Dynamic diurnal social taxonomy of urban environments using data from a geocoded time use activity-travel diary and point-based business establishment inventory

In this paper, we explore the diurnal dynamics of joint activity participation in a small city in Pennsylvania, USA, using behavioral data and an inventory of business establishments. We account for the variation caused by the collective impact of social, temporal and spatial choices of individuals to produce predicted space–time visualizations of activity participation. The focus is on how social contexts of an activity impact the temporal and spatial decisions regarding the activity locations and how this impact varies depending on activity types. A comparison across activity types and social interaction types is made among spatial patterns during a day. The CentreSIM dataset, which is a household-based activity diary survey collected in Centre County (Pennsylvania, USA) in 2003, provides very detailed social interaction information enabling the analysis of social, spatial and temporal aspects of activity participation. In this paper we use this information to develop a spatio-temporal interpolation method and demonstration based on kriging. In this way, we extract the dynamic social taxonomy of places from the behavioral information in the dataset and suggest how urban and transportation models can be informed from the dynamics of places by observing “what is taking place” (activities being pursued in the context of this paper) combined with “what exists” (business establishments) or “what is available” (businesses that are open). The method here can also be used to improve the design of urban environments (e.g., filling gaps in desired activity locations), manage specific places (e.g., extending the opening and closing times of businesses), study transportation policies that are sensitive to time of day (e.g., pricing of parking to discourage crowding and traffic congestion), and modeling of spatio-temporal decisions of social activities in travel demand models (e.g., to guide the development of model specification and representation of the space in which behavioral models are applied).     

A stochastic optimization approach for the selection of reflective cracking mitigation techniques

PurposeIn Hot Mix Asphalt (HMA) overlays, the existing cracks in the underlying pavements can propagate upward to the new added overlay and may cause Reflective Cracks (RC). These cracks allow water infiltration to the underlying layers and causes further moisture damage as well as weakening the unbound layers. Over the years, several methods have been developed for mitigating the RCs. This study aims to investigate the current reflective cracking mitigation methods and develop a methodology for the selection of appropriate mitigation technique. The developed model is then applied to a case study in the state of Florida.MethodTo accomplish this goal, a nationwide literature review was conducted to better understand the current in practice methods in the United States. Moreover, a life cycle cost analysis (LCCA) in five different road types was performed to find the annuity of roadway rehabilitation for each of the mitigation methods. The uncertainty in the LCCA results is represented using Exploratory Modeling and Analysis (EMA) method. Then through a Multi Criteria Decision Making (MCDM) model, a stochastic optimization model was developed to find the appropriate reflective cracking mitigation solution under Florida’s climate and road conditions, based on different cost and performance weights.ResultsBased on the available data for the state of Florida, the LCCA results indicate that the annuity of maintaining the roadway with Fabrics and ISAC are lower compared to other methods. However, the results of stochastic optimization model reveal that while looking at the performance and cost at the same time, different methods would be more feasible. For instance, while the cost of the used method does not matter at all and only performance matters, STRATA® is more probable to be the appropriate mitigation technique. The findings of this research are critical for decision makers to better understand the most cost-effective mitigation technique in different conditions.     

Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses

Real time monitoring of driver attention by computer vision techniques is a key issue in the development of advanced driver assistance systems. While past work mostly focused on structured feature-based approaches, characterized by high computational requirements, emerging technologies based on iconic classifiers recently proved to be good candidates for the implementation of accurate and real-time solutions, characterized by simplicity and automatic fast training stages.In this work the combined use of binary classifiers and iconic data reduction, based on Sanger neural networks, is proposed, detailing critical aspects related to the application of this approach to the specific problem of driving assistance. In particular it is investigated the possibility of a simplified learning stage, based on a small dictionary of poses, that makes the system almost independent from the actual user.On-board experiments demonstrate the effectiveness of the approach, even in case of noise and adverse light conditions. Moreover the system proved unexpected robustness to various categories of users, including people with beard and eyeglasses. Temporal integration of classification results, together with a partial distinction among visual distraction and fatigue effects, make the proposed technology an excellent candidate for the exploration of adaptive and user-centered applications in the automotive field.     

