Data-driven route optimization using a Hamiltonian circuit framework: A case study of Delhi

A major issue in cities all around the world, particularly in major metropolises like Delhi, is traffic congestion. Both the huge number of private vehicles on the road and the significant reliance on public transportation are major causes of this problem. Inadequate road capacity exacerbates the situation even more. A number of roads in Delhi are getting close to saturation when the volume of traffic on them matches the capacity V/C = 1. To tackle this challenge, traffic engineering offers viable solutions. This study presents a data-driven application of a classical Hamiltonian Circuit (HC) framework integrated with real-world traffic, fuel consumption, and vehicle-type data. Rather than proposing a new optimization algorithm, the work adapts established graph-theoretic methods to urban mobility conditions using empirical congestion metrics and a percentile-based pruning strategy. As demonstrated by the simulation results, the optimized circular routes derived using the backtracking-based HC framework significantly reduce traffic stress on public transit. Overall, the approach improves traffic flow and helps mitigate congestion in key areas of Delhi, particularly during peak-hour conditions.