Integrated flexible transport service for passenger and freight under stochastic demand and service time

The growth of e-commerce has increased the pressure on urban freight systems, exacerbating traffic congestion and environmental costs. Conversely, public transport often has underutilized capacity, which presents an opportunity for freight transport. Building on this potential, we propose an integrated flexible transport system that serves both passenger and freight. We develop a two-stage stochastic optimization model that accounts for uncertainties in demand location, servie time, and volume for the integrated system. The flexible bus routes are strategically planned in the first stage, and actual passenger and freight orders are assigned to each bus in the second stage. To solve the problem, a reliability-based decomposition method is proposed to optimize reliability measures, determine the optimal flexible transport route, and minimize costs. Numerical studies show that integrating freight and passenger operations reduces costs by 12 %, while accounting for stochasticity reduces costs by 11.8 % compared to deterministic models. A case study in Manhattan validates the model's scalability and effectiveness in large-scale, real-world setting.