Enhancing Urban Paratransit Reliability: Spatial–Temporal and Causal Analysis of Service Inefficiencies

Reliable paratransit services are essential for urban mobility, particularly for individuals with disabilities who depend on demand-responsive transportation. However, service inefficiencies in urban paratransit systems, such as travel time variability, congestion impacts, and scheduling constraints, continue to pose significant challenges. This study introduces a multitier analytical framework that integrates spatial–temporal modeling, machine learning–based outlier detection, and causal inference to systematically evaluate the reliability of paratransit services in an urban context. Leveraging trip transaction data from a metropolitan paratransit system, we develop the paratransit efficiency index (PEI) to assess travel time reliability at both system-wide and individual trip levels. We then analyze PEI's spatial–temporal variability using geographically and temporally weighted regression and identify outlier trips with high PEI using XGBoost to pinpoint service unreliability and systemic inefficiencies. Later, we utilize causal inference techniques to show that peak-hour pick-ups causally contribute to travel time inefficiency (a 19.4% relative increase in outlier probability), whereas subscription-based bookings causally improve service consistency (a 33.9% relative decrease). The findings provide actionable insights for urban planners and transit agencies to optimize scheduling, mitigate congestion effects, and explore innovative strategies such as integrating transportation network companies for high-cost or unreliable trips. By addressing critical urban transportation equity issues, this study offers data-driven solutions to enhance the efficiency and resilience of paratransit services in growing metropolitan areas.