A Data-Driven Approach to Enhancing Gravity Models for Trip Demand Prediction

TL;DR

This paper presents a machine learning-enhanced gravity model that significantly improves trip demand prediction accuracy by integrating diverse datasets, offering a more reliable tool for transportation planning and policy-making.

Contribution

It introduces a novel data-driven extension to the traditional gravity model using machine learning, capturing complex interactions for better trip demand forecasting.

Findings

51.48% improvement in R-squared

63.59% reduction in MAE

44.32% increase in CPC

Abstract

Accurate prediction of trips between zones is critical for transportation planning, as it supports resource allocation and infrastructure development across various modes of transport. Although the gravity model has been widely used due to its simplicity, it often inadequately represents the complex factors influencing modern travel behavior. This study introduces a data-driven approach to enhance the gravity model by integrating geographical, economic, social, and travel data from the counties in Tennessee and New York state. Using machine learning techniques, we extend the capabilities of the traditional model to handle more complex interactions between variables. Our experiments demonstrate that machine learning-enhanced models significantly outperform the traditional model. Our results show a 51.48% improvement in R-squared, indicating a substantial enhancement in the model's explanatory power. Also, a 63.59% reduction in Mean Absolute Error (MAE) reflects a significant increase in prediction accuracy. Furthermore, a 44.32% increase in Common Part of Commuters (CPC) demonstrates improved prediction reliability. These findings highlight the substantial benefits of integrating diverse datasets and advanced algorithms into transportation models. They provide urban planners and policymakers with more reliable forecasting and decision-making tools.