Transit Frequency Setting Problem with Demand Uncertainty

Abstract

Public transit systems are the backbone of efficient and sustainable urban mobility systems in the era of urbanization. This paper proposes a transit frequency setting model for a single transit line to generate transit schedules that could better serve passengers. The proposed model optimizes both service patterns and frequency for transit routes during a service period, and crowding levels on transit vehicles are also considered. To handle demand uncertainties when designing transit schedules, both stochastic programming and robust optimization techniques are introduced to protect transit schedules against inaccurate demand estimates. To address the computation complexity in both extended models under large-scale demand matrices, a Bender decomposition algorithm and two dimension reduction techniques are designed. The proposed models will be tested with real-world data from Chicago Transit Authority (CTA).