Multi-Output Bus Travel Time Prediction Using Convolutional LSTM Neural Networks

Ensuring accurate and dependable predictions of bus arrival times is essential to improving public transportation services and maintaining their appeal in urban settings. Such predictions, whether displayed on electronic boards or integrated into mobile applications, enable passengers to make better travel decisions, such as choosing alternate routes, anticipating delays, or avoiding missed connections. Furthermore, advanced Intelligent Transport Systems (ITS) utilize this information to facilitate smoother passenger transfers by holding delayed services within predefined limits. However, as urban congestion and travel time unpredictability grow, traditional methods face significant challenges in providing reliable predictions, making the problem increasingly complex. This research focuses on developing a robust system for forecasting bus arrival times in Astana city, utilizing extensive spatio-temporal data from two datasets. Multiple machine learning and deep learning models are implemented and compared to achieve this goal. These include K-means clustering to classify bus routes, K-Nearest Neighbors (KNN) for predictions based on proximity, and a Conv-LSTM model, which integrates convolutional and long short-term memory layers to address intricate temporal and spatial correlations. Support Vector Machines (SVM) and regression models are also incorporated to establish benchmarks and comparative insights. Through empirical evaluation, the proposed models demonstrate varying strengths, with the Conv-LSTM model showing exceptional performance in adapting to dynamic urban conditions and detecting subtle fluctuations in bus travel times. The findings highlight the transformative potential of sophisticated predictive modeling techniques to enhance urban transit systems, ensuring passengers receive timely and accurate information while improving overall operational efficiency.