# FRTP: Federating Route Search Records to Enhance Long-term Traffic Prediction

arXiv: https://arxiv.org/abs/2412.17373 · HTML: https://mlsanity.com/paper/2412.17373/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction](https://mlsanity.com/paper/1701.04245/) (2017)
- [How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey](https://mlsanity.com/paper/2005.11691/) (2020)
- [Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks](https://mlsanity.com/paper/1701.02543/) (2017)
- [Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework](https://mlsanity.com/paper/1612.01022/) (2016)
- [DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting](https://mlsanity.com/paper/1709.09585/) (2017)

## Built on by (most cited first, 1 of 1)

- [Place with Intention: An Empirical Attendance Predictive Study of Expo 2025 Osaka, Kansai, Japan](https://mlsanity.com/paper/2601.14570/) (2026)

## Abstract (arXiv)

Accurate traffic prediction, especially predicting traffic conditions several days in advance is essential for intelligent transportation systems (ITS). Such predictions enable mid- and long-term traffic optimization, which is crucial for efficient transportation planning. However, the inclusion of diverse external features, alongside the complexities of spatial relationships and temporal uncertainties, significantly increases the complexity of forecasting models. Additionally, traditional approaches have handled data preprocessing separately from the learning model, leading to inefficiencies caused by repeated trials of preprocessing and training. In this study, we propose a federated architecture capable of learning directly from raw data with varying features and time granularities or lengths. The model adopts a unified design that accommodates different feature types, time scales, and temporal periods. Our experiments focus on federating route search records and begin by processing raw data within the model framework. Unlike traditional models, this approach integrates the data federation phase into the learning process, enabling compatibility with various time frequencies and input/output configurations. The accuracy of the proposed model is demonstrated through evaluations using diverse learning patterns and parameter settings. The results show that online search log data is useful for forecasting long-term traffic, highlighting the model's adaptability and efficiency.