Bus Arrival Time Prediction Using Decomposition Technology and Deep Learning

<div class="section abstract"><div class="htmlview paragraph">Bus arrival time prediction is an important part of urban bus operation, which maintains the stability and punctuality of the bus system. Providing accurate public transport travel service information can attract more public transport travelers, thereby improving the public transport share. By knowing the arrival time of the bus in advance, travelers can arrange their travel time more effectively and reduce their waiting time at bus stops. In addition, accurate bus arrival time also contributes to the design, development and management of the bus system, promoting better resource scheduling and lower operating costs. However, the prediction based on historical data cannot cope with the complex changes of real-time traffic conditions and meet the requirements of public transportation information system. Therefore, current research is increasingly focused on improving the accuracy of real-time prediction, while prediction models are exploring and adjusting to adapt to complex traffic conditions and real-time changes. In this paper, an innovative prediction method is proposed, which integrate Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Temporal Convolutional Network (TCN), and Long Short-Term Memory (LSTM). This hybrid methodology can be used as a real-time tool to provide effective short-term bus arrival time prediction without the need of additional variable input. Finally, a case study was carried out based on the actual data of Xuzhou Route 1 bus, so the proposed method can be empirically evaluated and compared with other methods. As a result, the proposed model offers higher prediction accuracy in bus arrival time prediction, demonstrating superior performance compared to traditional prediction methods.</div></div>