SmartTransit.AI: A Dynamic Paratransit and Microtransit Application
SmartTransit.AI: A Dynamic Paratransit and Microtransit Application
Sophie Pavia, David Rogers, Amutheezan Sivagnanam, Michael Wilbur, Danushka Edirimanna, Youngseo Kim, Ayan Mukhopadhyay, Philip Pugliese, Samitha Samaranayake, Aron Laszka, Abhishek Dubey
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8767-8770.
https://doi.org/10.24963/ijcai.2024/1028
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.
Keywords:
Multidisciplinary Topics and Applications: MDA: Transportation
Planning and Scheduling: PS: Applications
Planning and Scheduling: PS: Planning algorithms
Planning and Scheduling: PS: Scheduling