Problem Statement. School bus fleets are aging, and our communities have poor air quality. Replacing school buses with zero emission school buses will address both of these issues.
Problem Statement. 1.1. This section, Problem Statement, is included for informational and contextual purposes to support the Network Communication Policy section.
1.2. The predicted large scale growth of IoT devices will create major challenges for mobile network operators. One major challenge that mobile network operators must overcome is the risk caused by the mass deployment of inefficient, insecure or defective IoT devices on the mobile network operators’ [domestic and roaming] networks. When deployed on a mass scale such devices can cause network signaling traffic to increase exponentially which impacts network services for all users of the mobile network. In the worst cases the mass deployment of such IoT devices can disable a mobile network completely.
1.3. IoT devices overusing the mobile network can affect not only the devices causing the incident but also other devices on the same IoT service platform or those devices of other end customers.
1.4. Network signaling resources are dimensioned assuming an overall device usage profile with a sensible balance between traffic and signaling needs. It is therefore important that IoT devices using mobile networks adhere to some basic principles before they can be safely connected to mobile networks.
1.5. Good design is essential to ensure that IoT Device performance is optimized and to prevent failure mechanisms creating runaway situations which may result in network overload.
Problem Statement. Given the preliminary tests performed above, the problems of dual-loop data could be summarized as follows: • The total volume of vehicles detected and assigned to bins by the dual-loop system for a given time interval was consistently lower than the total volume of vehicles detected by the corresponding single loops for the same interval. In other words, the volume of classified vehicles was consistently lower than the volume of actual vehicles detected, reflecting vehicles that were dropped by the dual-loop system during the classification step. • The existing dual-loop detector systems had problems measuring vehicle lengths; hence the vehicle bin volume distribution for any time period might differ significantly from the actual distribution. Therefore, the detected trucking information did not reflect the true trucking activities during that period at that location. This study quantitatively evaluated the accuracy of WSDOT dual-loop data using video ground truth data and investigated the types and potential causes of dual-loop data inaccuracies. It also sought and recommended methods to improve the quality of dual- loop data. The objectives of the research are summarized below: • Identify dual-loop detectors in the FLOW system in good working condition to serve as representatives of WSDOT dual-loop detectors for analysis. • Quantitatively evaluate the accuracy of the sample dual-loop measurements of vehicle volumes and vehicle classifications. • Identify and interpret the potential causes of dual-loop data inaccuracies. • Recommend strategies for improving dual-loop data quality. Due to heavy weights and large turning radii, truck movements have characteristics very different from those of passenger cars and smaller vehicles. These differences make the collection of reliable and continuous vehicle classification data and truck volume data very important for reliable freeway performance monitoring. Of course, such data are critical to the specific monitoring of regional freight movements. To date, several different technologies have been used for vehicle detection and classification. These include single-loop detectors, dual-loop detectors, classification with vehicle acoustic signatures, video imaging systems, and laser and night vision systems. Each of these technologies has its own advantages and disadvantages. Dual-loop detectors are often used to collect vehicle classification data. As previously described, a dual-loop detector consists of two conse...
Problem Statement. As Xxxxx has pointed out in its project proposal, the worldwide bank of blowing agents contained in foams was estimated to exceed 11 billion tons CO2-eq in 2002 and is likely to remain above 9 billion tons CO2-eq in 2015 under most business-as-usual scenarios. However, following the phase-out of the more emissive foam applications that were still using CFCs in the late 1990s, the emissions from foam banks are expected to settle in the range of 180 million tons CO2-eq annually over the next few decades – i.e., 2% of banked quantities per year. This means that losses from foams could continue well into the future – perhaps for in excess of 100 years – particularly if some of those foams are land-filled. However, because the annual baseline loss rate is relatively low, attention typically switches to preventing emissions from more emissive banks – e.g., refrigerants, where loss rates are often well in excess of 20% of banked quantities annually. This trend persists despite the fact that the foam banks are larger overall. It reflects the fact that measures can be more cost-effective and easier to introduce when preventing refrigerant emissions. Nevertheless, the opportunity for the mitigation of emissions from foams remains highly significant, particularly at end-of-life. Based on the US EPA model data, a 5 billion metric ton CO2-eq bank from ODS/HFC foam sources could be estimated for the USA in 2005, with annual emissions in the 1 The U.S. EPA’s Vintaging Model was developed as a tool for estimating the annual chemical emissions from industrial sectors that have historically used ODSs such as CFCs, HCFCs, and halons in their products. The Vintaging Model also estimates emissions from ODS substitutes such as HFCs. The model name refers to the fact that it tracks the use and emissions of annual “vintages” of equipment that enter service or are disposed in each of several end- uses that make up an industrial sector (Xxxxxx, 2003). region of 92 million metric tons CO2-eq. By applying a population factor of 12.8% for California, based on the US census, the California ODS/HFC banks could be estimated at 640 million tons CO2-eq with 12 million tons CO2-eq being emitted per annum from ODS and HFC foam sources (US EPA, 2005). Original Air Resources Board estimates in 2007 noted that approximately 60% of the total bank of high-global warming potential greenhouse gases is from foam sources, with most of the remaining banks from refrigerants. CARB estimated that the foa...
