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This dissertation performs a comprehensive analysis of the effect of different environmental, demographic and vehicle variables on the severity of two-vehicle crashes. The limitations associated with previous studies have been addressed by using a large crash database, properly defining the independent variables, using appropriate statistical models, and by considering the effect of factors normally unaccounted for such as crash type, impact speed, and weight or height incompatibilities between the two vehicles.The use of multinomial logit models at the individual occupant and crash levels provides the flexibility to evaluate variables that have opposing effects at different injury levels (such as airbags). Alternative formulations with interaction terms and with instrumental variables are included. An analysis of marginal probabilities and costs is also provided, which is particularly useful when discussing potential safety treatments with transportation officials, politicians and other decision makers.The findings from the different models are consistent and suggest that the type of crash has a great impact on severity. Age is the most significant demographic variable, with children and older occupants being least and most likely to be injured, respectively. Behavior also seems to be critical, as the use of seatbelts greatly decreases occupant injuries. Heavier vehicles increase the safety of its occupants but decrease the safety of occupants of the other vehicle. The effect of vehicle type is not as significant as weight, with the exception of pickups, which are both more crashworthy and more aggressive than passenger cars. Further research is needed on the effects of airbags and impaired driving, as the analyses conducted have been inconclusive.

Software maintenance is an important activity in software engineering. Over the decades, software maintenance costs have been continually reported to account for a large majority of software costs [Zelkowitz 1979, Boehm 1981, McKee 1984, Boehm 1988, Erlikh 2000]. This fact is not surprising. On the one hand, software environments and requirements are constantly changing, which lead to new software system upgrades to keep pace with the changes. On the other hand, the economic benefits of software reuse have encouraged the software industry to reuse and enhance the existing systems rather than to build new ones [Boehm 1981, 1999]. Thus, it is crucial for project managers to estimate and manage the software maintenance costs effectively. / Accurate cost estimation of software projects is one of the most desired capabilities in software development Process. Accurate cost estimates not only help the customer make successful investments but also assist the software project manager in coming up with appropriate plans for the project and making reasonable decisions during the project execution. Although there have been reports that software maintenance accounts for the majority of the software total cost, the software estimation research has focused considerably on new development and much less on maintenance. Now if we talk about real time software system(RTSS) development cost estimation and maintenance cost estimation is not much differ from simple software but some critical factor are considered for RTSS development and maintenance like response time of software for input and processing time to give correct output. As like simple software maintenance cost estimation existing models (i.e. Modified COCOMO-II) can be used but after inclusion of some critical parameters related to RTSS. A Hypothetical Expert input and an industry data set of eighty completed software maintenance projects were used to build the model for RTSS maintenance cost. The full model, which was derived through the Bayesian analysis, yields effort estimates within 30% of the actual 51% of the time,outperforming the original COCOMO II model when it was used to estimate theseprojects by 34%. Further performance improvement was obtained when calibrating the full model to each individual program, generating effort estimates within 30% of the actual 80% of the time.

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