Travel time prediction is one of the most important parameters to forecast network-wide traffic conditions. Travelers can access traffic roadway networks and arrive in their destinations at the lowest costs guided by accurate travel time estimation on alternative routes. In this study, we propose a long short-term memory (LSTM)-based deep learning model, deep learning on spatiotemporal features with Convolution Neural Network (DLSF-CNN), to extract the spatial–temporal correlation of travel time on different routes to accurately predict route travel time. Specifically, this model utilizes network-wide travel time, considering its topological structure as inputs, and combines convolutional neural network and LSTM techniques to accurately predict travel time. In addition to their spatial dependence, both coarse-grained and fine-grained temporal dependences are fully considered among the road segments along a route as well. The shift problem is formulated in the coarse-grained granularity to predict the route travel time in the next time interval. The experimental tests were conducted using real route travel time obtained by taxi trajectories in Harbin. The test results show that the travel time prediction accuracy of DLSF-CNN is above 90%. Meanwhile, the proposed model outperformed the other machine learning models based on multiple evaluation criteria. The RMSE (Root Mean Squard Error) and R² (R Squared) increased by 18.6% and 22.46%, respectively. The results indicate the proposed model performs reasonably well under prevailing traffic conditions.

Space-energy driven laser-ablation debris removal is a feasible way to address the worsening space debris environment. Energy analysis is crucial for the design and run of a space-energy driven laser-ablation debris removal system. This study details the energy analysis of a space-energy driven laser-ablation debris removal system as affected by laser energy, frequency and range. The results show that the laser irradiation time and energy efficiency are decreased with increases in the laser energy and frequency, and the energy efficiency in the case of different planes is significantly lower than that in the case of coplanar. However, laser range has no effect on the perigee changing and energy efficiency. The results can effectively guide the removal scheme design and evaluation.