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Smart roadside infrastructure sensors in the form of intelligenttransportation system stations (ITS-Ss) are increasingly deployed worldwide atrelevant traffic nodes. The resulting digital twins of the real environment aresuitable for developing and validating connected and automated drivingfunctions and for increasing the operational safety of intelligent vehicles byproviding ITS-S real-time data. However, ITS-Ss are very costly to establishand operate. The choice of sensor technology also has an impact on the overallcosts as well as on the data quality. So far, there is only insufficientknowledge about the concrete expenses that arise with the construction ofdifferent ITS-S setups. Within this work, multiple modular infrastructuresensor setups are investigated with the help of a life cycle cost analysis(LCCA). Their economic efficiency, different user requirements and sensor dataqualities are considered. Based on the static cost model, a Monte Carlosimulation is performed, to generate a range of possible project costs and toquantify the financial risks of implementing ITS-S projects of differentscales. Due to its modularity, the calculation model is suitable for diverseapplications and outputs a distinctive evaluation of the underlyingcost-benefit ratio of investigated setups.

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