中国高速公路事故特点及分布规律研究

中国的高速公路建设现在正处于快速发展时期,其事故发生率与事故严重程度与国外高速公路相比均高出很多,可以预见的是高速公路的安全形势将随着经济和社会的发展,以及汽车保有量和高速公路通车里程的增加,变得越来越严峻。在大量调研高速公路事故数据的基础上,利用统计分析的方法,研究讨论了我国高速公路交通事故的基本特点及分布规律,得出了一系列有价值的结论。研究结果可以为高速公路的交通安全管理与控制,以及交通事故的预测和预防提供良好的参考和借鉴。     

Development of destination choice model with pairwise district-level constants using taxi GPS data

In this paper, a destination choice model with pairwise district-level constants is proposed for trip distribution based on a nearly complete OD trip matrix in a region. It is found that the coefficients are weakly identified in a destination choice model with pairwise zone-level constants. Thus, a destination choice model with pairwise district-level constants is then proposed and an iterative algorithm is developed for model estimation. Herein, the “district” means a spatial aggregation of a number of zones. The proposed model is demonstrated through simulation experiments. Then, destination choice models with and without pairwise district-level constants are estimated based on GPS data of taxi passenger trips collected during morning peak hours within the Inner Ring Road of Shanghai, China. The datasets comprise 504,187 trip records and a sample of 10,000 taxi trips for model development. The zones used in the study are actually 961 residents’ committees while the districts are 52 residential districts that are spatial aggregations and upper-level administrative units of residents’ committees. It is found that the estimated value of time dramatically drops after the involvement of district-level constants, indicating that the traditional model tends to overestimate the value of time when ignoring pairwise associations between two zones in trip distribution. The proposed destination choice model can ensure its predicted trip OD matrix to match the observed one at district level. Thus, the proposed model has potential to be widely applied for trip distribution under the situation where a complete OD trip matrix can be observed.     

客户配送要求变动下的VRPSDP干扰管理优化

为解决客户配送要求变动对VRPSDP方案造成的干扰问题，从成本和服务时间两个方面，就干扰事件对原物流配送车辆路径方案造成的影响进行了干扰辨识分析.构建了VRPSDP干扰管理模型，并设计基于禁忌搜索的调度算法.从Solomon标准测试算例中随机抽取6组算例对模型和算法的有效性进行验证.结果显示，本文的干扰管理方案与传统的增派车辆和全局重调度方案相比较，广义总费用偏离有明显地降低，且本文方法能够在较短的时间内生成满意的物流配送车辆调度调整方案.     

On the application of the Haskind-Hanaoka relations to hydroelasticity problems

The Haskind-Hanaoka relations for obtaining the wave-exciting forces, given the incoming and radiation potentials in floating rigid-body dynamics, are extended to deformable-body dynamics in the presence of a free surface and within the assumption of linear potentials theory. These relations are applied to the mode-superposition and the direct methods of solution of hydroelasticity problems. In other words, it is shown that regardless of the structural analysis method used in a hydroelasticity problem, the Haskind-Hanaoka relations can be used to calculate the wave-exciting forces without solving for the diffraction potential explicitly. These relations can also be used in the composite singularity distribution method to increase further the computational efficiency.     

磨粒分布函数及可靠性数学用于部分弹流中接触：概率与磨损研究

本文提出一种实际机械中的润滑均为部分弹流状态的新观点.文中利用磨粒尺寸分布函数及可靠性数学方法解决了部分弹流中接触概率的计算和磨损规律,并进一步提出了设计计算时,接触概率的控制规范.     

信息熵理论在计算断面含沙量分布中的运用

运用信息论中的最大熵原理导出悬沙颗粒所处位置高度y的概率密度函数p(y).由p(y)与相对含沙量的关系得出适合于断面任一垂线的含沙量分布公式,在此基础上结合边界及流速情况提出了便于工程运用的针对宽浅河槽及非宽浅河槽断面含沙量分布的计算方法,计算结果与实测结果吻合良好.     

