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 2024 IJCAI '24

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Why This Matters

Deploying algorithmic innovations in real transit systems requires solving numerous practical challenges beyond optimization, including human-computer interfaces, real-time data integration, and software robustness. This demonstration work is valuable because it illustrates how research algorithms can be translated into functional systems that transit agencies can actually use. The modular architecture enables adaptation to different agency constraints and algorithms, making it a practical tool for understanding how to implement advanced transit optimization in practice.

What We Did

This paper presents a demonstration of the SmartTransit.AI software system for dynamic paratransit and microtransit operations. The demo showcases the complete software architecture including web-based operations management interfaces, vehicle operator mobile applications, and real-time optimization components. The system is demonstrated using generative demand models based on real passenger data and shows how the various system components integrate to support both offline scheduling and online dispatch decisions for shared mobility services.

Key Results

The demonstration effectively shows how SmartTransit.AI enables real-time management of paratransit services through multiple coordinated interfaces. The system successfully processes multiple data feeds, generates optimized routes, and presents results to operators and users in actionable formats. The integrated visualization and optimization components demonstrate the feasibility of deploying sophisticated algorithms in actual transit operations, with the ability to handle the dynamic constraints and information flows required in practice.

Full Abstract

Cite This Paper

@inproceedings{paviaIJCAI24demo,
  author = {Pavia, Sophie and Rogers, David and Sivagnanam, Amutheezan and Wilbur, Michael and Edirimanna, Danushka and Kim, Youngseo and Mukhopadhyay, Ayan and Pugliese, Philip and Samaranayake, Samitha and Laszka, Aron and Dubey, Abhishek},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence},
  title = {SmartTransit.AI: a dynamic paratransit and microtransit application},
  year = {2024},
  series = {IJCAI '24},
  abstract = {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.},
  articleno = {1028},
  contribution = {lead},
  acceptance = {15},
  doi = {10.24963/ijcai.2024/1028},
  isbn = {978-1-956792-04-1},
  location = {Jeju, Korea},
  numpages = {4},
  url = {https://doi.org/10.24963/ijcai.2024/1028},
  keywords = {transit software, paratransit operations, microtransit, real-time optimization, software architecture, operational interfaces, transportation dispatch, system integration}
}
Quick Info
Year 2024
Series IJCAI '24
DOI 10.24963/ijcai.2024/1028
Keywords
transit software paratransit operations microtransit real-time optimization software architecture operational interfaces transportation dispatch system integration
Research Areas
transit middleware ML for CPS
Search Tags

SmartTransit.AI, dynamic, paratransit, microtransit, application, transit software, paratransit operations, real-time optimization, software architecture, operational interfaces, transportation dispatch, system integration, transit, middleware, ML for CPS, 2024, Pavia, Rogers, Sivagnanam, Wilbur, Edirimanna, Kim, Mukhopadhyay, Pugliese, Samaranayake, Laszka, Dubey, IJCAI24