Developing empirical model with graphical tool to estimate and predict capacity of rural highway roundabouts

Transportation infrastructure plays a significant role in the economic growth of developing countries where heavy vehicles act as the backbone in transporting goods and people via highways. However, presence of heavy vehicles in traffic stream lowers the overall operating speed and flow in rural highways especially at the vicinity of intersections. This research aims at developing an empirical model for estimating and predicting capacity of highway roundabouts considering both geometric and traffic parameters. Based on data collected from six roundabouts along the national highways in Bangladesh, multivariate regression method has been used to develop an empirical model as a function of entry width, rotary lane width, distance between entry and nearest exit, central island diameter, and circulating traffic flow to estimate and predict the capacity of rural highway roundabouts. For incorporating the presence of heavy vehicles in the model, all vehicle types are converted to passenger car equivalent units. A comparative study with established methods (i.e., HCM 2016, TRRL, IRC and German method) proves the superiority of the developed model in terms of predictive capacity. Furthermore, a Python based program called PyNomo, has been executed with the help of several add-on packages (e.g., numpy, scipy, pyx) to generate a compound parallel scale nomograph consisting of multiple variables using the developed empirical model. Considering the impact of heavy vehicles in the socio-economic development of a country, the nomograph will aid the practitioners and engineers as a quick and easily interpretable graphical tool for planning and designing roundabouts in rural highways.