Mathematical characterization of spatiotemporal congested traffic patterns: mixed speed data analysis in the greater Toronto and Hamilton area,Canada

This paper formulates a comprehensive methodology for analyzing, quantifying and identifying congestion characteristics based on speed distribution. Utilizing vehicle speed data, a mathematical approach is applied, in order to characterize roadway segments, in terms of travel reliability, congestion severity and duration. We argue that the Gaussian mixture model (GMM) and its parameter combination is the appropriate tool if we are to obtain quantitative congestion measures and rank roadway performance. A significant contribution of our approach is that it is based on assumptions regarding mixed components as well as speed distribution and can be applied to large databases. We test our framework on the greater Toronto and Hamilton area in Ontario, Canada, and conclude that congestion quantification through the application of the GMM can be successfully accomplished. Results indicate that speed patterns differ significantly between counties as well as days of the week.     

An application-oriented model of passenger waiting time based on bus departure time intervals

This paper develops an application-oriented model to estimate waiting times as a function of bus departure time intervals. Bus stops are classified into Type A and B depending on whether they are connected with urban rail transit systems. Distributions of passenger arrival rates are analyzed based on field data for Beijing. The results indicate that the best fits for the distribution of passenger arrival rates for Type A and B bus stops are the lognormal distribution and gamma distribution, respectively. By analyzing relationships between passenger arrival rates and bus departure time intervals, it is demonstrated that parameters of the passenger arrival rate distribution can be expressed by the average and coefficient of variation of bus departure time intervals in functional relationships. The validation shows that the model provides a reliable estimation of the average passenger waiting time based on readily available bus departure time intervals.     

Transit trip distribution model considering land use differences between catchment areas

Sustainable land use planning and advanced public transport system are believed to be effective solutions to traffic congestion. To this end, it is important to reveal the relationship between transit ridership and land use. However, current Direct Ridership Models only focus on the relationship between single station's boarding volume and the corresponding catchment area land use. This paper analyzed the ridership distribution between transit stations by considering the land use difference between catchment areas. Land use difference was calculated from point of interest (POI) data extracted from an electronic map of Beijing; transit trip distribution volume was obtained from ‘automatic fare collection’ facility. After data specification, a transit ridership distribution model was proposed and calibrated. The calibration results suggest that land use difference has a directly proportional correlation with transit ridership distribution. The research findings build a bridge between detailed urban form and public transport, which is of significance for the further research of sustainable urban planning. Copyright © 2016 John Wiley & Sons, Ltd.     

Pedestrian detection for traffic safety based on Accumulate Binary Haar features and improved deep belief network algorithm

ABSTRACT

In order to improve traffic safety and protect pedestrians, an improved and efficient pedestrian detection method for auto driver assistance systems is proposed. Firstly, an improved Accumulate Binary Haar (ABH) feature extraction algorithm is proposed. In this novel feature, Haar features keep only the ordinal relationship named by binary Haar features. Then, the feature brings in the idea of a Local Binary Pattern (LBP), assembling several neighboring binary Haar features to improve discriminating power and reduce the effect of illumination. Next, a pedestrian classification method based on an improved deep belief network (DBN) classification algorithm is proposed. An improved method of input is constructed using a Restricted Bolzmann Machine (RBM) with T distribution function visible layer nodes, which can convert information on pedestrian features to a Bernoulli distribution, and the Bernoulli distribution can then be used for recognition. In addition, a middle layer of the RBM structure is created, which achieves data transfer between the hidden layer structure and keeps the key information. Finally, the cost-sensitive Support Vector Machine (SVM) classifier is used for the output of the classifier, which could address the class-imbalance problem. Extensive experiments show that the improved DBN pedestrian detection method is better than other shallow classic algorithms, and the proposed method is effective and sufficiently feasible for pedestrian detection in complex urban environments.