Introduction:
Bus stops are vital components of a transit system, significantly influencing the efficiency and accessibility of public transportation. For individuals with physical disabilities, inaccessible bus stops can pose substantial challenges, limiting their mobility and restricting their access to fixed-route bus services. In response to this issue, transit agencies grapple with the task of identifying bus stops for improvements, ensuring compliance with the Americans with Disabilities Act (ADA) while working within tight budget constraints.
Analytic Hierarchy Process (AHP) Application:
In a recent paper, researchers introduced an optimization model that utilizes Analytic Hierarchy Process (AHP) to objectively prioritize bus stops for ADA improvements. The traditional method of improving bus stops involves compliance with ADA standards, but due to budget limitations, not all stops can be addressed simultaneously. The AHP model, however, combines various factors affecting benefits to riders with physical disabilities, providing a more strategic and objective platform for decision-making.
Factors Considered and Data Integration:
The optimization model takes into account a range of factors, including spatial distribution of riders with physical disabilities, transit ridership, wheelchair ridership, customer complaints, facility deployment costs, and service area demographic information. These factors are evaluated using spatial multicriteria decision making (MCDM), making use of Geographic Information System (GIS) technology.
AHP and Binary Linear Programming Model:
The AHP technique is employed to assign weights to bus stops based on the identified factors, emphasizing the significance of each criterion. Subsequently, a binary linear programming model is formulated. This model ensures that, within the confines of the available budget, bus stops selected for improvements will maximize benefits for riders with physical disabilities. The optimization model outputs a priority list of bus stops, simplifying the decision-making process for transit agencies.
Conclusion:
This optimization model, combining AHP and binary linear programming, provides a systematic and data-driven approach for transit agencies to identify bus stops for ADA improvements. By optimizing benefits to riders with physical disabilities within budget constraints, the model offers a more objective and efficient platform compared to traditional decision-making methods. As transit systems strive for inclusivity and compliance with ADA standards, this innovative approach presents a valuable tool for enhancing accessibility in public transportation.
