Neuroevolved bi-directional LSTM applied to zero emission zones management in urban transport

Abstract

Plugin hybrid electric buses (PHEB) are nowadays an essential technology for the decarbonization of cities, proposing a more flexible solution than fully electric buses. There are a few studies for the design of operational strategies for PHEBs (i.e., to choose the propulsion method to use along the route) to minimize tailpipe emissions and/or maximize the electric range of the bus. However, all of them offer static strategies that cannot dynamically adapt to unforeseen traffic events, which can significantly impact on the performance of the bus. In this work, we propose modeling the problem of finding optimal PHEB operational strategies as a classification problem, allowing the generation of pseudo-optimal strategies in very short computational times. The method designed, called ML-EPBO, takes decisions based on local information of the bus, as well as from the route, so it can adapt to any events causing unexpected traffic conditions. Neuroevolution is used in this work to fine-tune ML-EPBO for optimal performance. Results show how ML-EPBO instantly offer solutions highly competitive with the best existing techniques in the literature, which do not offer the possibility of dynamically adapting to unexpected traffic events because they make use of global knowledge of the problem and have high computational complexity. Indeed, the average accuracy value obtained by the model is over 99.2%, and the strategies proposed are predicted over 70% of the cases with 100% of accuracy, even outperforming the state of the art results by up to 4.7% in the level of emissions or 1.82% in the electric range.