# Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility

arXiv: https://arxiv.org/abs/2303.15495 · HTML: https://mlsanity.com/paper/2303.15495/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things](https://mlsanity.com/paper/2301.03561/) (2023)
- [Data-Driven Optimization of Public Transit Schedule](https://mlsanity.com/paper/1912.02574/) (2019)

## Built on by (most cited first, 4 of 4)

- [From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety](https://mlsanity.com/paper/2312.02078/) (2023)
- [Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers](https://mlsanity.com/paper/2403.15417/) (2024)
- [Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques](https://mlsanity.com/paper/2502.13256/) (2025)
- [Real-Time Bus Departure Prediction Using Neural Networks for Smart IoT Public Bus Transit](https://mlsanity.com/paper/2501.10514/) (2025)

## Abstract (arXiv)

In urban settings, bus transit stands as a significant mode of public transportation, yet faces hurdles in delivering accurate and reliable arrival times. This discrepancy often culminates in delays and a decline in ridership, particularly in areas with a heavy reliance on bus transit. A prevalent challenge is the mismatch between actual bus arrival times and their scheduled counterparts, leading to disruptions in fixed schedules. Our study, utilizing New York City bus data, reveals an average delay of approximately eight minutes between scheduled and actual bus arrival times. This research introduces an innovative, AI-based, data-driven methodology for predicting bus arrival times at various transit points (stations), offering a collective prediction for all bus lines within large metropolitan areas. Through the deployment of a fully connected neural network, our method elevates the accuracy and efficiency of public bus transit systems. Our comprehensive evaluation encompasses over 200 bus lines and 2 million data points, showcasing an error margin of under 40 seconds for arrival time estimates. Additionally, the inference time for each data point in the validation set is recorded at below 0.006 ms, demonstrating the potential of our Neural-Net-based approach in substantially enhancing the punctuality and reliability of bus transit systems.