TY - GEN
T1 - SmartTransit.AI
T2 - 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
AU - Pavia, Sophie
AU - Rogers, David
AU - Sivagnanam, Amutheezan
AU - Wilbur, Michael
AU - Edirimanna, Danushka
AU - Kim, Youngseo
AU - Mukhopadhyay, Ayan
AU - Pugliese, Philip
AU - Samaranayake, Samitha
AU - Laszka, Aron
AU - Dubey, Abhishek
N1 - Publisher Copyright:
© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2024
Y1 - 2024
N2 - New rideshare and shared mobility services have transformed urban mobility in recent years. Such services have the potential to improve efficiency and reduce costs by allowing users to share rides in high-capacity vehicles and vans. Most transit agencies already operate various ridepooling services, including microtransit and paratransit. However, the objectives and constraints for implementing these services vary greatly between agencies and can be challenging. First, off-the-shelf ridepooling formulations must be adapted for real-world conditions and constraints. Second, the lack of modular and reusable software makes it hard to implement and evaluate new ridepooling algorithms and approaches in real-world settings. We demonstrate a modular on-demand public transportation scheduling software for microtransit and paratransit services. The software is aimed at transit agencies looking to incorporate state-of-the-art rideshare and ridepooling algorithms in their everyday operations. We provide management software for dispatchers and mobile applications for drivers and users and conclude with results from the demonstration in Chattanooga, TN.
AB - New rideshare and shared mobility services have transformed urban mobility in recent years. Such services have the potential to improve efficiency and reduce costs by allowing users to share rides in high-capacity vehicles and vans. Most transit agencies already operate various ridepooling services, including microtransit and paratransit. However, the objectives and constraints for implementing these services vary greatly between agencies and can be challenging. First, off-the-shelf ridepooling formulations must be adapted for real-world conditions and constraints. Second, the lack of modular and reusable software makes it hard to implement and evaluate new ridepooling algorithms and approaches in real-world settings. We demonstrate a modular on-demand public transportation scheduling software for microtransit and paratransit services. The software is aimed at transit agencies looking to incorporate state-of-the-art rideshare and ridepooling algorithms in their everyday operations. We provide management software for dispatchers and mobile applications for drivers and users and conclude with results from the demonstration in Chattanooga, TN.
UR - http://www.scopus.com/inward/record.url?scp=85204294923&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85204294923&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85204294923
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 8767
EP - 8770
BT - Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
A2 - Larson, Kate
PB - International Joint Conferences on Artificial Intelligence
Y2 - 3 August 2024 through 9 August 2024
ER -