Does emission control area policy reduce sulfur dioxides concentration in Shanghai?

As an important measure to abate ship-source air emissions, the establishment of Emission Control Area (ECA) has been adopted not only at international level, but also at national and regional level. However, there exists a research gap of employing econometric models to enrich the ECA policy evaluation research by testing the effectiveness of an ECA policy based on the empirical data. This paper focuses on the effectiveness of the ECA policy in shanghai port by adopting regression discontinuity (RD) approach. The RD design is considered as a wonderful substitution for the randomized control trial, which usually serves as a conventional and effective tool to address endogeneity. The causal effect of the ECA policy on the SO₂ concentration reduction can be detected by the RD approach. According to the estimated results, there is a discontinuity around the ECA policy cutoff point, and it is indicated that the SO₂ concentration in Shanghai decreased by at least 0.229 μg/m³ daily on average due to the implementation of the ECA policy. The positive role that the ECA policy plays in reducing SO₂ concentration in Shanghai is well demonstrated. This paper also puts forward an agenda for the future studies in the domain of the ECA policy evaluation.     

The effect of railway local irregularities on ground vibration

The environmental effects of ground-borne vibrations generated due to localised railway defects is a growing concern in urban areas. Frequency domain modelling approaches are well suited for predicting vibration levels on standard railway lines due to track periodicity. However, when considering individual, non-periodic, localised defects (e.g. a rail joint), frequency domain modelling becomes challenging. Therefore in this study, a previously validated, time domain, three-dimensional ground vibration prediction model is modified to analyse such defects. A range of different local (discontinuous) rail and wheel irregularity are mathematically modelled, including: rail joints, switches, crossings and wheel flats. Each is investigated using a sensitivity analysis, where defect size and vehicle speed is varied. To quantify the effect on railroad ground-borne vibration levels, a variety of exposure–response relationships are analysed, including: peak particle velocity, maximum weighted time-averaged velocity and weighted decibel velocity. It is shown that local irregularities cause a significant increase in vibration in comparison to a smooth track, and that the vibrations can propagate to greater distances from the line. Furthermore, the results show that step-down joints generate the highest levels of vibration, whereas wheel flats generate much lower levels. It is also found that defect size influences vibration levels, and larger defects cause greater vibration. Lastly, it is shown that for different defect types, train speed effects are complex, and may cause either an increase or decrease in vibration levels.     

Exploring the feasibility of classification trees versus ordinal discrete choice models for analyzing crash severity

A cross-median crash (CMC) is one of the most severe types of crashes in which a vehicle crosses the median and sometimes collides with opposing traffic. A study of severity of CMCs in the state of Wisconsin was conducted by Lu et al. in 2010. Discrete choice models, namely ordinal logit and probit models were used to analyze factors related to the severity of CMCs. Separate models were developed for single and multi-vehicle CMCs. Although 25 different crash, roadway, and geometric variables were used, only 3 variables were found to be statistically significant which were alcohol usage, posted speed, and road conditions. The objective of this research was to explore the feasibility of GUIDE Classification Tree method to analyze the severity of CMCs to discover if any additional information could be revealed.A dataset of CMCs in the state of Wisconsin between 2001 and 2007, used in the study by Lu et al. was used to develop three different GUIDE Classification Trees. Additionally, the effects of variable types (continuous or discrete), misclassification costs, and tree pruning characteristics on models results were also explored. The results were directly compared with discrete choice models developed in the study by Lu et al. showing that the GUIDE Classification Trees revealed new variables (median width and traffic volume) that affect CMC severity and provided useful insight on the data. The results of this research suggest that the use of Classification Tree analysis should at least be considered in conjunction with regression-based crash models to better understand factors affecting crashes. Classification Tree models were able to reveal additional information about the dependent variable and offer advantages with respect to multicollinearity and variable redundancy issues.     

