To develop an effective waste management strategy for a given region, it is important to know the amount of waste generated and the composition of the waste stream. Past research has shown that the amount of waste generated is proportional to the population and the average mean living standards or the average income of the people. In addition, other factors may affect the amount and composition of waste. These are climate, living habits, level of education, religious and cultural beliefs, and social and public attitudes. This paper presents the findings of a study carried out in a suburban municipal area in Sri Lanka to determine the solid waste generation rate and waste composition based on field surveys and to determine the related socio-economic factors. A database was developed that included information on the quantity and composition of waste generated in a sample of households in the study area over a time period. The collected data was analysed to relate waste generation and composition data to various socio-economic factors. Over 400 sample households were selected for the study using a stratified random sampling methodology based on municipal wards and property values. A technique that considers both the number of households in a particular income group (property value range) and the standard deviation of property values within a given income group was used to determine the appropriate sample size for each municipal ward. Through category and regression analyses, the quantities of waste and waste composition were related to several socio-economic factors. The paper describes the basis for the sample selection, the methodology adopted for data collection, the socio-economic parameters used for the analysis, and the relationships developed from the analysis.

The optimal spacing of bus-stops along a local bus-route with nonuniform many to many travel demand is determined. Weak assumptions are made regarding the local street network. The daily demand functions are assumed to vary slowly within a spacing. The analysis is based on continuum approximations and methods of calculus. The probability of a null demand for boarding and alighting a given bus at a given bus-stop is exploited to space stops closer than otherwise.

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