Incorporating the extended theory of planned behavior in a school travel mode choice model: a case study of Shaoxing,China

Travel to and from school can have social, economic, and environmental implications for students and their parents. Therefore, understanding school travel mode choice behavior is essential to find policy-oriented approaches to optimizing school travel mode share. Recent research suggests that psychological factors of parents play a significant role in school travel mode choice behavior and the Multiple Indicators and Multiple Causes (MIMIC) model has been used to test the effect of psychological constructs on mode choice behavior. However, little research has used a systematic framework of behavioral theory to organize these psychological factors and investigate their internal relationships. This paper proposes an extended theory of planned behavior (ETPB) to delve into the psychological factors caused by the effects of adults’ cognition and behavioral habits and explores the factors’ relationship paradigm. A theoretical framework of travel mode choice behavior for students in China is constructed. We established the MIMIC model that accommodates latent variables from ETPB. We found that not all the psychological latent variables have significant effects on school travel mode choice behavior, but habit can play an essential role. The results provide theoretical support for demand policies for school travel.     

A Pareto-improving hybrid rationing and pricing policy with multiclass network equilibria

This paper extends the work on Pareto-improving hybrid rationing and pricing policy for general road networks by considering heterogeneous users with different values of time. Mathematical programming models are proposed to find a multiclass Pareto-improving pure road space rationing scheme (MPI-PR) and multiclass hybrid rationing and pricing schemes (MHPI and MHPI-S). A numerical example with a multimodal network is provided for comparing both the efficiency and equity of the three proposed policies. We discover that MHPI-S can achieve the largest reduction in total system delay, MHPI can induce the least spatial inequity and MHPI-S is a progressive policy which is appealing to policy makers. Furthermore, numerical results reveal that different classes of users react differently to the same hybrid policies and multiclass Pareto-improving hybrid schemes yield less delay reduction when compared to their single-class counterparts.     

荷坳隧道四洞小净距段支护结构设计研究北大核心CSCD

多洞空间小净距隧道支护结构参数量化设计与安全性分析是当前设计中的一大难题。文章针对深圳机荷高速公路荷坳隧道四洞小净距段的设计,采用总安全系数法对四洞的开挖与支护模式、各洞的开挖顺序及支护参数进行了研究比较,并采用隧规荷载-结构法、增量法、地层-结构法对支护结构的安全系数进行了对比计算,得出以下结论:(1)提出了基于总安全系数法原理的多洞空间小净距隧道设计方法;(2)根据总安全系数法得出了荷坳隧道四洞小净距段应采用“先行洞二次衬砌完成后再开挖下一孔隧道”的开挖与支护模式,且应按洞1→洞2→洞3→洞4的开挖顺序施工;(3)隧规荷载-结构法、增量法、地层-结构法虽无法得出结构的总承载安全系数,但在一定程度上可以反应部分支护结构的安全性。     

基于湿度应力场理论的膨胀土边坡稳定分析

边坡土体中的膨胀力是导致膨胀土边坡失稳的主要原因之一。文章基于湿度应力场理论,采用ANSYS软件的热传导分析功能模拟分析边坡的降雨入渗以及膨胀变形,并采用有限元强度折减法对不同条件下边坡安全系数进行计算,分析了膨胀土边坡稳定规律,为膨胀土边坡处治提供理论依据。     

You (expect to) get what you pay for: A system approach to delay, fare, and complaints

In this paper, an analytical framework integrating delay, fare, and complaints with passenger air travel has been laid out. Examining aggregate monthly data for US domestic air travel, we have identified causal relationships among fare, complaints, and levels of delay. An analytical framework is proposed that formalizes these relationships in an integrated manner. This integrated framework is then estimated in a set of simultaneous equations by using 118 months of data from January 1997 to October 2006. Results show that complaints are influenced by levels of delays. However, complaints are positively influenced by average yield. These findings lead us to support the central hypothesis that complaints are responsive to levels of delays, but they tend to vary according to fare. That is, air travelers are less likely to complain in return for lower fares, even when faced with the same or even higher levels of delays. These findings have important policy implications, including the passengers’ bill of rights and regulator’s choice between market and operational performances.     

埠阳庄大桥复杂地质条件下的桩基施工

埠阳庄大桥实际地质状况复杂,重点介绍该桥桩基施工中的相关准备工作以及对问题的分析和处理措施,为同类地质条件下梁桩基施工提供借鉴经验。     

橡胶粉改性MAC沥青混合料路用性能的研究

主要讨论了橡胶粉在MAC沥青混合料中的应用,研究了橡胶粉干法改性沥青混合料高温性能、抗水损害性能及混合料的抗飞散性能,证明了其优良的路用价值。