Formulating and solving the network design problem by decomposition

The optimal transportation network design problem is formulated as a convex nonlinear programming problem and a solution method based on standard traffic assignment algorithms is presented. The technique can deal with network improvements which introduce new links, which increase the capacity of existing links, or which decrease the free-flow (uncongested) travel time on existing links (with or without simultaneously increasing link capacity). Preliminary computational experience with the method demonstrates that it is capable of solving very large problems with reasonable amounts of computer time.     

Stakeholder Collaboration and Organizational Innovation in the Planning of the Deschutes Estuary Feasibility Study

Coastal managers have sought to enhance the collaborative inputs of stakeholder groups into management activities. Nonetheless, established organizational approaches have led to primarily consultative forms of engagement and constrained citizen involvement in formative activities. In Olympia, Washington, managers overseeing the Deschutes Estuary Feasibility Study (DEFS) implemented an innovative cooperative research planning initiative that diverged from conventional consultative processes. Stakeholders, rather than government officials, identified the research priorities for the socioeconomic component of this restoration feasibility study. This design method altered the traditional roles and responsibilities of different organizational actors, and the involvement of citizen groups in these formative activities changed the relationship between governmental and nongovernmental actors. Using conceptual frameworks from organizational sociology, this study develops insights into the behavior of the organizations involved with the DEFS cooperative planning effort, demonstrating how engaging stakeholders in formative research planning activities may foster new types of collaboration among coastal management organizations.     

Extracting patterns from Twitter to promote biking

Emphasis on non-motorized travel modes (for example, biking) reduces motorized trips and provides positive effects on the environment and the quality of human life. Understanding factors that influence people to biking or bike commuting can help decision makers, transportation planners, and bike commuting networks. Historically, conventional methods like surveys and crash data analyses were conducted to understand relevant factors. Survey and crash data analysis are difficult to perform in broad scale due to data availability and efforts. An innovative approach to determining these factors is to conduct social media mining to understand sentiments or motivations of bike commuters. People use terms (with hashtag at the beginning of the term) in Twitter, a popular social media network, to express their thoughts, activities or information. This study developed a framework for using Twitter data in understating the sentiments of the bikers with minimal effort. In this study, Twitter data associated with bike commuting hashtags were obtained for eight years (2009–2016). This study provided a framework of data collection and application of various natural language processing (NLP) tools (for example, text mining, sentiment analysis) to extract knowledge from the unstructured text data. Findings show that biking is associated with weather and seasonal patterns. The general sentiment towards biking is positive. However, negative sentiments are associated with bad weather, crime, and other challenges. The polarity scores indicate somewhat positiveness in the recent few years. The developed framework and the findings of this study will help planners and decision makers to promote biking on a broader scale.     

GIS and network models: Issues for three potential applications

Geographic Information Systems (GISs) are now being developed for urban transportation planning and modeling. Supporting other recent work, this paper demonstrates the combination of GIS and network-based, urban transportation planning (UTP) modeling software to create powerful tools for the analysis of policies and plans. Following a literature review and informal survey identifying GIS and UTP modeling software in use at transportation agencies, three applications of GIS for network modeling and relevant issues are discussed: 1) developing or modifying UTP models, 2) incorporating network data into a GIS framework, and 3) creating and using a high-speed interactive system suitable for providing near real-time alternatives and policy analysis. Some specific suggestions are made related to the use of two software products, and extensions to other platforms are noted. Conclusions allude to the potential of such systems while realistically pointing to the present difficulties and magnitude of effort that will be required.     

Multi-impact optimization to reduce aviation noise and emissions

This paper describes a multi-indicator assessment and minimization problem focused on aviation “Community” noise. The model explores a combined noise and emission objective for airfreight movements at Luxembourg’s Findel Airport. Community noise is evaluated via four population impact indicators emissions are tabulated from in-flight segments in the proximity of the airport and from the contribution of taxiing. A set of scenarios based on trajectory, aircraft scheduling, fleet composition, operational procedures are implemented in software. An integer programming methodology is used to search for minimal environmental impact. An on/off switch allows for an evaluation of each indicator in the objective. Different impacts on the population are explored in a case study involving the Cargolux Airlines International S.A.     