Problem Statement. Decision support increasingly requires the ability to manage, combine and use information from heterogeneous sources. A Framework for decision support based on four pillars related to semantic interoperability, GIS interoperabil- ity, P2P architectures, and query processing. In the following, the main characteristics and issues of the four pillars is presented. Information heterogeneity can occur at the syntactic, structural and se- mantic level. Semantic interoperability is more acute in dynamic and au- tonomous environments, due to the lack of relationships among sources. Se- mantic interoperability is essentially based on having a common understand- ing of the meaning of the information exchanged by di erent sources. It's a multi-level problem that can occur in data sources, fromats or models. To exchange information and share computational geo-data resources among heterogeneous systems, conversion tools are usually developed to transfer data from one format to other format. GIS composed of image and traditional database, it required geographic data exhibits complex structure, large size, and complex semantics. P2P can provide infrastructure for dynamic environment in which au- tonomous and independent peers can joint or leave the network easily and frequently. However in a diverse and large community, it is hard to discover relevant data for decision making. P2P allow to develop community which share common interest, so that large community will be 'clustered' based on the same interest. Then searching relevant data can be easier. Consider the fragments of export schema of the two provider peers shown in gure 1.1. One of the provider peer is a toll collection company that views the shared transportation network as a point to point network. This provider peer is interested in land transportation network characteristics related to the nature and type of the road ( gure 1.1b). Another provider peer PP 2, a design and development company, requires detail road information such as size, width, length and tra c capacity. The two provider peers generate di erent representations of the same ontology concepts. Refer to above issues, the research focuses on some questions as follow:
Problem Statement. In the past two decades, TSP systems have been deployed in many cities worldwide. However, enthusiasm for TSP in North America has been tempered with concerns that overall traffic performance may be unduly compromised when signal timing plans intended to optimize traffic flow are overridden to provide a travel advantage to transit vehicles (Xxxxx and Xxxxxxxxxxxxxx 2003). Several recent studies (see, for example, Xxxxxxxx et al. 2002, and Xxxx et al. 2002) have quantitatively evaluated the effects of TSP. While these studies have generally agreed on the benefits for transit operations, the overall impacts of TSP on local traffic networks remain unclear. Also, because the performance of a signal control strategy is closely related to traffic conditions, surrounding land use, traffic regulations, and roadway network geometry, the One involved four intersections on SW 164th Street in south Snohomish County. Phase Two covered 13 intersections on SR 99 in the City of Lynnwood. This report summarizes both the Phase One and Phase Two evaluations.
Problem Statement. Medium-duty and heavy-duty (MD/HD) vehicles represent a small share of California registered vehicle stock, accounting for about one million out of 31 million vehicles, or 3 percent; however, this small number of vehicles is responsible for about 23 percent of on-road greenhouse gas (GHG) emissions in the state because of comparatively low fuel efficiency and the high number of miles traveled per year. MD/HD vehicles additionally account for nearly 60 percent of Nitrogen Oxides (NOX) and 52 percent of Particulate Matter (2.5 Micrometers and smaller) (PM2.5) emissions from on-road transportation in California. For these reasons, MD/HD vehicles represent a significant opportunity to reduce GHG emissions and criteria emissions while focusing on a small number of vehicles. In response, California has led the nation in the development of projects incentivizing the adoption of MD/HD advanced vehicle technologies. Since 2010, the state has invested $530 million in such projects, resulting in the deployment of more than 9,000 new clean vehicles on California’s roads. Critical barriers remain, however, that threaten to slow the pace of clean vehicle adoption. Foremost among these are the high cost of zero-emission vehicle (XXX) infrastructure, the relative scarcity of public incentives for such infrastructure, and a significant knowledge gap among fleet owners about XXX infrastructure technology, permitting, and installation. The consequences of these barriers are magnified in many of the areas most in need of the improvements in air quality—areas categorized as disadvantaged, low-income, and tribal communities—which often suffer from poverty, unemployment, and lower educational attainment.
