Route Temporal⁃Spatial Information Based Residual Neural Networks for Bus Arrival Time Prediction

Bus arrival time prediction contributes to the quality improvement of public transport services.Passengers can arrange departure time effectively if they know the accurate bus arrival time in advance.We proposed a machine⁃learning approach,RTSI⁃ResNet,to forecast the bus arrival time at target stations.The residual neural network framework was employed to model the bus route temporal⁃spatial information.It was found that the bus travel time on a segment between two stations not only had correlation with the preceding buses,but also had common change trends with nearby downstream/upstream segments.Two features about bus travel time and headway were extracted from bus route including target section in both forward and reverse directions to constitute the route temporal⁃spatial information,which reflects the road traffic conditions comprehensively.Experiments on the bus trajectory data of route No.10 in Shenzhen public transport system demonstrated that the proposed RTSI⁃ResNet outperformed other well⁃known methods(e.g.,RNN/LSTM,SVM).Specifically,the advantage was more significant when the distance between bus and the target station was farther.

Transforming information from spin encoding to route encoding

Spin （polarization） is widely used in free-space optics, while in photouic integrated circuits （PICs）, infor- mation is usually encoded in optical route. So a practical way to connect these two encoding methods is necessary for information communication. In this letter, an encoding convertor is designed to connect spin encoding and route encoding. Finite element method is used to calculate the conversion efficiency and extinction ratio of the encoding convertor and the theoretical analyses are also given. Our protocol shows a friendly way to convert optical spin information to route information, which will promote the compatibility of free-space optics and PICs.