Exploring the influence of automated driving styles on network efficiency
| Autor: | Damir Varesanovic, Moeid Qurashi, Qing-Long Lu, Jaka Sodnik, Constantinos Antoniou |
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| Rok vydání: | 2021 |
| Předmět: |
Traffic efficiency
050210 logistics & transportation Computer science 05 social sciences Real-time computing 0211 other engineering and technologies Stability (learning theory) Ranging 02 engineering and technology Traffic flow Inner city 021105 building & construction 0502 economics and business Traffic conditions Cluster analysis Traffic simulator |
| Zdroj: | Transportation Research Procedia. 52:380-387 |
| ISSN: | 2352-1465 |
| DOI: | 10.1016/j.trpro.2021.01.094 |
| Popis: | Automated vehicle technology can be beneficial for many aspects of transport, especially, improving traffic flow stability and efficiency. However, the influence of different automated driving styles on traffic efficiency is still not fully understood. Transport systems are very complex and non-linear, i.e. many participants with different characteristics interact with each other and the aggregated result of their interactions could cause a remarkable change in the entire network. Considering that automated vehicles with different driving styles interact with the environment in different ways, we try to understand the influence of different automated driving styles (e.g., cautious, normal, aggressive) on the important variables in traffic flow theory (e.g., speed) to reveal their impact on network efficiency. Characteristics of these driving styles are extracted by clustering the highD dataset and then, translated into different car-following models for simulation in the SUMO traffic simulator environment. Multiple scenarios of mixed traffic conditions (i.e. ranging different ratios of driving styles) are simulated on the network of Munich inner city. |
| Databáze: | OpenAIRE |
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