的Ad Hoc网络链路层数据级联方法

在Ad Hoc网络条件下,受接入层效率及高误码率等客观因素影响,链路层传输性能较低。针对这一问题,提出了一种高效的链路层数据级联方法,使发端的数据既能更好地匹配接入层协议,得到较高的吞吐量,又能减少高误码率对传输性能的影响。理论及仿真结果表明：与传统的数据级联方法相比,该方法可获得更高的链路层吞吐量,提升了Ad Hoc网络链路层的传输性能。     

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.