基于Box-Cox-TQR概率密度的铁路运用车保有量预测方法

合理的铁路运用车保有量，对满足铁路货运需求，提高货车运用效率，降低运营成本等有重要作用.考虑铁路运输系统复杂的内外部环境及其动态变化特性，对影响运用车保有量因素定性分析；提出了粗糙集属性约简、灰色关联分析、逐步回归方法相结合的主要影响因素识别方法.以此为基础，建立了基于 Box-Cox变换分位数回归(Box-Cox-TQR)和核密度估计相结合的概率密度预测模型.以国家铁路局运用车保有量实际数据为基础，进行预测试验.结果表明，利用主要因素识别的方法符合目标值的运动变化规律，预测结果具有良好的精度.此外，概率密度预测比点预测、区间预测传递出更多信息，为管理决策提供更多准确有用信息.     

Inferring patterns in the multi-week activity sequences of public transport users

The public transport networks of dense cities such as London serve passengers with widely different travel patterns. In line with the diverse lives of urban dwellers, activities and journeys are combined within days and across days in diverse sequences. From personalized customer information, to improved travel demand models, understanding this type of heterogeneity among transit users is relevant to a number of applications core to public transport agencies’ function. In this study, passenger heterogeneity is investigated based on a longitudinal representation of each user’s multi-week activity sequence derived from smart card data. We propose a methodology leveraging this representation to identify clusters of users with similar activity sequence structure. The methodology is applied to a large sample (n = 33,026) from London’s public transport network, in which each passenger is represented by a continuous 4-week activity sequence. The application reveals 11 clusters, each characterized by a distinct sequence structure. Socio-demographic information available for a small sample of users (n = 1973) is combined to smart card transactions to analyze associations between the identified patterns and demographic attributes including passenger age, occupation, household composition and income, and vehicle ownership. The analysis reveals that significant connections exist between the demographic attributes of users and activity patterns identified exclusively from fare transactions.     

Spatial-temporal traffic flow pattern identification and anomaly detection with dictionary-based compression theory in a large-scale urban network

Traffic flow pattern identification, as well as anomaly detection, is an important component for traffic operations and control. To reveal the characteristics of regional traffic flow patterns in large road networks, this paper employs dictionary-based compression theory to identify the features of both spatial and temporal patterns by analyzing the multi-dimensional traffic-related data. An anomaly index is derived to quantify the network traffic in both spatial and temporal perspectives. Both pattern identifications are conducted in three different geographic levels: detector, intersection, and sub-region. From different geographic levels, this study finds several important features of traffic flow patterns, including the geographic distribution of traffic flow patterns, pattern shifts at different times-of-day, pattern fluctuations over different days, etc. Both spatial and temporal traffic flow patterns defined in this study can jointly characterize pattern changes and provide a good performance measure of traffic operations and management. The proposed method is further implemented in a case study for the impact of a newly constructed subway line. The before-and-after study identifies the major changes of surrounding road traffic near the subway stations. It is found that new metro stations attract more commute traffic in weekdays as well as entertaining traffic during weekends.     

Travel mode choice among same-sex couples

Same-sex partnered individuals are far more likely to use transit, walk, and cycle, and to a lesser extent, use carpools than are people in straight couples. As society becomes more tolerant, gay and lesbian populations are an increasingly visible social group, yet they have received scant attention by transportation scholars. This paper builds on this nascent literature by documenting and attempting to explain these dramatic differences by controlling for factors known to influence mode choice.We perform two separate analyses employing two distinct datasets. The first analysis examines journey-to-work data from the American Community Survey. The second analysis focuses in specifically on non-motorized (walking, biking) travel using use self-reported walk and bike frequency from the 2009 National Household Travel Survey. In both, we find that characteristics of the neighborhoods in which gays and lesbians live, as well characteristics of the individuals themselves, only explain part of the increased propensity to use “alternative” modes of transportation; a strong residual effect remains.     

