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In this paper, we investigated the performance of cooperative adaptive cruise control (CACC) algorithms in mixed traffic environments featuring connected automated vehicles (CAVs) and unconnected vehicles. For CAVs, we tested the recently proposed linear feedback control approach (Linear-CACCu) and adaptive model predictive control approach (A-MPC-CACCu) which have been tailored to extend CACC to mixed traffic environments. In contrast to most literature where CACC design and evaluation are performed on freeways, we focused on urban arterial roads using the CACC Field Operation Test Dataset from the Netherlands. We compared the performances of Linear-CACCu and A-MPC-CACCu to regular adaptive cruise control (ACC), where automated vehicles do not rely on connectivity, as well as human drivers. Performance comparison was done in terms of ego vehicle's spacing error, acceleration, and energy consumption which relate to safety, driving comfort, and energy efficiency, respectively. Simulation results showed that CACCu algorithms significantly outperformed the ACC and human drivers in these metrics. Moreover, we found that the fluctuations of the lead vehicle's behavior due to changes in traffic signal phase have a significant impact on which CACCu is optimal (i.e., A-MPC-CACCu or Linear-CACCu). Thus, the CACC mode could be switched based on the expectation of traffic signal phase changes to assure better performance. |
