Trends in urban flows: A transfer entropy approach

TL;DR

This paper introduces a transfer entropy method to analyze human movement patterns in urban environments using high-resolution sensor data, aiding urban planning and management decisions.

Contribution

It applies transfer entropy to urban human flow data, providing a novel approach to detect trends without compromising privacy.

Findings

Transfer entropy effectively captures human movement trends.

The method reveals dynamic flow patterns at fine spatial and temporal scales.

Results support urban planning and emergency response applications.

Abstract

The accurate estimation of human activity in cities is one of the first steps towards understanding the structure of the urban environment. Human activities are highly granular and dynamic in spatial and temporal dimensions. Estimating confidence is crucial for decision-making in numerous applications such as urban management, retail, transport planning and emergency management. Detecting general trends in the flow of people between spatial locations is neither obvious nor easy due to the high cost of capturing these movements without compromising the privacy of those involved. This research intends to address this problem by examining the movement of people in a SmartStreetSensors network at a fine spatial and temporal resolution using a Transfer Entropy approach.