时间序列法预测沥青路面损坏状况指数研究

由于沥青路面损坏状况影响因素很多,因此要准确预测路面损坏状况较困难。文章采用时间序列法建立预测模型,结合同三高速公路（上海段）路面损坏状况的实测数据进行预测分析。分析结果表明时间序列法具有较高的预测精度和易修正性。     

橡胶沥青技术在公路建设和养护中的应用

橡胶沥青作为一种新型的道路材料,具有降低路面噪音、提高行驶安全性与舒适性等优异性能。文章介绍了橡胶沥青的优点,分析了影响橡胶沥青及橡胶沥青混凝土质量的因素,并对橡胶沥青应用于公路建设和养护所存在的问题进行了探讨。     

CO2 emissions and expansion of railway,road, airline and in-land waterway networks over the 1985–2013 period in China: A time series analysis

China, the world’s largest CO₂ emitter, is continuing its long-term strategy to use transportation investments as a tool for development. With the expectation that transportation will contribute 30–40% of the total CO₂ emissions in China in the near future, there is an imminent need to identify how the development of different transportation modes may have different long-term effects on CO₂ emissions. Using time series data over the period of 1985–2013, this paper applies the combined autoregressive distributed lag (ARDL) and vector error correction model (VECM) approach to identify short- and long-run causal relationships between CO₂ emissions and mode-specific transportation development, including railway, road, airline, and inland waterway. We find that China’s domestic expansions of road, airline, and waterway infrastructure lead to long-run increases in CO₂ emissions. Among them, waterway has the strongest positive impact on CO₂ emissions, followed by road. Despite a short-run, positive impact on CO₂ emissions, railway expansion leads to long-run decreases in CO₂ emissions. The results are especially encouraging for the central government of China given its long-standing and on-going efforts to expand railway infrastructure at the national level. Looking forward, it is recommended that China continues its national investments in railway infrastructure to achieve both environment and economy goals.     

Local online kernel ridge regression for forecasting of urban travel times

Accurate and reliable forecasting of traffic variables is one of the primary functions of Intelligent Transportation Systems. Reliable systems that are able to forecast traffic conditions accurately, multiple time steps into the future, are required for advanced traveller information systems. However, traffic forecasting is a difficult task because of the nonlinear and nonstationary properties of traffic series. Traditional linear models are incapable of modelling such properties, and typically perform poorly, particularly when conditions differ from the norm. Machine learning approaches such as artificial neural networks, nonparametric regression and kernel methods (KMs) have often been shown to outperform linear models in the literature. A bottleneck of the latter approach is that the information pertaining to all previous traffic states must be contained within the kernel, but the computational complexity of KMs usually scales cubically with the number of data points in the kernel. In this paper, a novel kernel-based machine learning (ML) algorithm is developed, namely the local online kernel ridge regression (LOKRR) model. Exploiting the observation that traffic data exhibits strong cyclic patterns characterised by rush hour traffic, LOKRR makes use of local kernels with varying parameters that are defined around each time point. This approach has 3 advantages over the standard single kernel approach: (1) It allows parameters to vary by time of day, capturing the time varying distribution of traffic data; (2) It allows smaller kernels to be defined that contain only the relevant traffic patterns, and; (3) It is online, allowing new traffic data to be incorporated as it arrives. The model is applied to the forecasting of travel times on London’s road network, and is found to outperform three benchmark models in forecasting up to 1 h ahead.     

基于MIDAS/Civil的堂屋岭特大桥的抗震分析

文章利用Midas/Civil 2012结构分析软件,遵循以概率理论为基础的"两水平、两阶段"的桥梁抗震设计方法,对堂屋岭特大桥进行反应谱分析和时程瞬态分析,并利用统计方法将分析结果绘制成结构内力图,以判定该特大桥在不同的地震效应下的结构性能是否满足设计要求。     

某大桥地震作用下的时程响应分析

文章以某桥主桥2×100m的梁-斜拉组合体系为研究对象,采用ANSYS有限元软件对该桥进行了抗震性能研究,并基于地震时程分析法,评估了该大桥在地震荷载作用下的应力和位移响应情况,找到了该桥的薄弱环节,为该桥的设计提出有益的建议。     

Stochastic frontier estimation of budgets for Kuhn–Tucker demand systems: Application to activity time-use analysis