Cox Regression模型在交通事件持续时间研究中的应用

有效的交通事件管理要求交通管理者全面了解交通事件的各种特征才能准确估计事件持续时间,从而及时地疏导交通拥堵。利用某高速公路应急指挥中心管理系统中记录的近3 a的交通事件持续时间数据,建立Cox Regression模型探索影响持续时间的危险因素并评价其作用强度和方向。研究表明,日夜、报警方式、事件类型、占用车道数、涉及车辆数、涉及死亡、救护车、牵引车、吊车、驳车、涉及货车等11项是交通事件持续时间的显著影响因素,因此,交通管理者对这些因素进行改善可有效提高交通事件管理效率和安全性。     

船用堆堆芯控制棒分布对围板/反射层不连续因子的影响分析

针对控制棒大距离移动时积分价值计算有较大误差的问题,分析发现船用反应堆围板/反射层节块的等效均匀化参数尤其是不连续因子受反应堆内控制棒的分布状态影响是导致误差的主要原因,通过引入二维围板/反射层等效均匀化参数计算方法详细计算不同棒态下围板/反射层的均匀化参数,最后经验算发现,能够使积分价值计算误差控制在2%以内。     

建筑结构强度超声回弹逆回归区间融合评定法

运用因变量估计自变量及其可能取值范围的逆回归分析方法代替传统的回归分析方法,推导出回弹值和声速值的逆回归模型数据融合测强曲线。运用区间估计方法代替传统的点估计方法,导出结构混凝土强度的置信上限、下限和置信区间。给出混凝土强度的判定标准,在给定高置信度下对结构混凝土强度做出合格、不合格和待定结论的定量判断。该测强曲线比任何单一方法的测强曲线的精度高,减小偶然误差,能够消除无损检测中无法通过仪器校准的系统误差,并且解决了回弹值和声速值不同量纲的数据融合问题。     

基于乘客满意度的公交车站立乘客面积研究

公交车内站立乘客面积是影响公共交通乘车阶段乘客满意度的关键指标.将使乘客感受由不满意转变为满意的公交车站立乘客面积的临界值定义为临界站立乘客面积.本文旨在提出临界站立乘客面积的建议值.首先,在呼和浩特市开展了客流调查及问卷调查;然后,基于调查所获数据,采用有序 Logistic回归建立了乘客满意度与站立乘客面积的模型;最后,通过对所建模型的图形化分析,提出临界站立乘客面积(即保证乘客满意的最小站立乘客面积)的建议值为 0.25 m2/人.本文研究成果为公共交通乘车阶段的乘客满意度评价、舒适性评价提供量化依据,为公交车额定载客量的核定提供新思路.     

Exploration of day-to-day route choice models by a virtual experiment

This paper examines existing day-to-day models based on a virtual day-to-day route choice experiment using the latest mobile Internet technologies. With the realized day-to-day path flows and path travel times in the experiment, we calibrate several well-designed path-based day-to-day models that take the Wardrop’s user equilibrium as (part of) their stationary states. The nonlinear effects of path flows and path time differences on path switching are then investigated. Participants’ path preferences, time-varying sensitivity, and learning behavior in the day-to-day process are also examined. The prediction power of various models with various settings (nonlinear effects, time-varying sensitivity, and learning) is compared. The assumption of “rational behavior adjustment process” in Yang and Zhang (2009) is further verified. Finally, evolutions of different Lyapunov functions used in the literature are plotted, and no obvious diversity is observed.     

铁路隧道围岩分级的知识挖掘

从国内外各种围岩分类方法中可以看出,结构面与隧道围岩分级密切相关,不同的围岩分级具有不同的结构面特性。通过分析结构面与隧道围岩分级之间的关系,提出隧道围岩分级知识挖掘方法。根据数据挖掘原理,在结构面众多属性中首先剔除明显不合适的力学和岩性属性,然后按信息增益大小进一步选择挖掘所需的结构面属性,同时提出结构面权度概念表示结构面相互切割程度。信息增益分析结果表明,一般结构面和断层的各个属性在围岩分级中所起作用差别较大,因而采用的属性也不同。以表格形式表达概念描述和概念对比描述结果,反映出结构面各个属性对围岩分级的贡献大小及作用的程度;根据后者提出了围岩分级预测方法,应用结果是可行的,而且一般结构面和断层分别预测,效果会更好。     

一种新的支持向量回归算法及其在集装箱吞吐量预测中的应用

支持向量机是基于统计学习理论框架下的一种新的通用机器学习方法,是一种处理非线性分类和非线性回归的有效方法。由于具有完备的理论基础和出色的学习性能,该技术已成为当前国际机器学习界的研究热点,能较好地对应解决小样本、高维数、非线性和局部极小点等实际问题。近来,SVR方法被引入求解回归和预测问题,并在各领域中得到广泛的应用。文章提出了一种新的基于单参数的Lagrangian支持向量回归算法,并将该算法应用在集装箱吞吐量预测中。估算结果证明了这种改进的支持向量回归算法在集装箱吞吐量预测中的有效性和实用性。