Economic Analysis of Smart Roadside Infrastructure Sensors for Connected and Automated Mobility

TL;DR

This paper evaluates the costs and benefits of various modular roadside sensor setups for connected and automated vehicles using a life cycle cost analysis and Monte Carlo simulation to inform economic decisions.

Contribution

It introduces a modular cost model and simulation framework to assess the economic efficiency and risks of deploying different ITS-S sensor configurations.

Findings

Cost variability depends on sensor technology and scale.

Modular setups offer flexible cost-benefit analysis.

Financial risks can be quantified with Monte Carlo simulations.

Abstract

Smart roadside infrastructure sensors in the form of intelligent transportation system stations (ITS-Ss) are increasingly deployed worldwide at relevant traffic nodes. The resulting digital twins of the real environment are suitable for developing and validating connected and automated driving functions and for increasing the operational safety of intelligent vehicles by providing ITS-S real-time data. However, ITS-Ss are very costly to establish and operate. The choice of sensor technology also has an impact on the overall costs as well as on the data quality. So far, there is only insufficient knowledge about the concrete expenses that arise with the construction of different ITS-S setups. Within this work, multiple modular infrastructure sensor setups are investigated with the help of a life cycle cost analysis (LCCA). Their economic efficiency, different user requirements and sensor data qualities are considered. Based on the static cost model, a Monte Carlo simulation is performed, to generate a range of possible project costs and to quantify the financial risks of implementing ITS-S projects of different scales. Due to its modularity, the calculation model is suitable for diverse applications and outputs a distinctive evaluation of the underlying cost-benefit ratio of investigated setups.