Exploration of correlation between environmental factors and mobility at signalized intersections

Characterizing the relationship between environmental factors and mobility is critical for developing a sustainable traffic signal control system. In this study, the authors investigate the correlation of the environmental impacts of transport and mobility measurements at signalized intersections. A metamodeling-based method involving experimental design, simulations, and regression analysis was developed. The simulations, involving microscopic traffic modeling and emission estimation with an emerging emission estimator, provide the flexibility of generating cases with various intersection types, vehicle types, and other parameters such as driver behavior, fuel types, and meteorological factors. A multivariate multiple linear regression (MMLR) analysis was applied to determine the relationship between environmental and mobility measurements. Given the limitations of using the built-in emissions modules within current traffic simulation and signal optimization tools, the metamodeling-based approach presented in this paper makes a methodological contribution. The findings of this study set up the base for extensive application of simulation optimization to sustainable traffic operations and management. Moreover, the comparison of outputs from an advanced estimator with those from the current tool recommend improving the emissions module for more accurate analysis (e.g., benefit-cost analysis) in practical signal retiming projects.     

基于驾驶负荷的山区二级公路纵坡路段驾驶舒适度评价研究

为定量评价山区公路纵坡路段驾驶舒适度，本文以云南省文都二级公路为研究对象，进行实车试验开展驾驶负荷研究.首先，分析上、下坡路段坡度、坡长、加(减)速度指标对驾驶负荷影响程度，运用多元回归建立驾驶负荷模型；其次，利用心率增长率的第50、85分位值划分驾驶舒适度，结合所建模型对驾驶舒适度进行阈值分析，确定不同驾驶舒适程度对应坡度、加(减)速度区间范围；最后，对模型及阈值划分合理性进行分析验证.结果表明，坡度、加 (减)速度对纵坡路段驾驶负荷皆为显著变量，而坡长与驾驶负荷仅存在弱相关性.本文模型有效，阈值划分合理，可为低等级公路驾驶负荷研究提供参考.     

基于票务数据的团体旅客出行目的推断

为推进城际交通大数据的应用，需要补全出行目的信息，将团体旅客出行目的决策与文本主题生成类比，开发基于无监督学习框架的出行目的推断方法.提出嵌入出发时间生成模块的主题模型，以及团体旅客重建和语义化特征设计方法，并通过吉布斯采样估计参数. 基于调查数据的模型对比研究发现，模型对一般私务辨识性能提升7.7%；基于票务数据的案例研究发现，模型对出发时间预测精度达到90.9%，间接验证了模型的可靠性.主题标注表明，模型不仅推断出4种与典型模式相符的出行目的，还辨识出既有认识外的非常规模式.对道路客运分析表明，出行目的构成呈现地区差异，高铁开通对不同出行目的出行量的负向影响程度不一.     

A study on possibility of commuting trip using private motorized modes in cities around the world: Application of multilevel model

The focus of the current research was to evaluate how the individual’s social characteristics and urban infrastructure impacts the usage of Private Motorized Modes (PMM). Based on individual and urban characteristics a multilevel analysis was conducted on the possibility of commuting trip by private motorized modes on the rush time of 78 cities around the world. Also the selected cities were classified through a principal component analysis, and based on the classification the impact of and urban variables on the possibility of commuting trips made by private motorized modes (PCTP) was verified. Results showed a diverse range of variables related to the usage of PMM, as well as the urban structure and railway lengths being an important variable in travel behavior.     

A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction

With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k-nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k-nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature.     

The impact of competition on container port (in)efficiency

There are many studies on container port efficiency and that seek to understand what factors, such as technical and scale efficiency, private versus public terminal management or macro-economic factors, play on the efficiency score of a given port. There are fewer studies that focus on the role played by the inter-port competitive environment. This role remains difficult to assess. In fact, on the one hand, a port subject to high inter-port competition may record higher efficiency scores due to the pressure from the competitive environment. On the other hand, a port subject to high competition may be forced to over-invest and could therefore records a lower efficiency score. This article investigates this issue and examines how the degree of competition measured at different levels (local, regional and global level) impacts the efficiency score of a given container port. To do so, we implement a truncated regression with a parametric bootstrapping model. The model applied to information gathered for 200 container ports in 2007 and 2010 leads to the following conclusions: port efficiency decreases with competition intensity when measured in a range of 400–800 km (regional level); and the effect from competition is not significant when competition is measured at a local (less than 300 km) or at a global (more than 800 km) level. Estimates also show a tendency for ports who invested from 2007 to 2010 to experience a general decrease in efficiency scores, an element which could be explained by the time lag between the investment and the subsequent potential increase in container throughput.     

城市轨道交通新建线路对沿线住宅价格增值的时空效应

定量分析城市轨道交通对沿线土地价值的影响，是通过土地价值捕获将外部效益内部化， 解决中国新型城镇化过程中城市轨道交通财务可持续的关键问题。本文在开源数据环境下，获 取2011—2016年北京市二手房交易数据，采用特征价格模型(HPM)分析北京城市轨道交通新建 线路对沿线住宅价格增值的时空效应。研究表明：与全局常参数的多元线性回归模型(MLR)、空 间滞后模型(SLM)、空间误差模型(SEM)和空间杜宾模型(SDM)相比，局部变参数的地理加权回归 模型(GWR)拟合效果更优，可以更好地消除残差的空间效应，刻画轨道交通与土地价值关系的空 间异质性。城市轨道交通带来的可达性提升对沿线80%以上住宅小区的房价具有显著的正效 应，住宅价格增值比率随地铁站距离递远递减，且具有显著的空间异质性。轨道交通对住宅价格 的影响在空间上具有网络化效应，新建线路不仅会改变周边房价，对城轨网络其他位置的住宅也 具有影响。城市轨道交通对住宅价格的影响范围为1 km，在该范围内，住宅价格受到的增值效应 在 2011—2016 年基本稳定，约为 3%；受新线直接影响区域的住宅价格会产生相对更大的提升 (0.02%~0.22%)。     

Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.     

农用地估价中的评分估价法研究

针对国土资源部发布的《农用地估价规程》中的评分估价法的概念、步骤,就其实现方法进行了一系列的改进研究,提出采用回归分析法确定影响因素因子体系及其权重,并建立分值与价格之间的回归数学模型,同时采用模糊综合评判方法计算待估宗地的综合作用分值,最终得到其评估价格.     

现场总线控制系统时延特性及控制

分析了现场总线控制系统前向通道和反馈通道的传输时延特性，针对执行器的工作方式推导了线性被控对象在时间驱动方式下和在事件驱动方式下的离散时间模型，提出了2种现场总线控制系统的控制方法，1种是将随机时延转化为固定时延，进而对固定时延进行补偿；另1种是直接处理随机时延，这2种方法互为补偿，可有效地消除时延对现场总线控制系统性能的不良影响。