Mobility support for disadvantaged and disabled travelers during pandemic or similar situations

The COVID-19 lockdown has reduced public transportation service to the disadvantaged and disabled people who urgently need adequate mobility to obtain essential suppliers. This paper aims to improve the life quality of people with disabilities and elderly people by addressing social exclusion, accessibility, and mobility issues. Demand responsive transport services are frequently offered in the context of door-to-door transportation of the elderly and persons with disabilities. We study and compare two frameworks. We apply both Sample average approximation (SAA) and Rolling Horizon (RH) to optimize a car sharing system for the total cost, including initiation cost and operation cost after fleet size is determined. The model is implemented with given geographic conditions and other local information to be tailored for specific applications for local communities. Given that no historical data is available, random sample data is generated to simulate expected demands. We consider three types of probability distributions for daily demand data, and the results generated using three different distributions are being examined and compared. The research shows that the demand data following a normal distribution results in the minimum total cost. Additionally, we study the impact of several factors on total cost, including demand fulfillment rates and operation hours. Our results suggest that the impact of fulfillment rate on fleet size is exponential after a threshold under all three types of daily demand data, and extended operation hours can significantly reduce the total cost. Finally, the paper provides applicable frameworks for city planners, NPOs, and policymakers to better allocate limited resources to implement the carsharing system when little to no historical travel information is available for low-density population areas. It is anticipated that the outcome from this research would benefit disadvantaged and disabled travelers during COVID-19 or similar difficult situations in the future.