• Evolutionary optimization selects bus stops from discretized candidate locations. • Global/Partial Overflow metrics quantify deviations from target spacing intervals. • Multi-criteria objective balances spacing, people influx, POI proximity, constraints. • Zone-dependent rules set different spacing constraints for downtown and suburbs. • Real-route tests in Arequipa (Peru) produce homogeneous, interpretable plans. Bus stop location planning along fixed routes is a key design problem in public transportation because it affects accessibility, walking distances, and operational efficiency. This paper proposes an evolutionary algorithm approach to optimize bus stop locations along a fixed bidirectional route by selecting a subset from a finite set of candidate stop locations obtained through route discretization. The optimization accounts for spacing constraints (with different thresholds for downtown and suburban zones), people influx around candidate stops, area relevance, restricted road segments where stops should be avoided, and the pairing of opposite stops on two-way streets. To evaluate spacing quality, we introduce two dispersion-based metrics, Global Overflow (Ω G ) and Partial Overflow (Ω P ), which quantify deviations between the observed spacing interval and a target interval. Experiments on real routes from Arequipa (Peru) show that the approach produces homogeneous spacing while prioritizing stop locations in areas with higher people influx and area relevance, and avoiding restricted segments. The proposed framework is adaptable to different urban contexts and may support stop-location planning in growing cities.
Giráldez et al. (Sun,) studied this question.