A framework to analyze the vulnerability of European road networks due to Sea-Level Rise (SLR) and sea storm surges

This study proposes a framework to explore the concepts of exposure, vulnerability and connectivity in EU road network and to assess the potential transportation infrastructure sensitivities towards Sea-Level Rise (SLR) and storm surges. The magnitude and significance of impacts were determined and knowledge of network robustness was built up based on existing climate data and on future trends. Various spatial databases were integrated and a four-stage transport model was used to explore the likely impacts of network degradation. The pattern of the network was assessed via both node- and link-based measurements, where different road databases, namely TRANS-TOOLS and Tele Atlas/TomTom, were employed in order to analyze the impact of spatial resolution within network connectivity analyses. This general framework developed for European Union, was tested on a specific and articulated case study area; namely, the north-east coastal region of Spain. The research conducted, yielded useful methods for the analysis of network vulnerability, where impacts are more significant in regional accessibility patterns. Accessibility indicators at the regional level changed drastically, with some regions showing up to a 26% decrease. According to the results of network connectivity indicators, the changes in network topology have reduced the number of alternative routes and placed more pressure on the transport system. The implementation of this framework and quantitative assessment methodologies outlined in this paper could be employed to assist policy makers to recognize the opportunities that may arise or diminish the adverse effects.     

某钢管混凝土拱桥加固与分析

结合工程实例,分析了某钢管混凝土拱桥加固改造后的受力状态。分析表明,该桥经加固后,受力状态虽满足规范要求,但是安全储备富余不足,应引起重视。同时,在静力分析的基础上,还分析了该桥梁的稳定性和动力特性。分析结果表明,桥梁的整体稳定性较好。     

界面污染对沥青小梁疲劳性能的影响研究

为研究层间界面的污染情况对沥青铺面整体使用性能所产生的影响，采用由不同类型混合料复合而成的、含有层间界面的复合沥青小梁，进行抗剪强度试验和四点弯曲疲劳试验，以机油污染和黄土污染作为污染源，调整污染量，用于模拟施工过程中可能存在的污染情况，并对其在不同工况下的层间抗剪强度及整体疲劳寿命进行测定。试验结果表明：（1）就各类影响层间抗剪强度因素的敏感性而言，机油污染的敏感性要高于黄土污染；（2）同等试验条件下，随着污染量的增加，复合小梁的疲劳寿命显著减小，一定程度后逐渐趋于稳定；（3）在相同污染量工况下，机油对于层间粘结性能的影响要大于黄土污染对其产生的影响；（4）就弯曲劲度模量损失量而言，当污染量达到一定程度后，复合小梁的层间粘结性就会丧失。     

考虑能源类型的城市出租车运营效率评价研究

以分析西安市燃气型、电动型及甲醇型3种能源类型出租车在运营中的既有期望运营投入和非期望运营投入，既有期望产出和非期望产出为基础，本文构建基于DEA-Malmquist的考虑能源类型的出租车运营效率评价模型。从西安市出租车管理处提取西安市 2020 年 9~12 月期间 14946辆出租车的GPS轨迹数据，并随机选取1000辆甲醇型出租车、1000 辆电动型出租车及 1000辆燃气型出租车作为样本进行实例分析。通过处理3000辆出租车GPS数据，得到乘客在工作日和双休日不同时段的出行需求和特征，分析3种不同能源类型出租车的运营效率差异。所得结论可为西安政府部门对不同能源类型出租车的管理运营提供决策支持。     

Analysis of yellow-light running at signalized intersections using high-resolution traffic data

Many accidents occurring at signalized intersections are closely related to drivers’ decisions of running through intersections during yellow light, i.e., yellow-light running (YLR). Therefore it is important to understand the relationships between YLR and the factors which contribute to drivers’ decision of YLR. This requires collecting a large amount of YLR cases. However, existing data collection method, which mainly relies on video cameras, has difficulties to collect a large amount of YLR data. In this research, we propose a method to study drivers’ YLR behaviors using high-resolution event-based data from signal control systems. We used 8 months’ high-resolution data collected by two stop-bar detectors at a signalized intersection located in Minnesota and identified over 30,000 YLR cases. To identify the possible reasons for drivers’ decision of YLR, this research further categorized the YLR cases into four types: “in should-go zone”, “in should-stop zone”, “in dilemma zone”, and “in optional zone” according to the driver’s location when signal turns to yellow. Statistical analysis indicates that the mean values of approaching speed and acceleration rate are significantly different for different types of YLR. We also show that there were about 10% of YLR drivers who cannot run through intersection before traffic light turns to red. Furthermore, based on a strong correlation between hourly traffic volume and number of YLR events, this research developed a regression model that can be used to predict the number of YLR events based on hourly flow rate. This research also showed that snowing weather conditions cause more YLR events.     

