Numerical optimization of critical gap at urban unsignalized intersections

The critical gap is an indispensable measure in assessing the capacity of unsignalized intersections in urban areas. The swift growth of cities exacerbates vehicular congestion at intersections. This study focuses on selecting an influential area to determine the critical gap at unsignalized intersections. The method of influence area is applied to determine the minimum accepted gap at intersections. The execution of artificial neural network and Gaussian process regression prediction models is employed to perform a comparison of the minimum acceptable gap for various categories of vehicles. Further, the critical gap values of several vehicle classes at major unsignalized intersections in two cities in the Indian state of Jharkhand are determined using the numerical optimization technique in response surface methodology. Heterogeneous traffic includes slow-moving three-wheelers, which take 8.17 s to manoeuvre through the conflict zone. The evaluation of heavy vehicles is limited due to their relatively low presence within city traffic during daytime hours. Transport planners and engineers could use the acquired critical gap values to design tier II and emerging smart cities.