Use of magnetic monitoring of vehicles for proactive strategy development

The proactive operational strategy in the transport system which is a parallel concept associated with the Intelligent Transportation System (ITS) seems to be modern and right direction of development. However, the basis for its operation is the “information” in the broad sense of the term, on the functioning of the transport system itself. As new technologies develop, there emerge new opportunities of putting to use a wide array of sensors which can deliver more complete traffic data but using complex matrix of sensors is often unjustified economically and confusing. The paper proposes use of magnetometers as an interesting alternative to pass the requirements of simplicity of application, minimum costs and maximum of acquired information. The process of obtaining the information requires analysis of the quantitative and qualitative changes of the magnetic field. The conducted analyses demonstrate the possibility of using the passive magnetic methods for the purpose of monitoring of vehicles. Placement of the magnetic sensor bear out an important issue and was also discussed. During the experimental research it has been proven that passive magnetic methods enable obtaining the information on the movement of a vehicle as well as on the vehicle itself. Estimations of such values as: vehicle’s moving direction, velocity, dimensions, clearance or mass having ferromagnetic properties and even the state of strain in a vehicle’s structure were confirmed in practice. In addition thanks to magneto-mechanical effects the theoretical possibility of assessing the stress occurring in the components of vehicles, which could be indicative of the volume of cargo carried. Moreover, the crash experiment showed the possibility of a collision detection using magnetic signal. The need for obtaining information increases from year to year, with information becoming the biggest asset which enables both development and effective use of the transport system. Included researches, proofs emerging opportunity of more extensive use of magnetic sensors and the passive magnetic methods which could be applied in the modern transportation system.     

铁路GSM-R干扰模型及快速干扰识别与判定方法

针对铁路GSM-R通信系统所需的高可靠性和高传输质量的特性,通过对GSM-R受干扰情况的建模与分析,归纳并得出了3种常见GSM-R干扰的干扰模型,并基于此提出了一种基于通信信号与底部噪声两个层次的快速干扰识别方法及对应的判定方法。相对以往的识别与判定流程,该方法具有较低的计算复杂度,使得快速地发现干扰、识别干扰更加容易实现,且更有益于进一步的定位干扰与消除干扰。     

基于选择方案抽样调查的城市群低频率出行行为研究

为分析城市群内低频率城际出行行为,对长三角城市群内上海与其他城市间的城际出行进行选择方案抽样调查,基于城际出行行为调查数据建立出行方式选择的多项Logit(MNL)模型；选择方案抽样调查数据与随机抽样调查数据存在方案分层结构偏差和因素重视度偏差,故引入反转因子(IPW),分别用外源性样本极大似然估计(ESMLE)简单修正法和加权外源性样本极大似然估计(WESMLE)法对MNL模型进行修正.结果表明:未修正模型及2种修正法下的模型命中率分别为57％,75％和84％；WESMLE修正后的模型能基本满足建模精度要求；城市群内低频率城际出行行为受出行时间、费用及个人属性等因素影响,同时,也表现出一定程度的随机性和随意性.     

桥梁施工中的风险识别和风险评价

分析桥梁工程施工中常见风险的特点和风险事故,提出了风险识别依据和识别方法,并探讨了风险评价准则和风险评价法,以期为保障桥梁工程的施工质量提供参考.     

浅论船舶在能见度不良时的避碰行动

船舶在能见度不良中的避碰行为是一个复杂的过程,存在着许多难以量化因素和各种不确定因素,往往使驾驶员做出错误决策,导致海难事故的发生。本文客观地提出了合理的避让方法。     

Modeling travel behavior by the structural relationships between lifestyle,built environment and non-working trips

In the context of sustainable urban transport in developing countries, individuals’ travel behavior faces multiple factors which influence their mobility patterns. Recognizing these factors could be a favorable method to organize more regular and sustainable trip patterns. This study aims to identify the less well-known lifestyle along with more popular built environment as the main factors which shape travel behaviors. Employing data from 900 respondents of 22 urban areas in city of Shiraz, Iran, this paper explores travel behaviors as non-working trip frequencies by different modes. Results of structural equation model indicate a strong significant effect of individual’s lifestyle patterns on their non-working trips. However, built environment impact on travel behavior is small compared to lifestyle. Besides, other variables such as travel attitudes and socio-economic factors stay crucial in the mode choice selection. These findings indicate the necessity of regarding lifestyle orientations in travel studies as well as objective factors such as land use attributes.