# Comparative Analysis of Machine Learning Models for Predicting Travel Time

arXiv: https://arxiv.org/abs/2111.08226 · HTML: https://mlsanity.com/paper/2111.08226/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Built on by (most cited first, 6 of 6)

- [DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos](https://mlsanity.com/paper/2304.08261/) (2023)
- [AI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environment](https://mlsanity.com/paper/2301.09315/) (2023)
- [Machine Learning-Based Classification Algorithms for the Prediction of Coronary Heart Diseases](https://mlsanity.com/paper/2112.01503/) (2021)
- [Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network](https://mlsanity.com/paper/2205.10507/) (2022)
- [Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning](https://mlsanity.com/paper/2304.09246/) (2023)
- [Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennessee](https://mlsanity.com/paper/2405.10375/) (2024)

## Abstract (arXiv)

In this paper, five different deep learning models are being compared for predicting travel time. These models are autoregressive integrated moving average (ARIMA) model, recurrent neural network (RNN) model, autoregressive (AR) model, Long-short term memory (LSTM) model, and gated recurrent units (GRU) model. The aim of this study is to investigate the performance of each developed model for forecasting travel time. The dataset used in this paper consists of travel time and travel speed information from the state of Missouri. The learning rate used for building each model was varied from 0.0001-0.01. The best learning rate was found to be 0.001. The study concluded that the ARIMA model was the best model architecture for travel time prediction and forecasting.