AI Model Predicts How Commuters Change Travel Habits When Given Incentives
Researchers developed a machine learning system that accurately forecasts individual travel behavior shifts in response to transit subsidies and policy changes—using far less data than previous methods. The breakthrough could help cities and transit agencies design more effective incentive programs and predict ridership patterns before implementing costly fare reforms.
Originaltitel: Group effect enhanced generative adversarial imitation learning for individual travel behavior modeling under incentives
• Customize generative adversarial imitation learning method to model individual-level longitudinal behavioral responses under policy incentives. • Develop a novel group effect enrichment to mitigate data sparsity and enhance the model generalizability. • Validate the proposed approach through extensive experiments using smart card data. • Demonstrate high data efficiency and accuracy in reproducing travel behavior over time and space. Understanding and modeling individual travel behavior responses is crucial for urban mobility regulation and policy evaluation. The Markov decision process (MDP) provides a structured framework for dynamic travel behavior modeling at the individual level. However, solving an MDP in this context is highly data-intensive and faces challenges of data quantity, spatial-temporal coverage, and situational diversity. To address these, we propose a group-effect-enhanced generative adversarial imitation learning (gcGAIL) model that improves the individual behavior modeling efficiency by leveraging shared behavioral patterns among passenger groups. We validate the gcGAIL model using a public transport fare-discount case study and compare against state-of-the-art benchmarks, including adversarial inverse reinforcement learning (AIRL), baseline GAIL, and conditional GAIL. Experimental results demonstrate that gcGAIL outperforms these methods in learning individual travel behavior responses to incentives over time in terms of accuracy, generalization, and pattern demonstration efficiency. Notably, gcGAIL is robust to spatial variation, data sparsity, and behavioral diversity, maintaining strong performance even with partial expert demonstrations and underrepresented passenger groups. The gcGAIL model predicts individual behavioral responses over time, providing a foundation for personalized incentive strategies that promote sustainable behavior change through more effective timing of incentive interventions.