Intelligent traffic management with Mamba-ATSP: Optimizing signal control and path planning using multi-modal data fusion

Efficient processing of multi-source data and dynamic environments is essential for addressing the complex multi-task challenges in intelligent traffic signal control and path planning. Traditional models cannot capture time dependence, global information, and non-linearity relationships inherent in traffic flows. Optimization performance is limited. for dealing with these challenges, we introduce Mamba-ATSP, self-attention mechanism and manaba framework are integrated. Mamba-ATSP In spatial time management, traffic signal optimization and route planning are characterized by modules that integrate multi modal data mission planning and ego attention. Experimental results demonstrate that Mamba-ATSP outperforms established models, such as DeepTraffic, ST-Mamba, and LightPath, on the PEMS and HighD datasets, achieving superior accuracy and efficiency across key metrics like traffic flow, path selection time, average waiting time, and real-time responsiveness. Ablation studies underscore the significance of each module, emphasizing the crucial roles of multi-modal fusion, self-attention, and the Mamba framework. Mamba-ATSP not only does it improve traffic signal control and operation planning, but also shows strong adaptability in dynamic traffic environments and extends its potential to other intelligent traffic systems.