Mixed-traffic streams that contain motorized and nonmotorized vehicles are becoming more common in urban areas. These streams contain standard vehicle types such as private cars, buses, and trucks, as well as nonstandard vehicles such as bicycles, motorcycles, and other vehicular forms. Models suitable for analysis of such streams hardly exist, and most available models are limited in scope and effectiveness. Analysis of mixed streams that use traditional approaches has achieved limited success and has involved much recalibration effort and significant model modifications. Effective analysis of these streams therefore inevitably requires new models to be developed that use different approaches. Aspects of a model developed specifically for mixed streams are presented. This model covers different vehicle types, including nonmotorized ones, and allows for some special behaviors, such as seepage to fronts of queues by two-wheeled vehicles and simultaneous use of two lanes. In addition to normal car-following rules, the model incorporates lateral movement with a gradual lane change maneuver (as opposed to an instantaneous one), the decisions of which are governed by fuzzy logic rules. The model was calibrated and tested with data from Nairobi, Kenya, and its predictions were found to be in close agreement with the field data. In addition to its being a normal traffic management tool, the model makes a significant contribution to the study of the influence of nonstandard vehicle types or behavior on traffic performance.

Small and medium enterprise projects play a major role in most economies in economic growth and development by creating employment opportunities to many people and as a source of technological innovation to create new products and eradication of poverty. Although their contribution in economic growth is indisputable, provocative argument on factors influencing their performance has remained unsolved to date. The purpose of this study was to determine the extent to which risk assessment influence performance of SME projects in Machakos County. The study applied pragmatism philosophical approach and descriptive survey research design. It tested the hypothesis at 95% confidence level which stated that risk assessment does not significantly influence performance of small and medium enterprise projects in Machakos County. The study used multiple regressions model against a sample size of 265 selected from a population of 5311 small and medium enterprise projects in Machakos County using stratified and convenience sampling approach as guided by the Yamane (1967) formula. A structured questionnaire was used to collect data whereby drop and pick approach was used. The study finding revealed that majority of the risk assessment components were positively supported by the respondents and their response mean was above 3.50, composite mean. Inferential statistics depicted that risk identification, prioritization and managing change significantly influenced financial performance with β=.102(p=0.016) and β=.092(p=0.012) respectively whereas organizational goals and objectives had insignificant influence with β=.031(p=0.366). Further, risk identification and prioritization significantly influenced non-financial performance with β=.104(p=0.017) whereas organizational goals and objectives and managing change had insignificant influence with β=.020(p=0.574) and β=.054(p=0.184) respectively. Management of SME projects should ensure the significant contribution by risk identification and prioritization towards performance in general is upheld with further endeavors to improve on the risk assessment components which have insignificant impact on performance. Further investigation is necessary to establish cause of risk assessment components influence disparities on both financial and non-financial performance perspectives.