有利于行人头保护的风挡玻璃下横梁结构优化设计与试验验证

基于某车型的风挡玻璃下横梁结构,针对提高行人头保护性能进行了优化设计.结果表明,优化后的风挡玻璃下横梁可以提供更大的变形吸能空间,降低加速度峰值和HIC值,提高行人保护性能,为车型行人保护性能评价和开发改进提供借鉴.     

Impacts of built environment and emerging green technologies on daily transportation greenhouse gas emissions in Quebec cities: a disaggregate modeling approach

This paper aims to investigate the impact of the built environment (BE) and emerging transit and car technologies on household transport-related greenhouse gas emissions (GHGs) across three urban regions. Trip-level GHG emissions are first estimated by combining different data sources such as origin–destination (OD) surveys, vehicle fleet fuel consumption rates, and transit ridership data. BE indicators for the different urban regions are generated for each household and the impact of neighborhood typologies is derived based on these indicators. A traditional ordinary least square (OLS) regression approach is then used to investigate the direct association between the BE indicators, socio-demographics, and household GHGs. The effect of neighborhood typologies on GHGs is explored using both OLS and a simultaneous equation modeling approach. Once the best models are determined for each urban region, the potential impact of BE is determined through elasticities and compared with the impact of technological improvements. For this, various fuel efficiency scenarios are formulated and the reductions on household GHGs are determined. Once the potential impact of green transit and car technologies is determined, the results are compared to those related to BE initiatives. Among other results, it is found that BE attributes have a statistically significant effect on GHGs. However, the elasticities are very small, as reported in several previous studies. For instance, a 10 % increase in population density will result in 3.5, 1.5 and 1.4 % reduction in Montreal, Quebec and Sherbrooke, respectively. It is also important to highlight the significant variation of household GHGs among neighborhoods in the same city, variation which is much greater than among cities. In the short term, improvements on the private passenger vehicle fleet are expected to be much more significant than BE and green transit technologies. However, the combined effect of BE strategies and private-motor vehicle technological improvement would result in more significant GHGs reductions in the long term.     

安全性通过认证

人们常说，过去数十年来，亚洲发展中国家已成为世界的工厂。近几年尤其是从经济危机开始以来，恰恰就是这些国家已开始被公认为世界上发展最快的经济体和某些前景最为人们看好的外国投资地点。这种投资的流入与期待同至。当今注入亚洲急速增长经济的投资不再仅着眼于廉价劳动力，而且还着眼于未来（中期乃至长期）的国内潜力。     

Factors Affecting Disaster Preparedness,Response, and Recovery Using the Community Capitals Framework

Abstract

Disaster research often focuses on how and why communities are affected by a discrete extreme event. We used the community capitals framework to understand how community characteristics influence their preparedness, response to, and recovery from successive or multiple disasters using the 1964 Good Friday Earthquake and the 1989 Exxon Valdez Oil Spill as case studies. This study assesses community response to these disasters by reviewing published literature on impacts to create profiles for six communities and by identifying community capitals before and during these disasters, and throughout the long-term recovery. While the presence of rich natural capitals commonly contributed resources to pre-disaster planning and long-term recovery, restriction of resource access immediately following the disasters was detrimental to many communities. Communities with strong political, social, and financial capitals tended to fare better immediately following disasters, enabling longer-term processes of transformation or recovery. However, in some communities the oil spill undermined these capitals more than the earthquake and resulting tsunami. In understanding how use and reliance on community capitals can lead to varied recovery success from different kinds of disasters, these findings can help coastal managers and planners prepare for future disasters.