化学灌浆处理水泥砼路面板底脱空方法

文章针对广西柳州-王灵高速公路水泥砼路面板底存在脱空问题,分析了病害产生的原因,介绍了应用化学灌浆处理时从灌浆材料、设备的选定,到施工工序,质量检查,处理效果及方法的过程,提出了处理工程中还存在的问题及对策,对处理水泥砼路面板底脱空及改善路面的使用性有一定的参考意义。     

The Impact of Travel Time Information on Travelers’ Learning under Uncertainty

In this work, laboratory experiment was conducted in order to evaluate the effect of feedback on decision-making under uncertainty, with and without provided information about travel times. We discuss the prediction of travelers’ response to uncertainty in two route–choice situations. In the first situation travelers are faced with a route–choice problem in which travel times are uncertain but some external information about routes’ travel times is provided. The second situation takes place in a more uncertain environment in which external information about travel times is not provided, and the travelers’ only source of information is their own experience. Experimental results are in conflict with the paradigm about traveler information systems: As a consequence of information, the propensity of travelers to minimize expected travel time is not necessarily increased. Providing travelers with static information about expected travel times reveals an increase in the heterogeneity of travelers’ choices and reduces the maximization rate.     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

基于高斯过程机器学习方法的隧道围岩分类模型

针对现有围岩分类方法的局限性,基于工程实例,利用分类性能优异的高斯过程机器学习模型建立围岩类别与其主要影响因素之间的非线性映射关系,进而提出一种基于高斯过程的隧道围岩分类模型,实现不同情况下围岩分类的合理识别.将该模型应用于川藏公路二郎山隧道围岩分类,研究结果表明,隧道围岩分类的高斯过程机器学习模型是科学可行的,与人工神经网络模型、支持向量机模型相比较,该模型具有参数自适应化的优点,能方便快捷地给出合理可靠且具有概率意义的围岩分类评价结果,可对围岩分类结果的不确定性或可信度进行定量化评价.     

The accuracy of proxy responses in a stated choice setting: A re-examination and some controversial conclusions

Data is typically gathered from an individual respondent who represents the group or the household. This individual is often identified as the “primary decision maker” and is asked to provide responses as a proxy for the group given that the cost of interviewing each member individually is impractical and/or expensive. The collection of joint preferences is rarely undertaken, with the use of proxy responses not uncommon in travel behaviour research. Under such a framework, there exists an assumption that the primary decision maker has perfect knowledge of other group member preferences, and bargaining behaviour, and is able to synthesise this information when providing a response on their behalf. The validity of such an assumption however remains an open question, with recent research calling the reliability of proxy responses into account (Bateman and Munro, 2009). In this paper, using three models estimated in willingness to pay space, we examine the accuracy of proxy responses in a stated choice experiment. We find that there is overlap between a proxy response and the own preferences of the individual providing the proxy choice, but while the proxy responses fail to represent the full preference heterogeneity that exists in the actual choices made by individuals, the proxy responses in aggregate provide a suitable replacement for actual data, subject to a number of caveats.     

Analyzing passenger train arrival delays with support vector regression

We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.     

Consumer preferences and willingness to pay for advanced vehicle technology options and fuel types

The automotive industry is witnessing a revolution with the advent of advanced vehicular technologies, smart vehicle options, and fuel alternatives. However, there is very limited research on consumer preferences for such advanced vehicular technologies. The deployment and penetration of advanced vehicular technologies in the marketplace, and planning for possible market adoption scenarios, calls for the collection and analysis of consumer preference data related to these emerging technologies. This study aims to address this need, offering a detailed analysis of consumer preference for alternative fuel types and technology options using data collected in stated choice experiments conducted on a sample of consumers from six metropolitan cities in South Korea. The results indicate that there is considerable heterogeneity in consumer preferences for various smart technology options such as wireless internet, vehicle connectivity, and voice command features, but relatively less heterogeneity in the preference for smart vehicle applications such as real-time traveler information on parking and traffic conditions.     

Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures

Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in real-time and augmenting it with ITS measures such as VSL and DMS to reduce crash risk.     

Vehicle electrification in a developing country: Status and issue,from a well-to-wheel perspective

The increase of public attention, scientific research and political interest in environmental problems associated with transportation has provided the motivation for re-invention of electric vehicles. However the usage of grid-dependent EVs with a high-carbon electricity grid might produce more damage to the environment. This study aims to provide an environmental impact comparison of ICEVs, HEVs and EVs during their usage cycle, by modeling their energy consumption (electricity or fuel) and the supply chains of the supplied energy, (well-to-wheel) based on a life cycle assessment. The results show that running EVs with the existing mixed sources of electrical energy produce larger impacts on the environment 60% of the time; when compared to HEVs. When compared to ICEVs, EVs produce a larger environmental impact on 7 out of 15 environmental impact categories. Overall the environmental impacts of EVs are substantial based on the well-to-wheel analysis. It will continue to be so if no change is made to the methods of electricity generation in the near future. Given that the environmental profile of EVs is linked with the existing national electricity generation mix, the national electricity supply must be made cleaner before the electrification of the urban transport system.     

城市公交线路模态工况构建方法研究

采用GPS设备对西安城市公交典型线路的运行工况进行实测,分别运用瞬态转模态方法和多目标优化法构建出城市客车模态工况.以8个特征参数相对误差为标准,对两种方法的精度进行了对比研究.研究表明,两种方法均能有效地完成工况构建,其中多目标优化法精度最高,瞬态转模态方法简单快捷,从而为工况构建方法的选取提供了思路和数据验证.