山区公路事故多发段道路条件分析与防治对策

针对山区公路交通事故的严重性,从山区公路道路条件的角度,对山区公路事故多发段道路环境中存在的主要隐患以及这些隐患如何导致了交通事故的发生进行了分析,并提出了对事故多发段进行改造的原则和防治对策。     

山区高速公路事故多发点的组合鉴别方法

基于国内外交通事故鉴别方法,结合山区高速公路特点及其事故特点,提出改进的鉴别指数与非定长分段法相结合的适合山区高速公路的混合鉴别方法,最后在包茂高速(西安—榆林)铜川段进行实例验证,证明了该方法的有效性。     

道路交通事故多发点鉴别

介绍了事故多发点的分析方法, 建立了事故多发点判别模型, 并给出了其算法, 根据中国交通流的特点, 开发了事故多发点数据库, 使事故多发点的鉴别工作能够快速进行。     

事故多发点鉴别及其成因分析

对比分析了事故多发点鉴别及其成因方法,归纳总结了各种方法的优缺点及适用条件。提出了基于BP神经网络的高速公路事故多发点鉴别方法,并应用实际高速公路交通事故数据,标定了网络的权值矩阵和偏置向量,该方法能有效解决矩阵法中人为确定边界的不足。针对事故多发点的突出事故诱导因素问题,提出了基于动态模糊聚类的多发点成因分析方法,该方法能将众多的事故诱导因素聚类为从主要原因到隐患原因等4个类别,进而得出较客观的成因分类结果。     

基于地理视角的交通事故多发点鉴别分析模型

为了对交通事故多发情况进行全面评价并建立预警机制, 考虑城市道路交通系统的地理视角特点, 综合交通事故多发点鉴别分析方法, 研究了事故多发点分析模型。首先明确被分析道路交通系统的内涵和层次结构, 针对交通系统的点、线、面不同层次确立基本评价指标体系, 集成常规统计法、矩阵分析法和改进的质量控制法等构建事故多发点鉴别分析模型, 并研究了参数的选取及分析结果的输出形式, 最后对某地9条道路的事故多发情况进行了分析。分析结果表明: 道路4为事故多发道路, 道路3具有最高的事故次数和当量事故次数, 道路5具有最高的事故率, 经综合评价, 确定事故多发道路为道路3、4、5, 因此, 该模型可以对道路交通事故多发点进行多层面鉴别分析。     

The role of paratransit: some reflections based on the experience of residents’ coach services in Hong Kong

Paratransit refers to urban transport services “somewhere between private passenger transport and conventional public transport in terms of cost and quality of service” [Rimmer (1980), Paratransit: A commentary. Environ. Plan. A 12, 937–944]. Since the 1980s, a new form of paratransit—residents’ coach, which provides exclusive transport services to residents living in private housing estates—has emerged in Hong Kong. What was the background underlying the emergence and subsequent growth of residents’ coach services? Also, what was the role of residents’ coach in the public transport system of Hong Kong? With the completion of more railway extensions, should residents’ coach services be replaced? If so, what are the potential impacts on people’s life? This paper addresses the above research questions through a large-scale questionnaire survey that examined not only people’s modal choice but also their residential choice, socio-economic background and attitudes. The findings suggest that transport policy makers should pay more attention to examine new forms of paratransit and the ways of integrating them into the overall public transport system, both spatially and temporally. It is only through the development of an efficient and multi-modal transport system can the maximum potential of paratransit in filling the transport gap between conventional mass transit and private cars be realized.     

某型车辆横向稳定杆的结构仿真及优化

为了评估车辆横向稳定杆的结构合理性,并对其结构进行优化设计,对某车辆横向稳定杆进行了分析。建立了该型稳定杆的 1/2 有限元模型,研究了原结构在载荷作用下的位移和扭转刚度,得到其应力云图与危险位置。在此基础上,以减小局部应力为目标,在不明显降低稳定杆的结构刚度和增大质量的前提下提出结构优化方案。采用有限元分析评估不同的优化方案,并确定了稳定杆的最优结构。研究确定了横向稳定杆的危险位置,并通过优化设计显著降低了其最大应力,能够为车辆横向稳定杆的结构设计和优化提供参考。     

交通事故多发路段研究

通过对已有的道路交通事故多发路段鉴别方法的分析,在累计频率法的基础上进行了改进,并编制了相应的计算机程序。通过程序以一定的步长重新划分路段单元,多次搜索事故多发路段位置,并进行精简合并处理。结果表明可以有效的减少事故多发路段鉴别遗漏,并准确定位。数据分析表明了事故多发路段的死亡人数和事故数量远高于正常路段,因此通过对事故多发路段的整治可有效的增加道路交通的安全性。     

From traces to trajectories: How well can we guess activity locations from mobile phone traces?

Passively generated mobile phone dataset is emerging as a new data source for research in human mobility patterns. Information on individuals’ trajectories is not directly available from such data; they must be inferred. Many questions remain in terms how well we can capture human mobility patterns from these datasets. Only one study has compared the results from a mobile phone dataset to those from the National Household Travel Survey (NHTS), though the comparison is on two different populations and samples. This study is a very first attempt that develops a procedure to generate a simulated mobile phone dataset containing the ground truth information. This procedure can be used by other researchers and practitioners who are interested in using mobile phone data and want to formally evaluate the effectiveness of an algorithm.To identify activity locations from mobile phone traces, we develop an ensemble of methods: a model-based clustering method to identify clusters, a logistic regression model to distinguish between activity and travel clusters, and a set of behavior-based algorithms to detect types of locations visited. We show that the distribution of the activity locations identified from the simulated mobile phone dataset resembles the ground truth better than the existing studies. For home locations, 70% and 97% of identified homes are within 100 and 1000 m from the truth, respectively. For work places, 65% and 86% of the identified work places are within 100 and 1000 m from the true ones, respectively. These results point to the possibility of using these passively generated mobile phone datasets to supplement or even replace household travel surveys in transportation planning in the future.