Vehicle ownership forecasting models based on the Gompertz curve generally employ per capita Gross Domestic Product as the primary explanatory variable. The γ coefficients in the curves specify the ultimate vehicle saturation level and α, β the profile of the S-curve shape. The γ coefficient is assumed to be the single universal value that all countries will eventually reach. The present research hypothesised that countries could have variable saturation levels and, as such, the γ coefficient would not be a universal constant. On that premise, it attempted modelling the γ coefficient as a function of several other extraneous factors that significantly influence a country's vehicle ownership and ultimately its saturation level, thus resulting in a country-specific γ; the application of the Gompertz model and the relationship of vehicle ownership would then be influenced by the country-specific characteristics. It was found that, while vehicle ownership is influenced by the GDP as in the case of Gompertz model, the country-specific γ would also be influenced by other identifiable variables such as household size, population density, share of public transport, and percentage of female drivers in each country. The results confirm the globally observable phenomenon that high-income countries do not converge to a universal vehicle ownership saturation level. Singapore and Hong Kong are examples, usually excluded from the Gompertz model, which can now be explained by the new model.
Calculating trips from each traffic zone is one of the essential steps in the four-step model. Multiple linear regression (MLR) is the most popular among the various methods available for calculating trips. The main limitation of this method is its reliance on independent variables related to the zone. Due to the assumptions in this method, future predictions are also subject to the question of accuracy. Conversely, updating these independent variables requires additional time and resources for conducting selected types of surveys, such as home visit surveys (HVS). Using high-frequency data (HFD) that is freely available and is updated frequently, this paper estimates trip generation using fuzzy logic to fill in the gap. The fuzzy model was created using 2013 HVS data and updated the data with 2013 for validation purposes and 2019 as a prediction year. The research area chosen for this purpose is Thimbirigasyaya DSD in Western Province, Sri Lanka. According to the results of this study, a fuzzy rule-based model can be used when there are no exact data available, and the available high-frequency data shows a non-linear relationship with the dependent variable.
Buses carried about 47% of passengers crossing the Colombo Municipal Council (CMC) boundary in 2013. The Sri Lanka Transport Board (SLTB) and private bus companies operates roughly 680 intra-provincial bus routes and 400 inter-provincial bus routes in the Western Province according to the bus route information from the National Transport Commission (NTC, 2012) . The number of buses on the seven (7) major radial corridors carries more bus passengers than other roads in the Western Province. Kandy, Galle and Malabe Road corridors are the highest followed by High Level, Negombo, Horana and Low Level Road corridors. This research paper is an attempt to analyse the bus passenger demand and supply on seven major corridors connecting Western Province boundaries. The impact of the passenger demands and volumes from the seven major corridors and the traffic speed are the main outcome of this analysis. The present bus traffic information and the impact of the traffic congestion due to the high traffic flow on major corridors are discussed in the analysis. The research will also focus on alternative solutions and the importance of integrating other public transport modes to cater the demand from the major seven corridors for the better utilisation of public transport inside the Western Province boundaries.
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