Road traffic conditions in Kenya: Exploring the policies and traffic cultures from unstructured user-generated data using NLP

Road traffic accidents (RTA) are a prevalent cause of fatality with African countries having the highest fatality index (25–34 per quota). The World Health Organization estimates Kenya's fatality rate due to RTA at 28 per quota. From literature, the country's fatality and injuries have increased by 26% and 46.5%, respectively, since the year 2015. The country is faced with incomplete RTA data capturing, hindering effective planning and policy adjustments to curb the menace. In this paper, we scrapped user-generated data (Twitter) and national transport and safety authority's (NTSA) reports to shed light on traffic safety, practices, and cultures in the country. To this end, we gathered 1,000,000 tweets and 8000 speeding entries between 2015 and 2021 and performed natural language processing (NLP) and quantitative study of the data. We applied NLP and n-gram search of keywords to categorize data into 8 topics: traffic, public service vehicle (PSVs), policing, accident, infrastructure, recklessness, robbery, and corruption. From the data, policing, which touches on all police and law-enforcement-related activity was found to be highly correlated with PSVs, recklessness, accidents, traffic congestion, robbery, infrastructure, and corruption with indices of r(76) = 0.92, 0. 91, 0.87, 0.82, 0.81, 0.76, and 0.70, respectively with p < 0.001. The topic modeling confirmed the identified topics to be the latent discussion issues affecting the public. From the study, PSVs, policing and traffic flow were isolated as key issues that ought to be addressed immediately. The research recommended the integration of driver monitoring systems to strengthen policing. The research, which utilized unstructured data, points to the utility of data mining which would greatly benefit traffic research, particularly African-based studies, that suffer from data inadequacy.     

Study on the framework of hybrid collision warning system using loop detectors and vehicle information

Safety warning systems generally operate based on information from sensors attached to individual vehicles. Various types of data used for collision risk calculation can be categorized into two types, microscopic or macroscopic, depending on how the sensors collect the information of traffic state. Most collision warning systems use only either of these types of data, but they all have limitations imposed by the data, such as requirement of high installation cost and high market penetration rate of devices. In order to overcome these limits, we propose a collision warning system that utilizes the integrated information of macroscopic data and microscopic data, from loop detectors and smartphones respectively. The proposed system is evaluated by simulating a real vehicle trip based on the NGSIM data. We compare the results against collision warning systems based on macroscopic data from infrastructure and microscopic data from Vehicle-to-Vehicle information. The analysis of three systems shows two findings that (a) ICWS (Infrastructure-based Collision Warning System) is inadequate for immediate collision warning system and (b) VCWS (V2V communication based Collision Warning System) and HCWS (Hybrid Collision Warning System) produce collision warning at very similar timing, even with different behavior of individual drivers. Advantages of HCWS are that it can be directly applied to existing system with small additional cost, because data of loop detector are already available to be used in Korea and smartphones are widely spread. Also, the computation power distributed to each individual smartphone greatly increases the efficiency of the system by distributing the computation resources and load.     

项目成熟度模型在高职教学管理中的应用研究

文章对高职院校项目成熟度模型进行了应用研究,提出了适合我国教育特点的具体的方法和步骤,为我国教学管理工作的改革与发展提供了参考。     

Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches

It is known that adverse weather conditions can affect driver performance due to reduction in visibility and slippery surface conditions. Lane keeping is one of the main factors that might be affected by weather conditions. Most of the previous studies on lane keeping have investigated driver lane-keeping performance from driver inattention perspective. In addition, the majority of previous lane-keeping studies have been conducted in controlled environments such as driving simulators. Therefore, there is a lack of studies that investigate driver lane-keeping ability considering adverse weather conditions in naturalistic settings. In this study, the relationship between weather conditions and driver lane-keeping performance was investigated using the SHRP2 naturalistic driving data for 141 drivers between 19 and 89 years of age. Moreover, a threshold was introduced to differentiate lane keeping and lane changing in naturalistic driving data. Two lane-keeping models were developed using the logistic regression and multivariate adaptive regression splines (MARS) to better understand factors affecting driver lane-keeping ability considering adverse weather conditions. The results revealed that heavy rain can significantly increase the standard deviation of lane position (SDLP), which is a very widely used method for analyzing lane-keeping ability. It was also found that traffic conditions, driver age and experience, and posted speed limits have significant effects on driver lane-keeping ability. An interesting finding of this study is that drivers have a better lane-keeping ability in roadways with higher posted speed limits. The results from this study might provide better insights into understanding the complex effect of adverse weather conditions on driver behavior.     

