# System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic

## TL;DR

This paper addresses the challenges of hybrid vehicular traffic with heterogeneous autonomy levels, proposing a system-of-systems model that integrates traffic, data science, and communication methods for data-driven traffic optimization.

## Contribution

It introduces a novel system-of-systems model for hybrid traffic management that combines multiple approaches for efficient data transfer and dynamic vehicle routing.

## Key findings

- Effective data transfer strategies for hybrid traffic
- Improved dynamic routing methods
- Enhanced infrastructure utilization

## Abstract

While the development of fully autonomous vehicles is one of the major research fields in the Intelligent Transportation Systems (ITSs) domain, the upcoming longterm transition period - the hybrid vehicular traffic - is often neglected. However, within the next decades, automotive systems with heterogeneous autonomy levels will share the same road network, resulting in new problems for traffic management systems and communication network infrastructure providers. In this paper, we identify key challenges of the upcoming hybrid traffic scenario and present a system-of-systems model, which brings together approaches and methods from traffic modeling, data science, and communication engineering in order to allow data-driven traffic flow optimization. The proposed model consists of data acquisition, data transfer, data analysis, and data exploitation and exploits real world sensor data as well as simulative optimization methods. Based on the results of multiple case studies, which focus on individual challenges (e.g., resource-efficient data transfer and dynamic routing of vehicles), we point out approaches for using the existing infrastructure with a higher grade of efficiency.