The relationship among highway geometrics,traffic-related elements and motor-vehicle accident frequencies

This research provides a demonstration of a statistical model of accident frequency that can eventually be used as part of a proactive program to allocate safety-related highway improvement funds. Negative binomial regressions of annual accident frequency on sections of principal arterials in Washington State were estimated using data from two years (1992 and 1993). In all, 31306 observations were used in model estimation (annual accident frequencies on specific sections of highway). Our estimation results isolated the effects of various highway geometric and traffic characteristics on annual accident frequency. Subsequent elasticity computations identified the relative importance of the variables included in our specifications. The findings show that the negative binomial regression used in this paper is a powerful predictive tool and one that should be increasingly applied in future accident frequency studies.     

从语义角度论语言的不确定性

文章介绍了语言不确定性产生的原因,并从语义角度分析了语言不确定性的主要表现形式及其语用功能。通过对语言不确定性的探讨与研究,可以加深对语言规律的认识,把握其形式和内容的对应关系,更好地体味语言的蕴涵,提高英语的运用能力和鉴赏能力。     

支持向量机在单桩竖向极限承载力预测中的应用

单桩竖向极限承载力是桩基础设计中一项重要的内容,本文应用V-支持向量回归机算法,对单桩竖向极限承载力进行了预测。结果表明这种方法可以作为单桩竖向极限承载力计算的一种参考。     

The Influences of the Built Environment and Residential Self-Selection on Pedestrian Behavior: Evidence from Austin, TX

Pedestrian travel offers a wide range of benefits to both individuals and society. Planners and public health officials alike have been promoting policies that improve the quality of the built environment for pedestrians: mixed land uses, interconnected street networks, sidewalks and other facilities. Whether such policies will prove effective remains open to debate. Two issues in particular need further attention. First, the impact of the built environment on pedestrian behavior may depend on the purpose of the trip, whether for utilitarian or recreational purposes. Second, the connection between the built environment and pedestrian behavior may be more a matter of residential location choice than of travel choice. This study aims to provide new evidence on both questions. Using 1368 respondents to a 1995 survey conducted in six neighborhoods in Austin, TX, two separate negative binomial models were estimated for the frequencies of strolling trips and pedestrian shopping trips within neighborhoods. We found that although residential self-selection impacts both types of trips, it is the most important factor explaining walking to a destination (i.e. for shopping). After accounting for self-selection, neighborhood characteristics (especially perceptions of these characteristics) impact strolling frequency, while characteristics of local commercial areas are important in facilitating shopping trips.     

SPSA在公交枢纽内车辆实时调度优化中的应用研究

对公交枢纽内车辆进行实时调度能提高换乘效率,特别是对已进行了调度优化的公交线路,而车辆到达出现延误的情况.根据线路的延迟到达时间和换乘客流量等因素建立了公交枢纽内车辆的实时调度优化模型,提出了基于整个系统费用最小的优化目标函数,并运用随机扰动梯度近似算法（SPSA）对优化模型进行求解,给出了实际应用算例.     

精密的Hayman不等式的推广

在函数值分布论中,有一个重要的Hayman不等式,杨乐证明了精密的Hayman不等式,文中基于Nevanlinna理论对著名的精密Hayman不等式进行了推广,主要是把原不等式中使用的计数函数的常数易为超越整函数,得到副一种形式的Hayman不等式。     

logistic模型在裂纹检测概率曲线拟和中的应用

提出了一种适合于描述无损检测概率曲线的经过修正的logistic回归模型,并运用最小二乘原理,利用非线性回归分析,结合高斯-牛顿迭代法,给出了求解该模型中未知参数的具体思路与方法.同时结合具体算例,对该模型的拟和优良性进行评估分析.计算结果表明,该模型的预测值与实验值吻合良好,有较高的预测精度.     

The impact of real-time information on bus ridership in New York City

In the past few years, numerous mobile applications have made it possible for public transit passengers to find routes and/or learn about the expected arrival time of their transit vehicles. Though these services are widely used, their impact on overall transit ridership remains unclear. The objective of this research is to assess the effect of real-time information provided via web-enabled and mobile devices on public transit ridership. An empirical evaluation is conducted for New York City, which is the setting of a natural experiment in which a real-time bus tracking system was gradually launched on a borough-by-borough basis beginning in 2011. Panel regression techniques are used to evaluate bus ridership over a three year period, while controlling for changes in transit service, fares, local socioeconomic conditions, weather, and other factors. A fixed effects model of average weekday unlinked bus trips per month reveals an increase of approximately 118 trips per route per weekday (median increase of 1.7% of weekday route-level ridership) attributable to providing real-time information. Further refinement of the fixed effects model suggests that this ridership increase may only be occurring on larger routes; specifically, the largest quartile of routes defined by revenue miles of service realized approximately 340 additional trips per route per weekday (median increase of 2.3% per route). Although the increase in weekday route-level ridership may appear modest, on aggregate these increases exert a substantial positive effect on farebox revenue. The implications of this research are critical to decision-makers at the country’s transit operators who face pressure to increase ridership under limited budgets, particularly as they seek to prioritize investments in infrastructure, service offerings, and new technologies.     

Real time location prediction with taxi-GPS data streams

The prediction of the destination location at the time of pickup is an important problem with potential for substantial impact on the efficiency of a GPS-enabled taxi service. While this problem has been explored earlier in the batch data set-up, we propose in this paper new solutions in the streaming data set-up. We examine four incremental learning methods using a damped window model namely, Multivariate multiple regression, Spherical-spherical regression, Randomized spherical K-NN regression and an Ensemble of these methods for their effectiveness in solving the destination prediction problem. The performance of these methods on several large datasets are evaluated using suitably chosen metrics and they were also compared with some other existing methods. We found that the Multivariate multiple regression method has the best performance in terms of prediction accuracy but the Spherical-spherical regression method is the best performer when we take into account the accuracy time trade-off criterion. The next pickup location problem, where we are interested in predicting the next pickup location for a taxi given the dropoff location coordinates of the previous trip as input is also considered and the aforementioned methods are examined for their suitability using real world datasets. As in the case of destination prediction problem, here also we find that the Multivariate multiple regression method gives better performance than the rest when we consider prediction accuracy but the Spherical-spherical regression method is the best performer when the accuracy-time trade-off criterion is taken into account.     

Application of multi-criteria decision analysis methods for assessing walkability: A case study in Porto Alegre,Brazil

The concept of walkability refers to the extent to which a neighbourhood is walking-friendly. Several walkability indexes have been developed to quantify and evaluate the pedestrian environment. These indexes differ in terms of type of data, methods and goals. The indexes variables may present either uniform or distinct weights, defined by arbitrary, empirical or other diverse weighting methods. This paper pursues the determination of a weighted walkability index, constructed on the basis of the relative importance of their attributes. Weights were determined by the application of the Fuzzy Analytic Hierarchy Process (FAHP), a robust multi-criteria method which considers the experts’ uncertainty in decision making. Moreover, FAHP weights were compared with the attribute weights obtained from other simpler methods, and a chi-square test for homogeneity was computed to compare the obtained values. The three most important walkability attributes were: Public Security, Traffic Safety and Pavement Quality, similar results to the ones found in the literature. The application to a case study in the city of Porto Alegre, Brazil, allowed categorizing the studied neighbourhoods and to analyse the effect of changes on attributes in walkability.