Towards Attention based ConvLSTM for Long-Term Travel Time Prediction of Bus Journey

Travel time prediction is critical for advanced travelerinformation systems (ATISs), which

provides valuable information for enhancing the efficiency and effectiveness of the urban

transportation systems. However, in the area of bus trips, existing studies have focused on directly

using the structured data to predict travel time for a single bus trip. For state‐of‐the‐art public

transportation information systems, a bus journey generally has multiple bus trips. Additionally,

due to the lack of study on data fusion, it is even inadequate for the development of underlying

intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data‐

driven travel time prediction model for bus journeys based on open data. We explore a

convolutional long short‐term memory (ConvLSTM) model with a self‐attention mechanism that

accurately predicts the running time of each segment of the trips and the waiting time at each

station. The model is more robust to capture long‐range dependence in time series data as well.