Alternative approach to estimating crash costs for cost - benefit analysis using Monte Carlo simulation

Cost-benefit analysis (CBA) is used as a tool to inform investment decisions in both the government and private sectors. An essential part of any CBA for road infrastructure projectsisthe calculation of crash cost savings. Currently, crash cost savings are typically addressed via deterministic methods, as the product of projected future traffic volumes and the expected accident rate of the road after project completion. Road traffic crashes, especially fatality and casualty crashes, typically occur only infrequently, and at unpredictable intervals, this doesn’t naturally accord with the deterministic model.This paper demonstrates how probabilistic methods can be applied to better account for crash cost savings in CBAs.The benefits of this approach are demonstrated via an example

Emergency relief goods transportation strategies – a Monte Carlo simulation approach

A road transportation network and its strategic utilization has a crucial role in emergencies occurring after natural disasters. After most natural disasters, such as floods, hurricanes, tornadoes, earthquakes and tsunamis, one of the most important emergency responses is to provide or deliver relief goods, such as water, food or medicinal supplies, to the affected areas. The complication is that in determining the routes to take for deliveries to affected areas, one has to take into consideration, at the very least, the costs, duration of trips and the availability of the routes. Also the supply and demand situation of the relief goods has to be taken into consideration before choosing the most preferred routes for the deliveries. In this paper, a Monte Carlo approach is applied for the emergency relief goods transportation strategy problem. Monte Carlo simulation has been used for varied applications in including project cost estimation, project schedule estimations, risk assessments, benefit cost analysis and selecting risk response strategies. The Monte Carlo model developed in this paper integrates costs, duration of routes and availability together with the supply and demands requirements to generate the most preferred routes. The results of the Monte Carlo simulations can be used by decision makers (emergency response team) to facilitate the decision making process while choosing the preferred and practical combinations of routes for various deliveries. The proposed approach is then applied to several simple situations to illustrate the simplicity, versatility and practicality of the approach

Synthesis and Characterization of Zinc Oxide Nanoparticles by Sol-Gel Process

In the present work zinc oxide nanoparticles (ZnO) were successfully synthesized by a sol-gel method and zinc acetate dehydrate and triethnolamine (TEA) were used as the precursor materials. Ethanol and ammonium hydroxide takes care for the homogeneity and PH value of the solution and helps to make a stoichiometric solution to get Zinc oxide nanoparticles. The ZnO powder obtained from this method is calcined at 700 oC and 900 o C temperatures. The samples were characterized by X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), Transmission electron microscopy (TEM), UV- visible spectroscopy and DSC-TG. Results shows that the calcinations temperature significantly affected the crystalline nature, particle size, and optical properties of the processed ZnO nanoparticles. The XRD spectra indicate that the ZnO crystal has a hexagonal wurtzite structure. TEM images agreement with the XRD data shown that the average size of the nanoparticles. The optical properties of the samples are investigated by measuring the UV-VIS absorption at room temperature. With increasing calcinations temperature the band gap of the samples remains almost same, and the size of the particles increases

Probabilistic benefit cost ratio – a case study

In this paper, a Monte Carlo approach to arriving at a probabilistic distribution of Benefit Cost Ratio (BCR) is presented and discussed. BCR is the ratio of the benefits of a project relative to costs and is generally seen as an indicator of the overall value for money of a project. A BCR calculation generally forms an integral part of a project proposal and used by governments to assist them in making investment decisions on projects. Project costs are increasingly being prepared by using a Monte Carlo approach whereby cost items and attendant risks are represented as probability distributions and then multiple simulations are computed to generate a probability distribution of the total cost of the project. However, the BCR is usually presented as a single number instead of a distribution, often because project benefits are still calculated as a single number. In this paper, probabilistic estimation (Monte Carlo) method is presented and then probabilistic distributions of total costs and total benefits are used to generate a probabilistic distribution of the BCR using the Monte Carlo approach. Finally the application and implications of the BCR probabilistic distribution are discussed via a case study

Ab Initio Structural Studies of Cyclobutylmethyl Cations: Effect of Fluoroalkyl Groups on the Relative Stability of the Carbocations

Ab initio calculations at MP2/cc-pVTZ level show that the trifluoromethyl group has a strong destabilizing effect on the nonclassical, σ-bridged cyclobutylmethyl cations. The GIAO-MP2 derived 13C NMR chemical shifts indicate substantial charge delocalization from the neighboring cyclobutyl ring for carbocations with an α-fluorolkyl group as compared to the 1-cyclobutylethyl cation, and this enhanced charge delocalization in case of the α-(trifluoromethyl)cyclobutylmethyl cation would lead to the ring-opening rearrangement to form the relatively more stable nonclassical primary cyclobutylmethyl cation, in which the carbocation center is farthest from the strongly electron-withdrawing trifluoromethyl group