Unified Mobility Estimation Mode

TL;DR

This paper proposes a unified model for human mobility estimation that reduces data requirements and computational costs, enabling large-scale mobility predictions similar to weather forecasting models.

Contribution

It introduces a novel concept combining recent mobility theorems to simplify and scale mobility prediction without detailed agent-based simulations.

Findings

Reduces data needs for mobility prediction

Lowers computational costs for large-scale modeling

Enables global mobility forecasts

Abstract

In literature, scientists describe human mobility in a range of granularities by several different models. Using frameworks like MATSIM, VehiLux, or Sumo, they often derive individual human movement indicators in their most detail. However, such agent-based models tend to be difficult and require much information and computational power to correctly predict the commutation behavior of large mobility systems. Mobility information can be costly and researchers often cannot acquire it dynamically over large areas, which leads to a lack of adequate calibration parameters, rendering the easy and spontaneous prediction of mobility in additional areas impossible. This paper targets this problem and represents a concept that combines multiple substantial mobility theorems formulated in recent years to reduce the amount of required information compared to existing simulations. Our concept also targets computational expenses and aims to reduce them to enable a global prediction of mobility. Inspired by methods from other domains, the core idea of the conceptional innovation can be compared to weather models, which predict weather on a large scale, on an adequate level of regional information (airspeed, air pressure, etc.), but without the detailed movement information of each air atom and its particular simulation.