Transportation habits: Evidence from time diary data

The interdisciplinary Time Use Observatory workshops learned that transportation research and social sciences strive for the same multi-day time-diary data in order to make interferences about human habitual (travel) behavior. It also is learned that when it comes to the mathematics and analytics involved both disciplines are miles apart, though both with founded reasons to do so. In brief, transportation research relies on modeling to make predictions whereas social sciences apply statistics to their data to draw conclusions. In line with the interdisciplinary philosophy of the Time Use Observatory workshops, this contribution aims to communicate 30 years of experience in analyzing time-diary data. To do so, it demonstrates the latter by calculation transportation habits and aims to illustrate that multi-day time-diary data might have some additional benefits for computing temporal regularities. It shows that including a flexible notion of both regular tempo (or recurrence) of activities (e.g. every day) and regular timing of activities (e.g. always at 6 am) produces different results for different kind of transportation purposes. It also shows that these calculations using multi-day time-diary data result in an indicator at the individual level that can be analyzed in terms of socio-demographic and socio-economic characteristics. This work concludes that partitioning temporal regularities in regular reoccurrence and regular timing is a crucial element of (transportation) habits.     

基于Hopfield神经网络的雷达多目标跟踪

针对海上船舶雷达在多目标跟踪过程中实时性较差和不能快速响应的问题,提出目前密集杂波情况下多目标跟踪中最为有效的数据关联算法——联合概率数据关联(Joint Probabilistic Data Association,JPDA)算法。为解决JPDA随着目标增多的情况会出现的组合“爆炸”及计算量较大导致跟踪实时性较差的问题,从分析联合概率数据关联确认矩阵着手,依据Hopfield神经网络在解决旅行商问题(Travelling Salesman Problem,TSP)时的思路,提出基于Hopfield神经网络联合概率数据关联(Hopfield Neural Network Joint Probability Data Association,H-JPDA)来改进联合概率数据关联算法,通过简化矩阵拆分过程,显著减少计算量,提高跟踪的实时性。基于上述改进的神经网络联合概率数据关联算法,通过MATLAB对多目标跟踪进行仿真,仿真结果表明该算法能提高跟踪的实时性和快速响应能力。     

城市轨道交通站点数据链客流预测方法研究

城市轨道交通客流预测数据是城市轨道交通设计与运营的重要依据与基础,在总结分析我国城市轨道交通客流预测经验及研究成果的基础上,提出基于城市土地利用法的站点数据链客流预测方法,系统介绍该预测方法的理论体系,并以杭州地铁4号线为例,验证站点数据链客流预测方法的科学性与合理性,以期使该预测方法在客流预测与校核中具有指导意义。     

冲压车间生产管理系统分析及设计

分析了整车制造业企业冲压车间业务点及生产方式,针对生产线之间相互独立、缺乏数据传递,而导致形成信息孤岛问题,设计了基于面向制造企业车间执行层的生产信息化管理制造执行系统(MES),设计开发了MES实时数据监测系统。使用被动扫描式实现现场设备状态的实时采集。用户可以通过PC浏览器远程监测现场设备的工作状态。MES实时数据监测系统开发成本低、运行稳定且维护简便,可以替代传统岗位式监测方式,具有较高的研究和市场应用价值。     

物流无人机对地风险评估方法研究

随着智能交通运输体系的完善和无人机应用场景的拓展，物流无人机应运而生。物流无人机被认为是解决快递配送“最后一公里”问题的有效运输方式，但由空中碰撞、系统失效等原因导致的无人机坠落有可能对地面人员造成伤害并产生经济损失。因此本文聚焦无人机物流运输场景，研究物流无人机坠地后造成的死亡人数和经济损失，并利用风险矩阵综合评估风险。主要内容包括：分析物流无人机空中碰撞后产生机货分离的情况，建立由物流无人机和货物坠地造成 的地面死亡人数计算模型；对无人机和货物进行分类，建立经济损失计算模型，包括由无人机和货物价值损失构成的直接经济损失，以及由社会服务机构费用消耗构成的间接经济损失；综合物 流无人机和货物坠地造成的地面死亡人数和经济损失建立风险评估矩阵。算例证明，利用本文方法得到的地面死亡人数为每飞行小时3.74×10-12~1.87×10-7人，符合等效安全水平；每次事故经济损失为6734.8~33619.0元，也在合理区间内。本文提供的方法可以对物流无人机地面风险评估和管控提供一定借鉴和指导意义。