Graph Input Representations for Machine Learning Applications in Urban Network Analysis

TL;DR

This paper investigates how different graph input representations, incorporating topological and temporal features, affect machine learning model performance in urban network analysis, specifically predicting taxi tips in New York.

Contribution

It introduces and evaluates six novel graph input representations that include temporal and topological data for urban network path analysis.

Findings

Temporal-based representations improve prediction accuracy.

Best RMSE achieved is 1.42$ with temporal information.

Different representations significantly impact model performance.

Abstract

Understanding and learning the characteristics of network paths has been of particular interest for decades and has led to several successful applications. Such analysis becomes challenging for urban networks as their size and complexity are significantly higher compared to other networks. The state-of-the-art machine learning (ML) techniques allow us to detect hidden patterns and, thus, infer the features associated with them. However, very little is known about the impact on the performance of such predictive models by the use of different input representations. In this paper, we design and evaluate six different graph input representations (i.e., representations of the network paths), by considering the network's topological and temporal characteristics, for being used as inputs for machine learning models to learn the behavior of urban networks paths. The representations are validated and then tested with a real-world taxi journeys dataset predicting the tips using a road network of New York. Our results demonstrate that the input representations that use temporal information help the model to achieve the highest accuracy (RMSE of 1.42$).