TY - GEN
T1 - Deploying Mobility-On-Demand for All by Optimizing Paratransit Services
AU - Pavia, Sophie
AU - Rogers, David
AU - Sivagnanam, Amutheezan
AU - Wilbur, Michael
AU - Edirimanna, Danushka
AU - Kim, Youngseo
AU - Pugliese, Philip
AU - Samaranayake, Samitha
AU - Laszka, Aron
AU - Mukhopadhyay, Ayan
AU - Dubey, Abhishek
N1 - Publisher Copyright:
© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2024
Y1 - 2024
N2 - While on-demand ride-sharing services have become popular in recent years, traditional on-demand transit services cannot be used by everyone, e.g., people who use wheelchairs. Paratransit services, operated by public transit agencies, are a critical infrastructure that offers door-to-door transportation assistance for individuals who face challenges in using standard transit routes. However, with declining ridership and mounting financial pressure, public transit agencies in the USA struggle to operate existing services. We collaborate with a public transit agency from the southern USA, highlight the specific nuances of paratransit optimization, and present a vehicle routing problem formulation for optimizing paratransit. We validate our approach using real-world data from the transit agency, present results from an actual pilot deployment of the proposed approach in the city, and show how the proposed approach comprehensively outperforms existing approaches used by the transit agency. To the best of our knowledge, this work presents one of the first examples of using open-source algorithmic approaches for paratransit optimization.
AB - While on-demand ride-sharing services have become popular in recent years, traditional on-demand transit services cannot be used by everyone, e.g., people who use wheelchairs. Paratransit services, operated by public transit agencies, are a critical infrastructure that offers door-to-door transportation assistance for individuals who face challenges in using standard transit routes. However, with declining ridership and mounting financial pressure, public transit agencies in the USA struggle to operate existing services. We collaborate with a public transit agency from the southern USA, highlight the specific nuances of paratransit optimization, and present a vehicle routing problem formulation for optimizing paratransit. We validate our approach using real-world data from the transit agency, present results from an actual pilot deployment of the proposed approach in the city, and show how the proposed approach comprehensively outperforms existing approaches used by the transit agency. To the best of our knowledge, this work presents one of the first examples of using open-source algorithmic approaches for paratransit optimization.
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M3 - Conference contribution
AN - SCOPUS:85204300620
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 7430
EP - 7437
BT - Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
A2 - Larson, Kate
PB - International Joint Conferences on Artificial Intelligence
T2 - 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Y2 - 3 August 2024 through 9 August 2024
ER -