Multi-step prediction of experienced travel times using agent-based modeling

This paper develops an agent-based modeling approach to predict multi-step ahead experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in a decision-making system. Each expert predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions is developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents associated with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level of output, namely the predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 are used to test different predictors. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-the-practice and state-of-the-art methods (instantaneous travel time, historical average and k-nearest neighbor), and maintains less than a 9% prediction error for trip departures up to 60 min into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, the proposed approach does not require offline training thus making it easily transferable to other locations and the fast algorithm computation allows the proposed approach to be implemented in real-time applications in Traffic Management Centers.     

Estimating travel time functions for urban roads: options and issues

Travel time functions specify the relationship between the travel time on a road and the volume of traffic on the road. Until recently, the parameters of travel time functions were rarely estimated in practice; however, a compelling case can be made for the empirical examination of these functions. This paper reviews, and qualitatively evaluates, a range of options for developing a set of travel time functions. A hierarchy of travel time functions is defined based on four levels of network detail: area, corridor, route and link. This hierarchy is illustrated by considering the development of travel time functions for Adelaide. Alternative sources of data for estimating travel time functions are identified.

In general, the costs and benefits increase as the travel time functions are estimated at finer levels of network detail. The costs of developing travel time functions include data acquisition costs and analysis costs. The benefits include the potential for reducing prediction errors, the degree of application flexibility and the policy sensitivity of the travel time functions.     

时间多项式进化算法在旅行商问题中的研究

提出了一种模拟生物遗传的进化算法，并将该算法应用于旅行商问题得到了较好的结果，根据达尔文进化论的优化过程，结合自然选择原则提出了启发式算法，该算法的时间复杂性与快速排序策略相当。在文中利用该算法求解中国旅行商问题得到目前的最佳结果。     

京唐城际铁路速度目标值选择

速度目标值是城际铁路最主要技术标准,不同的速度目标值决定了基础设施、设备及各项配套设施的选择。从京唐城际铁路功能定位出发,通过对客流特点、功能定位、时间目标值需求、经济技术比选等多方面因素的分析,综合得出京唐城际铁路速度目标值推荐采用350 km/h的方案。     

水工模型试验的流速测量方法与数据处理程序

在水工模型试验中,基础数据的准确度与精确度直接关系到科研成果的质量。流速是模型试验中观测的主要数据之一。模型流速的测量方法有许多种,如示踪物法、光电法、声学多普勒法等。光电法是目前水工模型试验中应用最多的流速测量方法。文章所述的模型测杆流速处理程序实现的功能就是计算流速测杆采集流速的平均值并直观地以流速过程线图显示。通过应用于上海国际航运中心洋山港区整体物理模型试验的实践表明,程序采用微调方法,解决了判断涨落潮时存在的问题,提高了水工模型试验工作的精度和效率,为科学决策提供了依据。     

衬砌脱空雷达波数值模拟与定量解释

地质雷达是隧道衬砌脱空病害检测主要方法,常规探测理论是以菲涅尔带半径为最终空间分辨率,小于该半径的脱空不能识别,而大于该半径的脱空在定量解释上也存在困难。针对隧道脱空检测常用900 MHz天线,采用时域有限差分对不同脱空量的模型进行雷达波数字模拟计算,并合成雷达记录剖面。在分析脱空区域顶、底反射叠加波形特征及振幅变化的基础上,给出不同脱空量模型的波形双峰极值点幅度变化规律,提出基于极值点振幅比反演脱空量的解释策略,并给出反演结果的拟合算法。该策略不仅为脱空区脱空量分析提供一条途经,同时为地质雷达在脱空定量解释方面提供有效的技术手段。     

并联混合动力汽车ECMS的时变等效因子提取算法的研究

为解决当前等效燃油消耗最小控制策略(ECMS)未能根据实际工况选取最优等效因子的问题,利用动态规划算法(DP)和ECMS各自的优点,构建并联混合动力汽车能量算法模型,即采用动态规划算法的等效燃油消耗最小控制策略(ECMSwDP),将等效因子作为全局最优算法的控制变量,通过对等效因子的离散全局优化,获得基于工况的最佳时变等效因子。在标准工况下对时变等效因子实时控制策略与全局最优控制策略DP的各项性能参数进行了数值仿真,验证了时变等效因子提取算法的有效性和等效因子初始值选取方法的可行性。     

罗兰C TOA-ASF测量系统设计与实现

附加二次相位因子（ASF）是影响罗兰C导航/授时系统精度的主要因素。进行ASF修正主要有理论预测法和实测法两种。针对罗兰C到达时间ASF实测修正的需要,本文给出了ASF测量系统的测量原理和测量系统组成,详细介绍了测量系统的设置、软硬件设计。车载试验表明,该测量系统的设计是成功的,能够满足ASF实测的需要。     

基于多目标规划的公交车调度方法研究

公交调度要考虑公司和乘客的多方利益。目标规划法是运筹学中解决多目标问题的有效方法,而优先因子的采用可以区分调度中的各方利益的主次。同一优先因子下加入了权重,可根据实际情况做出灵活有效的调度方案。     

餐饮物流配送路线的优化研究

近年来,我国的餐饮企业迅速发展壮大,随之而来的问题是如何控制物流配送成本,合理的配送规划将直接影响一个企业的成本控制、效率和竞争能力。针对餐饮业的特点提出一个规划方案:按货物品种来确定配送节点的方法,并以此为基础,分别从选择配送点和对路线变更方案进行了研究。最后的算例将提出的总成本概念应用到用最短路径方法中来求解最优路径,表明全面的成本概念在配送规划中的重要作用。