Applicability of Centrality Measures to explain Vehicular flow in Colombo Municipal Council Area, Sri Lanka

Abstract

Traffic congestion has become a major issue for all of the cities in the acrid. Sri Lanka also incurs 1.5% of GDP due to massive financial and man-hour loss due to traffic congestion. Thus, this sets the importance of understanding how development and design of future built environment influence travel demand and traffic flows. Traffic flow modeling enables us to envisage traffic flows in urban models require the acquisition and analysis of large quantities of data such as the network topology, its traffic flow data, vehicle fleet composition, emission measurements and so on. Data acquisition is an expensive process that involves household surveys and automatic as well as semiautomatic measurements performed all over the network. Currently Sri Tanka and most of the Developing Countries follows these conventional Traffic Flow models, which identified as expensive affair and inefficient method. Therefore, predicting or understanding traffic flow has become an emerging challenge for Sri Tanka in this context. areas. Yet most of the current conventional Given this background this study focused on an emerging set of research literature that are employed in transport planning applications in developed countries. Those researches have based on network centrality parameters that revealed successful results in measuring traffic flow. Yet, all above studies based on developed countries and there are none or very limited applications with refining to the emerging rides in developing world where such research need the most. Therefore, this study looks a! the applicability of centrality measures to access the traffic flows in Sri Lankan context. Accordingly, the objective of this research is to study the applicability of centrality measures to explain vehicular flow in Colombo municipal council area. In this study we employed three centrally measures such as Degree. Betweenness and Closeness, which are borrowed from the domain of complex network analysis. Centrality values have men computed by axial map which generated using motorable road network of the CMC. Then it calculated using UCT-Dep/h 'nap and ArcCIS software applications. Generated Centrality values have been evaluated using actual traffic flow data along the CMC road network. Correlation analysis indicates that actual traffic flow values has a significant correlation with Degree (0.337 with significant at the 0.05 level). Betmennes(0771 with significant at the 0.01 level) and measures. Multiple regression analysis results was Closeness (0.742 with significant at the 0.01 level) centrally indicated that centrally measures hare capabilities to explain over 7S% of the variation of actual Traffic flow values. Therefore, the study suggests that Centrality Measures can scree as an alternative method to identify and predict traffic flow pattern of cities. Accordingly method developed in this study van be consider as robust and dynamic planning tool that will offer promise for spatial and transport planners to overcome emerging challenges and changing needs in the built environment.