Developing strategies for improving travel time performance in multimodal public transport using ANP, PROMETHEE, network DEA, and optimization algorithms

This study proposes an integrated framework combining Multi-Criteria Decision Making (MCDM), Data Envelopment Analysis (DEA), and optimization algorithms to improve travel time performance in multimodal public transport systems. This novel integrated framework represents the first comprehensive combination of MCDM, DEA, and optimization techniques for multimodal transport optimization. Applied to Bhopal, India's bus, BRT, and metro systems, the framework employs ANP, PROMETHEE, Network DEA, Super-Efficiency DEA, GA, and PSO techniques. ANP analysis identified in-vehicle travel time (0.35) and transfer time (0.28) as highest priority criteria. PROMETHEE ranking placed transfer synchronization (Φ=0.352) and dedicated bus lanes (Φ=0.283) as top improvement strategies. Network DEA revealed BRT system efficiency of 0.92, compared to metro (0.85) and conventional bus (0.78). Super-Efficiency DEA confirmed BRT superiority with score 1.25. Optimization algorithms achieved significant travel time reductions: GA (28.5 minutes, 19 % improvement, R²=0.87) and PSO (27.8 minutes, 21 % improvement, R²=0.91) compared to current performance (35.2 minutes). Results demonstrate the framework's effectiveness for evidence-based multimodal transport optimization and sustainable urban mobility planning.