A competing Markov model for cracking prediction on civil structures

Cracks on the surface of civil structures (e.g. pavement sections, concrete structures) progress in several formations and under different deterioration mechanisms. In monitoring practice, it is often that cracking type with its worst damage level is selected as a representative condition state, while other cracking types and their damage levels are neglected in records, remaining as hidden information. Therefore, the practice in monitoring has a potential to conceal with a bias selection process, which possibly result in not optimal intervention strategies. In overcoming these problems, our paper presents a non-homogeneous Markov hazard model, with competing hazard rates. Cracking condition states are classified in three types (longitudinal crack, horizontal crack, and alligator crack), with three respective damage levels. The dynamic selection of cracking condition states are undergone a competing process of cracking types and damage levels. We apply a numerical solution using Bayesian estimation and Markov Chain Monte Carlo method to solve the problem of high-order integration of complete likelihood function. An empirical study on a data-set of Japanese pavement system is presented to demonstrate the applicability and contribution of the model.     

Lights,camera, legal action! The effectiveness of red light cameras on collisions in Los Angeles

This study estimates the effect of red light cameras (henceforth cameras) on collisions under the Los Angeles Automated Photo Enforcement Program that ran from 2006 to 2011. To control for selection bias and unobservables, a data set is constructed such that intersections with cameras are compared to control groups of nearby intersections without cameras, matched on observable characteristics. To capture potential spillover effects of cameras, control groups at various distances from the intersections with cameras are considered. A Poisson panel data model with random coefficients is applied to these data and estimated using Bayesian methods. The program suffered from weaknesses in enforcement. The city’s courts did not uphold citations and this dampened the effect cameras had on drivers. These problems are accounted for in modeling. Controlling for these concerns, results indicate that the cameras decreased red light running related collisions, but increased right-angle and injury collisions, as well as collisions overall.     

Projected prevalence of car-sharing in four Asian-Pacific countries in 2030: What the experts think

This paper presents a “big-picture view” for policymakers and related stakeholders regarding the future development of car-sharing services. Car-sharing has the potential to significantly disrupt the personal mobility market. Thus, understanding their market penetration and implications is urgently needed. Previous studies in this domain have predominantly focused on the views, opinions, and preferences of consumers. In this study, we complement the current demand modelling research on car-sharing by applying an expert elicitation and aggregation technique that relies on transport experts’ opinions to investigate the role of car-sharing in the future. Specifically, based on the opinions of mobility suppliers, this research elicits experts’ judgment from across government, industry, and academia to gain insights into the future of car-sharing markets in four countries – Australia, Malaysia, Indonesia, and Thailand. The analysis reveals that, from a mobility supplier’s perspective, energy and vehicle prices will not have a statistically significant impact on the future adoption of car-sharing. The results also show that the more knowledgeable an expert is, the more pessimistic they are about the market penetration of car-sharing in 2016, and the more optimistic they are about the prevalence of car-sharing in 2030.     

基于T—S模糊神经网络的边坡稳定性评价

边坡的稳定具有模糊性。基于模糊数学和神经网络理论,建立了边坡分定性分析的T—S模型,并结合具体工程进行了应用。研究表明：T—S模糊神经网络分析模型具有较好的适应性和应用性,对于边坡稳定性分析是一种较好的方法。     

Coloured Petri net-based traffic collision avoidance system encounter model for the analysis of potential induced collisions

The Traffic Alert and Collision Avoidance System (TCAS) is a world-wide accepted last-resort means of reducing the probability and frequency of mid-air collisions between aircraft. Unfortunately, it is widely known that in congested airspace, the use of the TCAS may actually lead to induced collisions. Therefore, further research regarding TCAS logic is required. In this paper, an encounter model is formalised to identify all of the potential collision scenarios that can be induced by a resolution advisory that was generated previously by the TCAS without considering the downstream consequences in the surrounding traffic. The existing encounter models focus on checking and validating the potential collisions between trajectories of a specific scenario. In contrast, the innovative approach described in this paper concentrates on quantitative analysis of the different induced collision scenarios that could be reached for a given initial trajectory and a rough specification of the surrounding traffic. This approach provides valuable information at the operational level. Furthermore, the proposed encounter model can be used as a test-bed to evaluate future TCAS logic changes to mitigate potential induced collisions in hot spot volumes. In addition, the encounter model is described by means of the coloured Petri net (CPN) formalism. The resulting state space provides a deep understanding of the cause-and-effect relationship that each TCAS action proposed to avoid an actual collision with a potential new collision in the surrounding traffic. Quantitative simulation results are conducted to validate the proposed encounter model, and the resulting collision scenarios are summarised as valuable information for future Air Traffic Management (ATM) systems.     

Modeling time-of-day car use behavior: A Bayesian network approach

In this research, a Bayesian network (BN) approach is proposed to model the car use behavior of drivers by time of day and to analyze its relationship with driver and car characteristics. The proposed BN model can be categorized as a tree-augmented naive (TAN) Bayesian network. A latent class variable is included in this model to describe the unobserved heterogeneity of drivers. Both the structure and the parameters are learned from the dataset, which is extracted from GPS data collected in Toyota City, Japan. Based on inferences and evidence sensitivity analysis using the estimated TAN model, the effects of each single observed characteristic on car use measures are tested and found to be significant. The features of each category of the latent class are also analyzed. By testing the effect of each car use measure on every other measure, it is found that the correlations between car use measures are significant and should be considered in modeling car use behavior.     

基于模糊时间Petri网的列车运行时间不确定性问题的处理

定义一种应用于铁路列车运行系统的模糊时间Petri网；针对列车运行时间存在的不确定性，该Petri网引入了4个模糊集理论函数：模糊时间片、模糊使能时间、模糊发生时间和模糊延迟，来处理时间的不确定性问题；能够对列车运行过程中的时间不确定性问题进行定量分析，可以有效应用于列车交会、列车终到时间、列车运行计划调整的分析等；其相对于已有的方法具有精确分析、计算简单、简化系统、便于系统集成的特点。     

公路工程项目最低评标价法

介绍了公路工程项目招投标中最低评标价法的概念，对最低评标价法的应用范围，应用最低评标价法时履约担保中的现金担保是否应该提交，以及现金担保应该由谁来保管等问题进行了探讨。     

挂网客土喷播技术在西汉高速公路上的应用

介绍了京昆高速公路西安至汉中段在边坡防护绿化中采用的挂网喷播技术,总结了该项技术的基本原理、技术特点,并根据实际情况对喷播厚度进行了分析,筛选了适合秦岭北麓的喷播草种,以期推动高速公路向"生态高速公路"发展。