Kathmandu Valley (approximately 570 km 2 ) is the largest city and the capital of Nepal. In 1991, it had a population of 1.1 million. The population rose to nearly 2.5 million in 2011, with a population density of 4,386/km 2 . This is about 1.6 times the population density of an average Canadian urbanized area, which is 2,656/km 2 . The transportation sector is the largest source of air pollution in the valley. In 1990, there were 40,133 vehicles in the valley. This number reached 330,336 in 2011, an increase of 8-fold within 20 years. Recent data showed that there are 15,008 diesel and 315,328 gasoline vehicles. This paper determines the amount of carbon dioxide (CO 2 ) emitted by these vehicles. Vehicle data were collected from the Department of Motor Vehicles, Kathmandu, Nepal. The emission rate of each type of vehicle was collected from the vehicle manufacturers. The study showed that Kathmandu Valley had highest level of pollution in Nepal. The CO 2 emission from each type of vehicle also was calculated. Recommendations regarding improving the air quality of the valley are presented.

In 2012, 775 fatalities were recorded, and many more were injured at construction sites in the United States. Of these, 415 fatalities (54%) were due to fall, slips, and trips as well as being struck by falling objects. In order to decrease fatalities at construction sites to these types of events, the Occupational Safety and Health Administration (OSHA) provides Fall Prevention and OSHA-10 trainings to construction workers. Moreover, safety personnel monitor whether the workers use personal protective equipment (PPE) properly. Data shows that construction fatalities have decreased by 2% annually since 1994; however, the owners still are not satisfied with this result. Various studies have shown that fall is the biggest contributor for construction fatalities. One study showed that half of the fall fatalities were because the workers either had not used PPEs or had not used them properly. In addition, studies showed that, with proper use of hard hats, the fatalities due to fall, slips, trips, and being struck by falling objects could be reduced. This study developed and tested a hard-hat detection tool that uses image-processing techniques to identify whether workers are wearing hard hats. The tool dispatches warning messages if the workers do not use hard hats.

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A highway construction cost index (HCCI) is an indicator of the purchasing power of a highway agency. Thus, it must reflect the actual construction market conditions. However, current methods used by most state departments of transportation are not robust enough to meet this primary goal due to (1) a significantly insufficient sample size of bid items used in HCCI calculation; and (2) inability to address the need to track highway construction market conditions in specific submarket segments such as, but not limited to, various project types, sizes, and locations. This study proposes an advanced methodology to overcome these apparent limitations using two new concepts: (1) dynamic item basket; and (2) multidimensional HCCIs. The dynamic item basket process identifies and utilizes an optimum amount of bid-item data to calculate HCCIs in order to minimize the potential error due to a small sample size, which leads to a better reflection of the current market conditions. Multidimensional HCCIs dissect the state highway construction market into distinctively smaller sectors of interest and thus, allow state Departments of Transportation to understand the market conditions with much higher granularity. A framework is developed to integrate these two concepts and a standalone prototype system, named the Dyna-Mu-HCCI System, is developed to automate the dataprocessing part of the framework. The historical bid data of the Montana Department of Transportation are used to evaluate the performance of the Dyna-Mu-HCCI System and measure the effects of the dynamic item basket (DIB) and multidimensional HCCIs. The results show an eightfold increase in terms of the number of bid items used in calculating HCCIs and at least a 20% increase in terms of the total cost of bid items used. In addition, the multidimensional HCCIs reveal different cost-change patterns from different highway sectors. For example, the bridge construction market historically shows a very different trend compared with the overall highway construction market. The new methodology is expected to aid state Departments of Transportation in making more-reliable decisions in preparing business plans and budgets with more accurate and detailed information about the construction market conditions. Further, the prototype Dyna-Mu-HCCI System is expected to significantly facilitate the HCCI calculation process and rapidly implement this new system. highway agency. Thus, it must reflect the actual construction market conditions. However, 10 current methods used by most state departments of transportation are not robust enough to meet 11 this primary goal due to a) a significantly insufficient sample size of bid items used in HCCI 12 calculation and b) inability to address the need to track highway construction market conditions 13 in specific sub-market segments in terms of project type, size and location. This study proposes 14 an advanced methodology to overcome these apparent limitations using two new concepts: a) 15 dynamic item basket and b) multidimension...

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