三维“大场景”遥感技术在西康高铁地质勘察中的应用

包家山越岭段地形地质条件复杂、岩石软硬不均、不良地质和断裂构造发育,为西康高铁地质选线带来较大的困难。为提高勘察效率和质量,根据区域地质资料、三维大场景遥感影像特征及野外调查,建立工作区地质构造解译标志,对其地质构造、地层岩性及不良地质体进行遥感判释。综合三维大场景立体遥感技术和野外调查手段,建立岩类基本的遥感影像解译标志,以便对岩性进行综合判释;调查验证测区的地质构造及不良地质体的分布状态及规模,分析滑坡等不良地质现象的分布、规模及其特征。取得了较高精度的遥感地质解译信息,判释准确率较高,为铁路选线方案提供重要依据。     

一种铁路信号安全控制平台的研究

以铁路信号控制系统中核心的安全控制平台为研究对象,立足于铁路信号计算机安全控制平台的功能及安全需求,借鉴融合失效-安全原则、冗余控制原理、计算机通信总线及编程技术,从总体设计原则、软/硬件结构设计及实现等方面研究设计一种铁路信号安全控制平台。信号安全控制平台测试、验证和确认结果表明,信号安全控制平台适应并满足新时期各种铁路信号安全控制系统兼容性、安全完整性要求,研究设计的信号安全控制平台能有效解决适配各种信号安全控制系统的通用性、平台软件与应用软件分离后的移植性等关键技术问题。     

公路施工项目“质量创优”活动与OPM3

通过对OPM3的关键路径实践与质量创优活动的过程进行对比,指出质量创优活动的不足之处在于其质量管理活动没有能够与企业的战略目标相联系。     

基于规范载荷的舰船设计海况等级确定方法

针对军用规范载荷难以给出舰船设计海况等级的问题,本文通过三维时域非线性水弹性理论分析了舰船在典型航速、不同海况下的波浪载荷极值与规范载荷之间的对应关系,进而提出了一种明确舰船设计海况等级的方法。以某型舰船为例,基于该方法确定了该型舰船的设计海况等级。研究成果表明：该方法可有效明确舰船规范载荷对应下的设计海况等级,在一定程度上为舰船的安全使用提供参考。     

基于ADS1202单数据链路的变流器电气量检测

针对多相大功率变流器需要采集并处理的温度、电流及电压等电气量数量多、空间分布分散的特点,本文设计了一种基于ADS1202的单数据链路的电气量检测方法。采用曼彻斯特码完成单数据链路信号传输,通过FPGA编程实现曼彻斯特码自同步解码,并使用Sink3数字滤波器对电气量进行处理。实验验证了信号调理、转换和后处理的有效性和可靠性,并实际应用于多相大功率变流器装置中电气量的分布检测。     

护岸工程基坑边坡稳定性计算传统方法与数值模拟法的对比

针对航道整治工程中护岸结构的基坑边坡易发生的滑坡坍塌问题,以浙江北部某航线的航道整治工程为例,对该工程的边坡稳定性问题采用传统极限平衡理论的条分法和数值模拟的FLAC3D方法进行对比分析。相比条分法,FLAC3D法分析条件更加完善,在理论上更加可靠,也能对边坡详细区域进行分析,以便进行针对性预防。而条分法计算理论理想化,计算结果偏保守。