无人水下航行器实时系统设计

针对集中式体系结构已不能满足无人水下航行器(UUV)越来越复杂的水下任务,设计并实现了UUV实时系统,该实时系统以分布式结构为基础,在硬件上采用CAN总线技术,减少了通信时间,提高了实时性,简化了系统,并提供了良好的扩展性;在软件设计上采用μC/OS实时操作系统,方便地实现了逻辑复杂的程序,并进一步满足了系统的实时性.仿真和实时性测试试验结果表明,该系统软、硬件设计满足程序运算、数据存储和实时性的要求,并具有较高的可靠性.     

中子倍增公式在连续提棒事故瞬态特性模拟中的应用

通过中子倍增公式建立模型,分别计算船用核动力反应堆在低功率和高功率运行时发生连续提棒事故后的各主要参数的变化,并将计算结果与三维实时仿真软件计算结果进行比较。结果表明,计算结果与三维实时仿真软件计算结果比较符合,可为事故的处置提供依据。     

交通调查者行为效应分析

交通调查者的行为对获取真实、有效的交通运输系统原始数据起着关键性作用.通过对调查者行为的影响因素进行分析,依据行为学理论解释交通调查者的行为机理,并提出交通调查者的行为效应模型,具有十分重要的意义.     

车辆离散特性研究及仿真

针对目前有关分车道行驶的方案,对不同车辆行驶速度特征进行的研究,以及对不分车道行驶和分车道行驶两种情况下的车辆运行状况进行的有效仿真,可为当前公交优先通行提供较好的理论基础.     

Excel在利用公交IC卡数据构造换乘矩阵中的应用

公交IC卡的应用,不仅方便了乘客出行,也为城市交通规划相关数据的采集提供了一种新的调查统计手段.针对当前城市公交IC卡在换乘矩阵构造过程中存在的信息量大、处理困难的问题,利用Excel软件强大的数据处理功能,对大量的IC卡信息进行了有效地处理,成功地构造了城市公交换乘矩阵,使其成为公交换乘枢纽规划的一个重要参考依据.     

Within day rescheduling microsimulation combined with macrosimulated traffic

The concept of rescheduling is essential to activity-based modeling in order to calculate effects of both unexpected incidents and adaptation of individuals to traffic demand management measures. When collaboration between individuals is involved or timetable based public transportation modes are chosen, rescheduling becomes complex. This paper describes a new framework to investigate algorithms for rescheduling at a large scale. The framework allows to explicitly model the information flow between traffic information services and travelers. It combines macroscopic traffic assignment with microscopic simulation of agents adapting their schedules. Perception filtering is introduced to allow for traveler specific interpretation of perceived macroscopic data and for information going unnoticed; perception filters feed person specific short term predictions about the environment required for schedule adaptation. Individuals are assumed to maximize schedule utility. Initial agendas are created by the FEATHERS activity-based schedule generator for mutually independent individuals using an undisturbed loaded transportation network. The new framework allows both actor behavior and external phenomena to influence the transportation network state; individuals interpret the state changes via perception filtering and start adapting their schedules, again affecting the network via updated traffic demand. The first rescheduling mechanism that has been investigated uses marginal utility that monotonically decreases with activity duration and a monotonically converging relaxation algorithm to efficiently determine the new activity timing. The current framework implementation is aimed to support re-timing, re-location and activity re-sequencing; re-routing at the level of the individual however, requires microscopic travel simulation.     

Modeling the behavioral determinants of travel behavior: An application of latent transition analysis

This paper applies the relatively new method of latent class transition analysis to explore the notion that qualitative differences in travel behavior patterns are substantively meaningful and therefore relevant from explanatory point of view. For example, because the bicycle may function as an important access and egress mode, a car user who also (occasionally) uses the bicycle may be more likely to switch to a public transit profile than someone who only uses the car. Data from the Dutch mobility panel are used to inductively reveal travel behavior patterns and model transitions in these patterns over time. Additionally, the effects of seven exogenous variables, including two important life events (i.e. moving house and changing jobs), on cluster membership and the transition probabilities are assessed. The results show that multiple-mode users compared to single-mode users are more likely to switch from one behavioral profile to another. In addition, age, the residential environment, moving house and changing jobs have strong influences on the transition probabilities between the revealed behavioral patterns over time.     

Using automated walking gait analysis for the identification of pedestrian attributes

Collecting microscopic pedestrian behavior and characteristics data is important for optimizing the design of pedestrian facilities for safety, efficiency, and comfortability. This paper provides a framework for the automated classification of pedestrian attributes such as age and gender based on information extracted from their walking gait behavior. The framework extends earlier work on the automated analysis of gait parameters to include analysis of the gait acceleration data which can enable the quantification of the variability, rhythmic pattern and stability of pedestrian’s gait. In this framework, computer vision techniques are used for the automatic detection and tracking of pedestrians in an open environment resulting in pedestrian trajectories and the speed and acceleration dynamic profiles. A collection of gait features are then derived from those dynamic profiles and used for the classification of pedestrian attributes. The gait features include conventional gait parameters such as gait length and frequency and dynamic parameters related to gait variations and stability measures. Two different techniques are used for the classification: a supervised k-Nearest Neighbors (k-NN) algorithm and a newly developed semi-supervised spectral clustering. The classification framework is demonstrated with two case studies from Vancouver, British Columbia and Oakland, California. The results show the superiority of features sets including gait variations and stability measures over features relying only on conventional gait parameters. For gender, correct classification rates (CCR) of 80% and 94% were achieved for the Vancouver and Oakland case studies, respectively. The classification accuracy for gender was higher in the Oakland case which only considered pedestrians walking alone. Pedestrian age classification resulted in a CCR of 90% for the Oakland case study.     

Incorporating human-factors in car-following models: A review of recent developments and research needs

Over the past decades there has been a considerable development in the modeling of car-following (CF) behavior as a result of research undertaken by both traffic engineers and traffic psychologists. While traffic engineers seek to understand the behavior of a traffic stream, traffic psychologists seek to describe the human abilities and errors involved in the driving process. This paper provides a comprehensive review of these two research streams.It is necessary to consider human-factors in CF modeling for a more realistic representation of CF behavior in complex driving situations (for example, in traffic breakdowns, crash-prone situations, and adverse weather conditions) to improve traffic safety and to better understand widely-reported puzzling traffic flow phenomena, such as capacity drop, stop-and-go oscillations, and traffic hysteresis. While there are some excellent reviews of CF models available in the literature, none of these specifically focuses on the human factors in these models.This paper addresses this gap by reviewing the available literature with a specific focus on the latest advances in car-following models from both the engineering and human behavior points of view. In so doing, it analyses the benefits and limitations of various models and highlights future research needs in the area.     

无线技术在汽车电控系统中的应用

为了解决传统汽车中组合开关线束复杂、价格昂贵的问题,设计并搭建了一个基于无线局域网络的电气机构控制系统。选用车载电脑为人车交互窗口来完成人车对话,以新力维无线模块WM001S为载体实现无线控制信号与汽车CAN总线的连接,这样就实现了控制开关与汽车ECU的通信。再通过ECU来实现对电气机构的控制,从而达到了借助无线网络来控制汽车电气机构的目的。实验结果证明,该系统最大响应时间为200ms、操作简单、工作稳定可靠、大大降低了线束的复杂程度以及布置线束的空间要求。