# Artificial Intelligence for Smart Transportation

arXiv: https://arxiv.org/abs/2308.07457 · HTML: https://mlsanity.com/paper/2308.07457/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments](https://mlsanity.com/paper/1705.01693/) (2017)
- [An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services](https://mlsanity.com/paper/2203.15127/) (2022)
- [A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles](https://mlsanity.com/paper/2003.12873/) (2020)
- [Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit](https://mlsanity.com/paper/2204.11992/) (2022)
- [Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service](https://mlsanity.com/paper/2004.05146/) (2020)
- [On the Interaction between Autonomous Mobility-on-Demand and Public Transportation Systems](https://mlsanity.com/paper/1804.11278/) (2018)
- [Impact of COVID-19 on Public Transit Accessibility and Ridership](https://mlsanity.com/paper/2008.02413/) (2020)
- [Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets](https://mlsanity.com/paper/2004.06043/) (2020)
- [Efficient Data Management for Intelligent Urban Mobility Systems](https://mlsanity.com/paper/2101.09261/) (2021)

## Built on by (most cited first, 2 of 2)

- [VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer](https://mlsanity.com/paper/2311.06623/) (2023)
- [On Formally Undecidable Traits of Intelligent Machines](https://mlsanity.com/paper/2402.09500/) (2024)

## Abstract (arXiv)

There are more than 7,000 public transit agencies in the U.S. (and many more private agencies), and together, they are responsible for serving 60 billion passenger miles each year. A well-functioning transit system fosters the growth and expansion of businesses, distributes social and economic benefits, and links the capabilities of community members, thereby enhancing what they can accomplish as a society. Since affordable public transit services are the backbones of many communities, this work investigates ways in which Artificial Intelligence (AI) can improve efficiency and increase utilization from the perspective of transit agencies. This book chapter discusses the primary requirements, objectives, and challenges related to the design of AI-driven smart transportation systems. We focus on three major topics. First, we discuss data sources and data. Second, we provide an overview of how AI can aid decision-making with a focus on transportation. Lastly, we discuss computational problems in the transportation domain and AI approaches to these problems.