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传统的四步骤模型中的出行生成模型是预测研究范围内每个交通小区的出行发生次数和出行吸引次数，直接将居民的出行次数简单的按交通小区进行集计处理，并没有充分考虑每个人的出行次数，难以反映个人和家庭的社会经济等因素对居民个人出行次数的影响。本文根据2003年吉林省长春市居民出行调查数据，利用非集计模型建立居民个人的出行次数选择模型，并应用相关统计软件对模型进行标定，进而分析居民个人的出行次数，从而求得居民个人出行次数的期望值，初步尝试探索非集计模型和四步骤模型的综合应用问题。     

Floating car based travel times for city logistics

City logistics routing requires time-dependent travel times for each network link. We rely on the concept of Floating Car Data (FCD) to develop and provide such travel times. Different levels of aggregation in the determination of time-dependent travel times from a database of historical FCD are presented and evaluated with regard to routing quality. Furthermore, a Data Mining approach is introduced, allowing for a substantial reduction of the volume of input data required for city logistics routing. The different approaches are investigated and evaluated by a huge amount of FCD collected for the urban area of Stuttgart, Germany. The results show that the Data Mining approach enables efficient provision of time-dependent travel times without a significant loss of routing quality for city logistics applications.     

一种基于DoDAF的指挥控制系统原型迭代设计方法

对目前指挥控制系统设计的现状进行分析,针对体系结构设计不规范难以互联互通互操作、系统设计初期难以直观展现系统原型和迭代优化的问题,提出了一种基于Do DAF的指控系统原型迭代设计方法,并完成了该设计方法在设计工具上的软件实现,验证了该方法的可行性,实现了指控系统的原型迭代设计。     

RS乘积码译码算法研究

文章介绍了一种基于梯度算法的Turbo乘积码（以RS码作为分量码）的译码算法。该算法以迭代Chase-Ⅱ算法为基础,利用该算法译码得到的每行（或列）最优判决码D（m-1）来代替竞争码字C,节省了寻找C的过程,从而简化了外信息和软输出的计算。文章以RS（15,11）码为分量码进行了matlab仿真,仿真结果表明：该算法能在基本保持turbo乘积码和Chase算法译码性能基础上,提高译码速度,降低译码复杂度。     

城乡公交车违规停车的危害性探讨

城乡公交车站点长时间停车等客,站外随意停车等违规现象十分普遍,文章针对这一情况,通过交通事故案例分析,找出城乡公交车违规停车现象存在的原因,指出了违规停车的危害性并提出解决方法.     

一维波动方程反问题的一种迭代解法及其敛速估计

对地震勘探中的一维波动方程反问题，给出了一种迭代解法，并证明了该算法的收敛性。     

公交乘客出行路径优化模型

分别以“换乘次数”最少和“出行时间”最短为优化目标，提出2个公交乘客出行路径优化模型，得到不同的最优线路，供公交乘客选择适合自身的出行路线，并以1个简单的公交网络对模型进行了验算。     

基于统一过程原则的软件自动化测试研究

软件自动化测试以较高的效率,广泛的覆盖率,成为测试行业的主流.介绍统一过程(RUP)和自动化测试及手工测试,总结目前自动化测试过程中存在的主要问题.重点分析在RUP原则的指导下,如何解决这些问题,实现测试自动化,从而高效率地进行软件测试,促进软件自动化测试的发展.     

Evaluating the added-value of online bus arrival prediction schemes

Online predictions of bus arrival times have the potential to reduce the uncertainty associated with bus operations. By better anticipating future conditions, online predictions can reduce perceived and actual passenger travel times as well as facilitate more proactive decision making by service providers. Even though considerable research efforts were devoted to the development of computationally expensive bus arrival prediction schemes, real-world real-time information (RTI) systems are typically based on very simple prediction rules. This paper narrows down the gap between the state-of-the-art and the state-of-the-practice in generating RTI for public transport systems by evaluating the added-value of schemes that integrate instantaneous data and dwell time predictions. The evaluation considers static information and a commonly deployed scheme as a benchmark. The RTI generation algorithms were applied and analyzed for a trunk bus network in Stockholm, Sweden. The schemes are assessed and compared based on their accuracy, reliability, robustness and potential waiting time savings. The impact of RTI on passengers waiting times are compared with those attained by service frequency and regularity improvements. A method which incorporates information on downstream travel conditions outperforms the commonly deployed scheme, leading to a 25% reduction in the mean absolute error. Furthermore, the incorporation of instantaneous travel times improves the prediction accuracy and reliability, and contributes to more robust predictions. The potential waiting time gains associated with the prediction scheme are equivalent to the gains expected when introducing a 60% increase in service frequency, and are not attainable by service regularity improvements.     

A reinforcement learning framework for the adaptive routing problem in stochastic time-dependent network

Most previous work in addressing the adaptive routing problem in stochastic and time-dependent (STD) network has been focusing on developing parametric models to reflect the network dynamics and designing efficient algorithms to solve these models. However, strong assumptions need to be made in the models and some algorithms also suffer from the curse of dimensionality. In this paper, we examine the application of Reinforcement Learning as a non-parametric model-free method to solve the problem. Both the online Q learning method for discrete state space and the offline fitted Q iteration algorithm for continuous state space are discussed. With a small case study on a mid-sized network, we demonstrate the significant advantages of using Reinforcement Learning to solve for the optimal routing policy over traditional stochastic dynamic programming method. And the fitted Q iteration algorithm combined with tree-based function approximation is shown to outperform other methods especially during peak demand periods.