Lakshika Meetiyagoda

Traffic congestion is a prevalent urban phenomenon. Extensive research has been conducted to support the mitigation of traffic congestion. However, an acceptable definition of traffic congestion levels is still elusive, and it generally relies on uncertain terminology to describe the severity. Therefore, this research proposes a conceptual model based on fuzzy logic to quantify and interpret traffic congestion levels. Parameters such as the number of vehicles, the number of lanes on the road, the percentage of different vehicle types passing through the area, the area occupied on the road by vehicles, and the time period under consideration have been incorporated into the model, taking into account similar research and identified gaps. A methodology has been devised to provide a numerical representation of real-time traffic congestion. A fuzzy inference system has been introduced to interpret traffic congestion using uncertain terms. The validity of the proposed system has been assessed using a confusion matrix. Furthermore, Cohen's Kappa analysis was employed to evaluate the overall accuracy of the system, revealing an observed Kappa value of 0.82 (> 0.8), signifying a high level of accuracy. This model holds the potential to benefit commuters and policymakers in developing more sustainable and resilient traffic management systems.

This research delves into the influence of shade on pedestrian activities, employing a comprehensive mixed-methods approach along the primary thoroughfares of Colombo, Sri Lanka. Google Street View photographs served as the primary data source for this spatial analysis. Hotspot analysis and spatial autocorrelation were utilized to depict the spatial distribution of shade and establish its correlation with pedestrian activities. The spatial analysis results revealed a consistent pattern of shade concentration aligning with areas of heightened pedestrian activities. The findings hold significant implications for urban planning and design practitioners, as well as policymakers.

Due to the tremendous increase in automobile transportation and heavy pedestrian movements in developing countries, roads are becoming deadlier year by year. It is acknowledged that context specific research on pedestrian-vehicular conflict are urgently needed considering the built environment characteristics. Therefore, this paper aims to (1) redefine pedestrian-vehicular conflict that would enable to explain micro- and macro-built environment-related variables in a particular context, and (2) develop a methodology that could be used in a place where comprehensive data are limitedly available for a spatial analysis. This research redefines pedestrian-vehicular conflict as “the pedestrian contact with potentially harmful vehicular traffic”, rather than accidents. Based on this definition devise a methodology. The primary data collection method adopted to collect causal factors related to spatial data was, photographs. The spatial data were analysed by using QGIS platform. The pedestrian volume models are constructed by a space syntax framework and correlated with a composite choropleth map to get the potential conflicting points. A perception survey was carried out to confirm the spatial analysis. The research findings indicate that the methodology developed can be used to identify built environment factors related risk areas spatially. Consequently, it is possible to fill the research gap by introducing a low-cost, widely applicable, impartial, spatial and perception-based methodology that assesses the built environmental characteristics in relation to pedestrian-vehicular conflict. This research would support the urban planners and designers, allowing them to comprehend the risk related nature of pedestrian-vehicular conflict in their urban planning schemes before intervening with plans and designs