横摆率跟踪控制的4WS汽车闭环操纵稳定性

针对主动后轮转向的4WS汽车，提出了横摆率跟踪控制方案，设计了H∞优化控制器，进行了4WS汽车在不同路面条件下的人一车闭环操纵稳定性仿真。结果表明，4WS汽车保持车辆侧滑角和横摆率稳态增益不变，降低它们的超调量、反应时间以及谐振峰值，增大系统的阻尼比，4WS汽车能够快速响应驾驶员的操纵指令，并对路面附着系数具有良好的鲁棒性。     

基于时序-神经网络的车辆变速器齿轮故障诊断

采用时序分析和BP神经网络，建立了基于时序-神经网络的车辆变速器齿轮故障诊断系统。通过对车辆变速器齿轮运行状念特征信号进行时序分析和特征向量提取，并以此作为BP神经网络的输入向量进行网络训练，从而实现变速器齿轮运行状态的识别与故障诊断。该系统应用于LC5T81变速器齿轮的故障诊断中，能够比较准确地识别与诊断出变速器齿轮的跑合运行状态、磨损运行状态和故障运行状态。验证表明该诊断系统有效、可行。     

季节冻土区挡土墙抗冻结构设计方法

根据我国近年来挡土墙抗冻技术研究成果 ,介绍了季节冻土区挡土墙抗冻结构设计的要求、荷载组合、冻胀力的设计取值及墙体强度和稳定的验算方法     

共同配送对城市货运交通系统的效益研究

从城市GDP、交通基础设施、货运量、货运车辆调度、能源和环境6 个要素出发,构建城市货运交通系统动力学模型,对比实施共同配送前后对城市货运交通拥堵、运输费用及货运系统节能减排的效益变化,寻求解决路径. 以济南市为例,确定模型参数及方程,动态仿真实施共同配送的效益. 结果表明：随着载货率提高、平均运距的增长,实施共同配送的边际收益呈递减趋势,配送时间的昼夜错峰调整也有显著影响;在决策区间载货率[70%, 80%]、平均运距[70, 80] km、配送时间昼夜比例[6∶4,7∶3]下,共同配送总体效益最优;节能减排效益仍存在挖掘的潜力,通过提高新能源货运车承担的货运比重,每20%的增加将带来货运系统节能减排平均30%的降低.     

基于分子动力学的车辆换道交互行为特性及其模型

传统换道模型中,把前后临界空隙作为参数固定数值,忽视了车辆和车道间的动态交互作用等因素.从分子动力学角度,系统考虑跟驰需求安全特性,从动态的需求安全距离角度研究车辆在“跟驰—换道—跟驰”过程中的行驶状态转换.确保在换道完成时,换道车辆和目标车道后车能以需求安全距离进行跟车行驶,建立了模拟分子动力学的期望安全间距模型,并对模型进行了仿真分析.结果表明,分子动力学特性模型可以把跟驰行为和换道行为很好地结合起来.研究成果为分析车辆运行交互特性,车辆可变限速技术,自适应巡航控制技术等提供理论依据和技术支撑.     

基于HLA的分布式三维视景列车运行模拟器的设计与实现

介绍一种基于HLA的三维视景列车运行模拟器.该模拟器可以接入CTCS3列控系统仿真测试平台,以三维视景输出方式,模拟列车运行.同时,模拟器之间可以通过RTI互相连接,仿真多辆列车运行.对该模拟嚣的软件框架设计和各个功能模块实现进行简要介绍.     

基于MVB总线的机车安全监测系统设计

机车控制网络通过MVB总线实现机车内部各节点之间的数据交换,从而实现对机车安全高效的控制.在介绍一种基于MVB总线的机车网络的基础上,提出一种新的安全监测单元的设计构思,并阐述其中部分的设计实现.     

Optimal timetable development for community shuttle network with metro stations

This paper investigates an issue for optimizing synchronized timetable for community shuttles linked with metro service. Considering a passenger arrival distribution, the problem is formulated to optimize timetables for multiple community shuttle routes, with the objective of minimizing passenger’s schedule delay cost and transfer cost. Two constraints, i.e., vehicle capacity and fleet size, are modeled in this paper. The first constraint is treated as soft, and the latter one is handled by a proposed timetable generating method. Two algorithms are employed to solve the problem, i.e., a genetic algorithm (GA) and a Frank–Wolfe algorithm combined with a heuristic algorithm of shifting departure times (FW-SDT). FW-SDT is an algorithm specially designed for this problem. The simulated and real-life examples confirm the feasibility of the two algorithms, and demonstrate that FW-SDT outperforms GA in both accuracy and effectiveness.     

Agent-based network transmission model using the properties of macroscopic fundamental diagram

The field of research that has recently come to the fore is the perimeter control, which aims to control traffic demand for a large urban area prior to controlling internal flow inside the area. Such control concept needs to be tested by simulations, hence, it is necessary to develop a model that can appropriately estimate the network-wide flow dynamics. In this paper, agent-based network transmission model (ANTM) is proposed for describing the aggregated flow dynamics over an urban area of multiple large-scale networks. The proposed model is the combination of the cell transmission model (CTM), macroscopic fundamental diagram (MFD), and agent concept. The CTM-based simulation is adopted for the simplicity considering the computation requirements for real-time feasibility. The MFD concept is applied for representing the network properties, and a new approach is taken particularly for estimating network outflow affected by both demand patterns and boundary capacity. The agent concept is applied for representing drivers’ travel behaviors. The model is compared with microscopic simulations and shows reasonable accuracy for large areas. In addition, various travel direction choice behaviors are applicable to this model. Various perimeter control policies are applicable as well, thus, the proposed model can be a useful tool for testing various control methods, in terms of reducing the congestion in urban areas.     

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.