Macroscopic impacts of optimised tourist bus routes on urban road networks: a spatiotemporal analysis

In developing countries, urban tourism systems increasingly rely on large-capacity buses, whose stopping, boarding, and manoeuvring operations often create localised congestion and traffic bottlenecks. However, contemporary routing algorithms can create a paradox whereby solutions that are optimal for individual tourist buses may collectively induce emergent network-wide congestion when deployed simultaneously across the entire fleet. This paper introduces a cross-scale evaluation framework that explicitly links vehicle-centric routing decisions with macroscopic transport network performance. A microscopic traffic simulation of Nha Trang, Vietnam, developed in SUMO and calibrated using empirical intersection signal timings and stochastic bus dwell times, serves as the experimental testbed. Three routing paradigms are systematically benchmarked: a fixed-route baseline, a metaheuristic approach based on a Genetic Algorithm (GA), and an AI-driven method using Double Deep Q-Network (Double DQN). Stress testing with near-critical background traffic, evaluated across multiple random seeds to account for stochastic demand, reveals that uncoordinated static routing results in a severe 71% loss in network throughput. While static metaheuristic optimisation reduces this loss to 37%, it remains vulnerable to dynamic bottlenecks. Conversely, the Double DQN agent continuously redistributes the spatial load, suppressing average vehicle time loss by over 60% and limiting throughput degradation to a statistically significant 24% (p < 0.01). These findings demonstrate that optimising routes independently for individual buses is insufficient; real-time adaptive routing is a potential mechanism to help preserving network-level capacity, sustain Macroscopic Fundamental Diagram (MFD) flows, and mitigate localised CO 2 emission hotspots.