Problem Statement. Since its introduction in the early 1960s, the inductance loop detector has become the most popular form of vehicle detection system (ITE, 1997). Many freeway corridors contain single-loop detectors for collecting volume (the number of vehicles passing per unit time) and lane occupancy (the fraction of some total time interval that a loop is occupied by vehicles) data. These data are valuable sources for transportation planning and traffic operations. However, recent developments in ATMS require increasingly more accurate and timely speed and vehicle-classification data, which are not directly measurable by single-loop detectors. To obtain such speed and vehicle-classification data, dual-loop detectors are typically employed. A dual-loop detector is formed by two consecutive single-loop detectors separated by several meters. It is also called a speed trap or double-loop detector. Because a dual- loop detector is capable of recording the time for a vehicle to traverse from the first loop to the second loop, and the distance between the two loops is predetermined, a dual-loop detector can calculate the speed of a vehicle fairly accurately. By applying the calculated speed and single-loop measured lane occupancies, the length of a vehicle can also be estimated, and the vehicle can be assigned to a certain class on the basis of its length. However, although dual-loop detectors are ideal for collecting speed and vehicle- classification data, there are too few of them on our current freeway systems to meet practical ATMS and ATIS needs, and the cost of upgrading a single-loop detector to a dual-loop detector is high. According to the experience of the WSDOT, the cost for upgrading from a single-loop detector to a dual-loop detector ranges from $3250 to $5750 (includes $750 direct cost for loop placement and $2500 - $5000 indirect cost caused by lane closure) (Xxxx and Nihan, 2003). In addition, most dual-loop detectors deployed in the greater Seattle area are reported to have serious under-count or over- count problems for bin volumes (Xxxxx et al., 2003). Therefore, making existing single- loop detectors capable of providing better speed and vehicle-classification data is of practical significance for traffic researchers. To meet ATMS and ATIS needs, new sensors that are capable of collecting speed and truck volume data have been developed in recent years. Among these new sensors, video image processors (VIPs) are noteworthy. These systems offer the advantage o...
Problem Statement. The RFA outlines the main problems in Ghana’s fisheries sector and establishes that the sector is in crisis—especially with the collapse of the small pelagic fisheries. Over the past decade, more than 100,000MT of high quality low-cost animal protein that was traditionally available to poor and vulnerable coastal and inland households has been lost. Local demand for fish outstrips supply, increasing the pressure on already overexploited fish stocks. With open access fisheries, overcapacity among fishing fleets, and little or no fisheries management controls or effective enforcement of regulations resulting in rampant IUU fishing, individual fishermen and women are losing economic ground while regional and national food insecurity increases. A weak institutional framework limits the ability to implement strong co-management and use rights. Meanwhile, the low added-value of fish processed locally keeps fishing households poor, and less likely and less able to change behavior or engage in more sustainable practices. Mangrove ecosystems—essential fish nurseries for demersal fisheries—are threatened by extensive cutting and habitat alteration. Endangered, threatened and protected species such as sea turtles, marine mammals and basking sharks are part of the by-catch of many fishermen. Unfortunately, there are no MPAs within Ghana to help preserve this biodiversity and protect endangered species. High poverty rates among fishing communities lead families to give up their children to child labor, a problem likely to increase as the small pelagic fishery collapses, in particular in the Central Region. Yet, few other options exist. Fishing settlement areas are particularly vulnerable to climate variability and change due to rising sea levels, increased severity of flooding and high uncertainty about the effects of elevated sea surface temperatures and ocean acidification on the productivity of the marine ecosystems, and potential changes in migration patterns of commercially important fish. Add to this that local communities are unable to produce food locally due to land use changes that are virtually wiping out areas available for local food growing on prime soils. The end result is severely vulnerable coastal households and communities with weak adaptive capacity, and high exposure to climate impacts. Still, there are reasons for optimism. Key enabling conditions are reaching thresholds that favor change. The USAID ICFG Project led by CRC and ongoing investments ...
Problem Statement. We seek to approximate the following trajectory optimiza- tion problem in a numerically tractable way: quires solving a differential equation interaction model and collision checking in continuous time, necessitating a careful {uy}y∈Y J(y, xy, uy) (3a) choice of representation for practical implementation.
