Exploring Deep Learning Approaches to Predict Person and Vehicle Trips: An Analysis of NHTS Data

TL;DR

This paper demonstrates that deep learning models significantly improve the accuracy of predicting person and vehicle trips using NHTS data, surpassing traditional models and aiding transportation planning.

Contribution

The study introduces a deep learning approach for trip prediction that captures complex relationships in NHTS data, achieving higher accuracy than traditional models.

Findings

Deep learning achieved 98% accuracy for person trips.

Deep learning achieved 96% accuracy for vehicle trips.

Outperforms traditional transportation models.

Abstract

Modern transportation planning relies heavily on accurate predictions of person and vehicle trips. However, traditional planning models often fail to account for the intricacies and dynamics of travel behavior, leading to less-than-optimal accuracy in these predictions. This study explores the potential of deep learning techniques to transform the way we approach trip predictions, and ultimately, transportation planning. Utilizing a comprehensive dataset from the National Household Travel Survey (NHTS), we developed and trained a deep learning model for predicting person and vehicle trips. The proposed model leverages the vast amount of information in the NHTS data, capturing complex, non-linear relationships that were previously overlooked by traditional models. As a result, our deep learning model achieved an impressive accuracy of 98% for person trip prediction and 96% for vehicle trip estimation. This represents a significant improvement over the performances of traditional transportation planning models, thereby demonstrating the power of deep learning in this domain. The implications of this study extend beyond just more accurate predictions. By enhancing the accuracy and reliability of trip prediction models, planners can formulate more effective, data-driven transportation policies, infrastructure, and services. As such, our research underscores the need for the transportation planning field to embrace advanced techniques like deep learning. The detailed methodology, along with a thorough discussion of the results and their implications, are presented in the subsequent sections of this paper.