Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis

TL;DR

This paper introduces a novel traffic prediction approach that incorporates urban human activity data into graph convolutional models, significantly improving accuracy while maintaining computational efficiency.

Contribution

It integrates human activity frequency data into existing graph-based traffic prediction models, enhancing their ability to capture causal relationships in urban traffic patterns.

Findings

Achieves state-of-the-art prediction accuracy

Utilizes human activity data to improve traffic inference

Maintains low computational overhead

Abstract

Traffic prediction is one of the key elements to ensure the safety and convenience of citizens. Existing traffic prediction models primarily focus on deep learning architectures to capture spatial and temporal correlation. They often overlook the underlying nature of traffic. Specifically, the sensor networks in most traffic datasets do not accurately represent the actual road network exploited by vehicles, failing to provide insights into the traffic patterns in urban activities. To overcome these limitations, we propose an improved traffic prediction method based on graph convolution deep learning algorithms. We leverage human activity frequency data from National Household Travel Survey to enhance the inference capability of a causal relationship between activity and traffic patterns. Despite making minimal modifications to the conventional graph convolutional recurrent networks and graph convolutional transformer architectures, our approach achieves state-of-the-art performance without introducing excessive computational overhead.