聚焦“专业岗位职务能力”背景下的课程改革与实践——以“建筑结构基础与钢筋算量”课程为例

建筑结构识图和钢筋算量是高职土建类专业学生的核心技能之一。通过聚焦"专业岗位职务能力",对课程重新定位,结合工作岗位任务进行教学内容选取并实施综合考核方式,使学生的知识技能更加贴合专业岗位需求,并实现学生学习热情的长效性,让学生更加适应市场需求,取得了较好的教学效果。     

A new logit‐artificial neural network ensemble for mode choice modeling: a case study for border transport

Logit model is one of the statistical techniques commonly used for mode choice modeling, while artificial neural network (ANN) is a very popular type of artificial intelligence technique used for mode choice modeling. Ensemble learning has evolved to be very effective approach to enhance the performance for many applications through integration of different models. In spite of this advantage, the use of ANN‐based ensembles in mode choice modeling is under explored. The focus of this study is to investigate the use of aforementioned techniques for different number of transportation modes and predictor variables. This study proposes a logit‐ANN ensemble for mode choice modeling and investigates its efficiency in different situations. Travel between Khobar‐Dammam metropolitan area of Saudi Arabia and Kingdom of Bahrain is selected for mode choice modeling. The travel on this route can be performed mainly by air travel or private vehicle through King Fahd causeway. The results show that the proposed ensemble gives consistently better accuracies than single models for multinomial choice problems irrespective of number of input variables. Copyright © 2015 John Wiley & Sons, Ltd.     

Link travel time inference using entry/exit information of trips on a network

This paper studies link travel time estimation using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation–Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times.     

The relationship between land use and automobile travel utility: A Multiple Indicators Multiple Causes approach

The relationship between land use and the utility of automobile travel is examined by refining the utility concept, particularly by combining the microeconomic utility theory, which is concerned with the disutility of travel, and the perspective on the positive utility. A conceptual model is accordingly developed and then adjusted considering different purposes of travel. The purpose-specific models are tested through a Multiple Indicators Multiple Causes approach in Seoul, Korea, using datasets from a sample survey and geographic information systems. The major finding is that land use affects the utility mainly by changing synergy and affective utility rather than instrumental utility, which encompasses disutility variables. Among land use variables, the utility is found to be the most sensitive to the number of transit facilities for commuting and shopping travel and land use balance for leisure travel.     

Large-scale transit market segmentation with spatial-behavioural features

Transit market segmentation enables transit providers to comprehend the commonalities and heterogeneities among different groups of passengers, so that they can cater for individual transit riders’ mobility needs. The problem has recently been attracting a great interest with the proliferation of automated data collection systems such as Smart Card Automated Fare Collection (AFC), which allow researchers to observe individual travel behaviours over a long time period. However, there is a need for an integrated market segmentation method that incorporating both spatial and behavioural features of individual transit passengers. This algorithm also needs to be efficient for large-scale implementation. This paper proposes a new algorithm named Spatial Affinity Propagation (SAP) based on the classical Affinity Propagation algorithm (AP) to enable large-scale spatial transit market segmentation with spatial-behavioural features. SAP segments transit passengers using spatial geodetic coordinates, where passengers from the same segment are located within immediate walking distance; and using behavioural features mined from AFC data. The comparison with AP and popular algorithms in literature shows that SAP provides nearly as good clustering performance as AP while being 52% more efficient in computation time. This efficient framework would enable transit operators to leverage the availability of AFC data to understand the commonalities and heterogeneities among different groups of passengers.     

The built environment and travel behavior in urban China: A literature review

Interests in studying of the built environment impacts on travel behavior have proliferated from North America to other parts of the world including China. Until very recently, there has been very little research into travel behavior in China. However, during the last decade, there has been a fast growing interest in studying the built environment and travel behavior in Chinese cities, perhaps motivated by China’s unprecedented urbanization and rapid urban transport development. Case studies from China provide new insights into the impacts of built environment on travel behavior that can help to enrich existing scholarship. However, currently there is a generally poor understanding of the role played by Chinese research and how it has enriched the international literature. This paper aims to fill this gap by reviewing studies in and outside China by both Chinese and non-Chinese scholars. The focus is on the contribution of these studies to the international literature. We identify four areas of contribution: how the built environment has been developed and its implications for travel behavior; the importance of housing sources in defining residential built environment and explaining travel behavior; the unique Danwei (or work unit) perspective on jobs-housing relationships and commuting behavior; and the importance of neighborhood types in explaining travel behavior in Chinese cities. The findings from this review should be relevant for researchers interested in developing future studies that will further advance geographic knowledge of the built environment and travel behavior, specifically in China and with broader global contexts.     

基于人口迁徙大数据的城市对外交通客运方式优势出行距离研究

为提高我国城市对外交通中客运多方式协同运行效率，优化客运资源配置，依托人口迁徙大数据，建立量化分析城市对外交通客运方式优势出行距离的方法. 提出用绝对优势出行距离和相对优势出行距离来表征优势出行距离，分别构建城市对外客运方式的绝对优势出行距离模型和相对优势出行距离模型；利用人口迁徙大数据，绘制基于出行距离的客运方式分担率曲线，对优势出行距离模型进行求解. 结果表明，我国城市对外交通中公路、铁路、航空客运方式的绝对优势出行距离分别为[8, 119] km、[119, 1 594] km和[1 594, 3 000] km，相对优势距离分别为[8, 463] km、[318, 983] km和[2 477, 3 000] km.     

基于电子签证平台的海事静态监管新模式

基于电子签证平台,日照海事局有效整合多种业务,创建了静态监管新模式。该模式科学高效,信息化程度高,将在以后的监管工作发挥重要的作用。     

基于自然驾驶数据的驾驶人换道决策识别研究

为实现人机共驾模式下智能系统对驾驶人换道决策的准确识别,将换道决策细分并提出了基于改进的极端梯度提升(XGBoost)的换道决策识别模型。以实车试验采集的自然驾驶数据作为输入,并采用滑动时间窗法确定识别时刻,建立各识别时间窗口下基于XGBoost的换道决策识别模型,同时运用交叉检验和网格搜索(GS)算法进一步提升模型性能,最后利用验证集数据评估所构建GS-XGBoost模型的识别性能,并与机器学习及深度学习模型进行对比。结果表明,所提出的模型在具体换道决策辨识上具有较好的实时性和准确性,且在1.8 s和1.6 s时间窗下的识别准确率最高,达到86.2%。     

Structural relationships among shipper’s perception,value, and choice intention of Korea–China train ferry service

ABSTRACT

The logistics environment between China and Korea has been changing rapidly in line with the ‘Go West’ policy of international trade and logistics area. The policy includes the BRI, Western Development Strategy, and Korea–China FTA. Against this background, the Korea–China train ferry (KCTF) is being newly illuminated as an alternative mode to cope with the logistics environment between Korea and China. One of the key success factors of the KCTF depends on, among others, shippers’ perception, which will affect the choice and the successful operation of the KCTF. From this perspective, this paper develops seven hypotheses based on the Theory of Reasoned Action (TRA) and Technology Acceptance Model (TAM). We test these hypotheses using Structural Equation Modeling (SEM) to explore the structural relationship between shippers’ behavioral beliefs influencing the perceived usefulness, the attitude toward and intention to choose the KCTF. The test results confirm that among the behavioral beliefs, transport accessibility in association with an efficient network of inland roads and railway networks is an important prerequisite. In addition, we verified a high statistical significance in the structural relationships among perceived usefulness, the attitude toward and the intention to choose the KCTF.