一种基于县域城乡客运公交化改造的公交时刻表编制方法

为进一步协调县域城乡客运公交化改造与公交高效化运营之间的关系,合理确定城乡客运公交化改造后的公交时刻表至关重要。以县域城乡客运公交化改造为研究对象,提出了一种适合城乡客运公交化改造的公交时刻表编制方法。通过人工调查法获取客流信息,建立客流处理模型,获得最大断面客流量等信息。在此基础上,运用客流需求法建立数学模型,求得发车时间间隔并优化。最后,通过比较验证获得最终的公交时刻表。以池河至石泉客运班线公交化改造为例,改造后的公交时刻表极大地方便了沿线居民的出行,从而证明该时刻表具有较好的可行性。     

路面平整度评价指标IRI的影响因素

研究了路面平整度标准评价指标——国际平整度指数(IRI)的计算模型和求解方法,找出了影响IRI精度的主要因素;利用数值模拟定量分析了位移传感器精度、数据采样间隔、评价距离、检测方向和检测车速等对IRI的影响。得出结论:提高位移传感器精度、减少数据采样间隔,可以较大提高IRI的测量精度;检测车速越接近理想车速,IRI测量精度越高;检测方向对IRI测量精度产生影响,但其影响较小。     

CIM模型在高速公路投资风险评估中的应用

在分析、识别高速公路投资风险的基础上,建立了高速公路投资风险评估指标体系.针对风险因素具有结构多层次、因素多方面、评估模糊性、出现随机性、直接量化困难等特点,基于模糊理论,结合层次分析法,建立了控制区间和记忆(CIM)模型对高速公路投资风险进行评估.通过控制区间和记忆模型在工程实践中的应用,验证了CIM综合评估法在风险评估中的有效性和可操作性.     

不同闭塞方式下城轨列车追踪运行过程及其仿真系统的研究

介绍了城市轨道交通不同信号闭塞方式及其追踪列车间隔时间的计算方法,建立了多列车追踪运行的仿真系统,并进行了算例的设计。在算例中,通过对两列车在固定闭塞、准移动闭塞、移动闭塞方式下的追踪运行模拟,分析了不同闭塞方式下列车追踪运行的效果,同时利用仿真系统对最小追踪列车间隔时间进行了验算,并得出相同发车间隔情况下不同信号闭塞方式的列车追踪运行结果以及三种闭塞方式条件下最小追踪列车间隔时间的计算结果。该系统可以为科研、设计人员进行城市轨道交通列车安全间隔及通过能力等方面的研究提供便利条件。     

约束于圆弧的路线平面直线段重构算法

提出一种利用一组(x,y)直角坐标点拟合与给定的圆弧相切的直线的算法。该算法遵循总体最小二乘准则来构建约束于圆弧的拟合直线的数学模型,在分析函数单调隔根区间的基础上,利用反拉格朗日插值迭代法计算出函数在定义域内的所有数值根,再经最小二乘准则检验获得数学模型的解,即拟合直线的回归参数。研究结果表明:拟合数学模型简明,基于单调隔根区间的迭代算法稳健,速度快,效果优,可用于重构路线平面的精确几何参数。     

基于驾驶员短期记忆的限速标志合理间距研究

从驾驶安全性和驾驶员短期记忆特性两个方面综合考虑高速公路限速标志的设置间距.采用室内实验的方法,对驾驶员的短期记忆衰减规律进行研究,建立驾驶员短期记忆衰减规律指数模型,并将此结果应用于高速公路限速标志的设置过程中.根据高速公路的不同限速值,定量给出高速公路限速标志的合理设置间距.     

A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.     

奥运地铁启示及奥运后地铁发展

从奥运期间的北京轨道交通得到两点启示：现有技术水平可以在保证运营安全的前提下缩短行车间隔,提高轨道交通运营效率;修建轨道交通应首先考虑其社会、经济效益。提出奥运后北京轨道交通的发展重点：缩短行车间隔,提高拥挤线路的运输能力,改善服务水平;针对城市中心线网密度低的现状,研究制定不同区域内合理的轨道交通线网密度：加强城市轨道交通线间以及与其他交通方式换乘的研究,最终实现轨道交通又好又快发展。     

Confidence intervals of willingness-to-pay for random coefficient logit models

Random coefficient logit (RCL) models containing random parameters are increasingly used for modelling travel choices. Willingness-to-pay (WTP) measures, such as the value of travel time savings (VTTS) are, in the case of RCL models estimated in preference space, ratios of random parameters. In this paper we apply the Delta method to compute the confidence intervals of such WTP measures, taking into account the variance–covariance matrix of the estimates of the distributional parameters. The same Delta method can be applied when the model is estimated in WTP space. Compared to simulation methods such as proposed by Krinsky and Robb, the Delta method is able to avoid most of the simulations by deriving partly analytical expressions for the standard errors. Examples of such computations are shown for different combinations of random distributions.     

Time of day intervals partition for bus schedule using GPS data

Time of day partition of bus operating hours is a prerequisite of bus schedule design. Reasonable partition plan is essential to improve the punctuality and level of service. In most mega cities, bus vehicles have been equipped with global positioning system (GPS) devices, which is convenient for transit agency to monitor bus operations. In this paper, a new algorithm is developed based on GPS data to partition bus operating hours into time of day intervals. Firstly, the impacts of passenger demand and network traffic state on bus operational performance are analyzed. Then bus dwell time at stops and inter-stop travel time, which can be attained based on GPS data, are selected as partition indexes. For buses clustered in the same time-of-day interval, threshold values of differences in dwell time at stops and inter-stop travel time are determined. The buses in the same time-of-day interval should have adjacent dispatching numbers, which is set as a constraint. Consequently, a partition algorithm with three steps is developed. Finally, a bus route in Suzhou China is taken as an example to validate the algorithm. Three partition schemes are given by setting different threshold values for the two partition indexes. The present scheme in practice is compared with the three proposed schemes. To balance the number of ToD intervals and partition precision, a Benefit Evaluation Index is proposed, for a better time-of-day interval plan.