Gap setting control strategy for connected and automated vehicles in freeway lane‐drop bottlenecks
Abstract Commercial automated vehicles equipped with adaptive cruise control (ACC) systems offer multiple gap settings that determine their longitudinal behaviour. This study introduces two novel strategies—inflow control and combined control—that leverage the distinct driving behaviours associated...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12538 |
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| Summary: | Abstract Commercial automated vehicles equipped with adaptive cruise control (ACC) systems offer multiple gap settings that determine their longitudinal behaviour. This study introduces two novel strategies—inflow control and combined control—that leverage the distinct driving behaviours associated with different gap settings in connected and automated vehicles. These strategies aim to enhance traffic efficiency in freeway lane‐drop bottlenecks, where capacity drops are common, by maintaining bottleneck occupancy at the target level using a proportional‐integral‐derivative controller. Simulation experiments were conducted using VISSIM to validate the proposed strategies. The results from a hypothetical lane‐drop bottleneck indicate that the proposed strategies enhanced both efficiency and safety across all simulated demand levels, with the combined control outperforming inflow control by redistributing the relative positions of vehicles before the mandatory lane changes using a new gap setting. Moreover, the proposed strategies were effective under all the simulated market penetration rates (MPRs), where better performances were demonstrated at higher MPRs. An evaluation of a calibrated real‐world network further demonstrated the potential of recommending gap settings to drivers of ACC‐equipped vehicles using variable message signs to enhance freeway efficiency in the near future. |
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| ISSN: | 1751-956X 1751-9578 |