The costs and benefits of reducing SO2 emissions from ships in the US West Coastal waters

Potential costs and benefits of policy options for reducing offshore ship pollution are examined using a meta-analysis of studies synthesized regionally for the US West Coast. Net benefits of reducing SO₂ emissions from cargo ships in the US West Coast waters are found to range between $98 million and $284 million, annually; the benefit–cost ratio varies between 1.8 and 3.36, depending on the size of the control area and the sulfur content limit. The results show that about 21,000 tons of on-land equivalent SO₂ emissions or about 33% of SO₂ emissions from all mobile sources in California in 2005 can be reduced annually if the US West Coast exclusive economic zone is designated as an International Maritime Organization-compliant SO_x emission control area (SECA) with fuel-sulfur content not exceeding 1.5%. The analysis demonstrates that designating this area reduces more emissions than establishing a smaller zone at a lower but favorable benefit-cost ratio. Control measures that require 0.5% low-sulfur fuels reduce more SO₂ emissions, and also may have higher net benefits. Technological alternatives may achieve benefits of emissions reductions on the US West Coast across higher ranges of potential fuel prices. Combinations of fuel switching and control technology strategies provide the most cost-effective benefits from SECAs on the US West Coast and other world regions.     

Decomposing travel times measured by probe-based traffic monitoring systems to individual road segments

In probe-based traffic monitoring systems, traffic conditions can be inferred based on the position data of a set of periodically polled probe vehicles. In such systems, the two consecutive polled positions do not necessarily correspond to the end points of individual links. Obtaining estimates of travel time at the individual link level requires the total traversal time (which is equal to the polling interval duration) be decomposed. This paper presents an algorithm for solving the problem of decomposing the traversal time to times taken to traverse individual road segments on the route. The proposed algorithm assumes minimal information about the network, namely network topography (i.e. links and nodes) and the free flow speed of each link. Unlike existing deterministic methods, the proposed solution algorithm defines a likelihood function that is maximized to solve for the most likely travel time for each road segment on the traversed route. The proposed scheme is evaluated using simulated data and compared to a benchmark deterministic method. The evaluation results suggest that the proposed method outperforms the bench mark method and on average improves the accuracy of the estimated link travel times by up to 90%.     

城市轨道交通iAFC方案研究

移动支付、语音购票、刷脸过闸等技术是城市轨道交通AFC(自动售检票系统)未来发展的方向。本文针对城市轨道交通领域移动支付技术的发展和应用,结合徐州地铁iAFC(互联网售检票系统)建设方案,对iAFC系统架构、业务场景、接口设计等方面进行阐述,对类似新建或改造AFC项目提供借鉴。     

预应力空心板先张法钢绞线整体式张拉施工工艺

介绍了预应力空心板钢绞线整体式张拉及放张的施工工艺、操作要点及操作工序,以及在操作过程中须注意的一些问题。     

面向地下停车场的轻量级目标检测算法研究

基于深度学习的目标检测算法能够取得良好的检测速度离不开高性能GPU硬件设备的支持。然而,在智能车中搭载高性能、高功耗、大尺寸的硬件设备与汽车的长续航理念不符。因此,文章以YOLOv3目标检测算法为基线模型进行改进,提出轻量化的目标检测模型Mobile-YOLO,并在采集制作的地下停车场数据集中进行训练测试。实验结果表明,提出了Mobile-YOLO相较于YOLOv3,在平均精度均值略微提升的情况下,检测速度提升了47.1%。在移动端平台TX2上每秒能够检测31张图像。     

长途跨线高速列车直达与中转选择问题

我国快速客运网基本形成,长途跨线列车直达与中转选择对于高速列车开行方案设计至关重要。为提升长途跨线高速列车开行效益和服务品质,按长途跨线列车直达与中转方式对比的思路,以旅客出行需求、基础设施能力、移动设备能力等多方面因素对长途跨线列车开行方式的影响为约束条件,研究长途跨线列车直达与中转选择模型及求解方法。选取2025年15个长途跨线起讫点为对象进行案例研究,提出D站—E站、F站—D站等9个起迄点之间开行长途跨线直达列车的建议方案,研究表明直达与中转换乘选择模型能够解决长途跨线列车开行方案编制问题。     