We propose a stochastic frontier approach to estimate budgets for the multiple discrete–continuous extreme value (MDCEV) model. The approach is useful when the underlying time and/or money budgets driving a choice situation are unobserved, but the expenditures on the choice alternatives of interest are observed. Several MDCEV applications hitherto used the observed total expenditure on the choice alternatives as the budget to model expenditure allocation among choice alternatives. This does not allow for increases or decreases in the total expenditure due to changes in choice alternative-specific attributes, but only allows a reallocation of the observed total expenditure among different alternatives. The stochastic frontier approach helps address this issue by invoking the notion that consumers operate under latent budgets that can be conceived (and modeled) as the maximum possible expenditure they are willing to incur. The proposed method is applied to analyze the daily out-of-home activity participation and time-use patterns in a survey sample of non-working adults in Florida. First, a stochastic frontier regression is performed on the observed out-of-home activity time expenditure (OH-ATE) to estimate the unobserved out-of-home activity time frontier (OH-ATF). The estimated frontier is interpreted as a subjective limit or maximum possible time individuals can allocate to out-of-home activities and used as the time budget governing out-of-home time-use choices in an MDCEV model. The efficacy of this approach is compared with other approaches for estimating time budgets for the MDCEV model, including: (a) a log-linear regression on the total observed expenditure for out-of-home activities and (b) arbitrarily assumed, constant time budgets for all individuals in the sample. A comparison of predictive accuracy in time-use patterns suggests that the stochastic frontier and log-linear regression approaches perform better than arbitrary assumptions on time budgets. Between the stochastic frontier and log-linear regression approaches, the former results in slightly better predictions of activity participation rates while the latter results in slightly better predictions of activity durations. A comparison of policy simulations demonstrates that the stochastic frontier approach allows for the total out-of-home activity time expenditure to either expand or shrink due to changes in alternative-specific attributes. The log-linear regression approach allows for changes in total time expenditure due to changes in decision-maker attributes, but not due to changes in alternative-specific attributes.     

On altruists and egoists in activity participation and travel: who are they and do they live together?

Formulation and specification of activity analysis models require better understanding of time allocation behavior that goes beyond the more recent within household analyses to understand selfish and altruistic behavior and how this relates to travel behavior. Using data from 1,471 persons in a recent 2-day time use/activity diary and latent class cluster analysis we identify 11 distinct daily behaviors that span from the intensely self-serving to intensely altruistic. Predicted cluster membership is then used to study within household interactions. The analysis shows strong correlation exists between social role and patterns of altruistic behavior. However, a substantial amount of heterogeneity is also found within social roles. In addition, travel behavior is also very different among altruistic and self-serving time allocation groups. At the household level, a substantial number of households contain persons with similar behavior. Another group of households contains a mix of self-serving and altruistic persons that follow specialized household roles within their households. The majority of households, however, are populated by altruistic persons. Single person households are more likely to be in the self-serving groups but not in their entirety. Altruism at home is directed most often toward the immediate family members. This is less pronounced when we examine altruistic acts outside the home. Konstadinos G. Goulias is a professor of Geography at the University of California Santa Barbara, has been a professor of Civil Engineering at the Pennsylvania State University from 1991 to 2004, and he is the founder and chair of the TRB task force on moving activity-based approaches to practice. Kriste M. Henson is a technical staff member at Los Alamos National Laboratory in the Decision Applications Division and is currently pursing a Ph.D. in Geography at the University of California—Santa Barbara.     

Understanding time use: Daily or weekly data?

The appropriate duration of time diaries as a source of time use data is analyzed in a structured way. Nine detailed European surveys based on seven-days diaries are used in order to study different dimensions of data quality, duration and variability of activities, and modeling capabilities. Pseudo diaries of 1, 2 (one week, one weekend) and 3 (one week, both weekend) days are constructed to further analyze these issues, selecting the seven-days diaries data as a benchmark. Comparative results show that two and three-days weighted surveys seem to be an adequate surrogate for the information obtained in weekly surveys that capture a basic work–leisure cycle.     

不对称连续刚构桥地震反应分析

文章以贵州省六盘山市一座不对称连续刚构桥为工程实例,利用结构分析软件MIDASCIVIL建立了该桥的有限元分析模型,并选用Taft波与时程分析法计算了该桥的地震响应情况。结果表明桥墩是地震作用下全桥受力最不利的位置,必须重视桥墩的抗震设计和验算。     

某连续刚构桥地震响应分析

文章以某连续刚构桥为例,利用结构分析软件MIDAS CIVIL建立了地震响应计算分析模型,并结合时程分析法对该桥的地震响应进行计算分析,所得结果可为大跨径连续刚构桥的抗震设计提供依据。     

Decouple transport CO2 emissions from China’s economic expansion: A temporal-spatial analysis

China’s transport industry is energy intensive and high-polluting. While with the surging urbanization and the development of service industry, China’s economic relies more and more on the transport sector. Therefore, exploring the relationship between transport energy-related carbon emission (TECE) and economic development is crucial to the realization of China’s “Post Paris” mitigation target. The paper carries out a decoupling research between TECE and Gross domestic product (GDP) at both national level and province level based on Logarithmic Mean Divisia Index (LMDI) decomposition analysis with the extended Kaya identity and Tapio decoupling model. The model quantifies eight factors’ effects on the relationship with focusing on external macro socio-economic related factors (i.e., spatial pattern, urbanization, per capita service industry output value, reciprocal of the service industry’s share of GDP, and demographic variable) successfully. The key conclusions are indicated as follows: (1) the national decoupling status was extensive coupling during 2004–2010 and then weak decoupling during 2010–2016. The progress can be attributed to the decline of energy intensity. (2) Per capita service output was always the prominent factor to promote carbon emissions growth in different time periods and provinces with inhibiting the advancement of decoupling process, followed by urbanization. (3) Scenario analysis shows that with the continuous growth of traffic demand and the promotion of urbanization, improving energy efficiency has become the key link to realize the decoupling between China’s TECE and its economy.