Effects of income inequality on evacuation,reentry and segregation after disasters

Large-scale disasters often trigger mass evacuation due to significant damages to urban systems. Understanding the evacuation and reentry (return) process of affected individuals is crucial for disaster management. Moreover, measuring the heterogeneity in the individuals' post-disaster behavior with respect to their socio-economic characteristics is essential for policy making. Recent studies have used large-scale location datasets collected from mobile devices to analyze post-disaster mobility patterns. Despite the availability of such data and the societal importance of the problem, no studies have focused on how income inequality affects the equity in post-disaster mobility. To overcome these research gaps, we overlay mobility data with income information from census to quantify the effects of income inequality on evacuation and reentry behavior after disasters, and the resulting spatial income segregation. Spatio-temporal analysis using location data of more than 1.7 million mobile phone users from Florida affected by Hurricane Irma reveal significant effects of income inequality on evacuation behavior. Evacuees with higher income were more likely to evacuate from affected areas and reach safer locations with less damage on housing and infrastructure. These differences were common among evacuees from both inside and outside mandatory evacuation zones. As a result of such effects of inequality, significant spatial income segregation was observed in the affected areas. Insights on the effects of income inequality on post-disaster mobility and spatial segregation could contribute to policies that better address social equity in pre-disaster preparation and post-disaster relief.     

Road vehicle emission inventory of a Brazilian metropolitan area and insights for other emerging economies

The vehicle fleet in the Ceará state has grown 180% over the last ten years. The growth of the resulting emissions is unknown in view of the expansion of this fleet in the greater Fortaleza Metropolitan Area (FMA). The largest fleet in the FMA is in the Fortaleza city itself, where flex fuel vehicles predominate (∼30%). Flex fuel motorcycles increased significantly (greater than 800%) between 2010 and 2015. This paper aims to estimate the road vehicle emissions of carbon monoxide (CO), non-methane hydrocarbons (NMHC), aldehydes (RCHO), nitrogen oxides (NO_x), and particulate matter (PM) from the main road vehicle fleets of Fortaleza and its metropolitan area using a macrosimulation, bottom-up method, between 2010 and 2015. The results showed that road vehicle emissions of CO, NMHC and RCHO increased mainly by Otto cycle vehicles increase due to the introduction of flex fuel vehicles; however, the NO_x and PM emissions noticeable reduction is also a result of emission policies that seed the introduction of new technologies. In 2015, more than 70,000 tons of CO (21.2 ton/1000person), 8000 tons of NMHC (2.5 ton/1000person), 290 tons of RCHO (0.09 ton/1000person), 15,000 tons of NO_x (4.4 ton/1000person) and 600 tons of PM (0.2 ton/1000person) were emitted in the region under study. Comparing with other Brazilian regions, FMA emit higher levels of pollutants per inhabitant than the state of São Paulo and the state of Rio de Janeiro but lower levels than Porto Alegre city.     

The carbon footprint of business travel in the knowledge-intensive service sector

We explore the travel needs and patterns, and the corresponding carbon footprint, of small service organizations during different phases of knowledge-intensive business processes, and compare the results with the priorities given to travel-related goals by staff. We apply a combination of focus group data, mobile positioning, and individual follow-up interviews as study methods. The need for physical travel is determined by a combination of the perceived potential for knowledge creation and transfer offered by each trip, the strength of interpersonal relationships in business networks, and the significance of the travel goal in terms of economic sustainability. The priorities given to travel goals reflect the environmental load of business travel only in domestic contexts, where executing core business processes accounted for the highest carbon footprint. We propose the ways in which the management of business interactions could take into account sociotechnical environment and social recognition of low-carbon communication and travel modes.     

基于云服务的交通量计算系统的设计

为了提高交通量数据采集与计算能力,基于云服务设计了一个交通量计算系统,集中处理全市交通量数据.应用了Windows Mobile智能设备开发技术编写了手机端交通量计数器程序,通过3G网络实现了交通量原始数据的上传.在云服务中心,应用了Excel VBA二次开发技术生成了各种统计图表,调查员在工作中,随时可下载统计图,了解交叉口的实时交通流量、流向等数据.实践证明,该交通量计算系统扩展性良好,能应对大城市交通量计算需求,能够推进智慧交通、智慧城市的建设.