Portal de Periódicos da CAPES

Dynamic Bus Arrival Time Prediction with Artificial Neural Networks

2002; American Society of Civil Engineers; Volume: 128; Issue: 5 Linguagem: Inglês

10.1061/(asce)0733-947x(2002)128

1943-5436

Steven Chien, Yuqing Ding, Chien‐Hung Wei,

Traffic control and management

Transit operations are interrupted frequently by stochastic variations in traffic and ridership conditions that deteriorate schedule or headway adherence and thus lengthen passenger wait times. Providing passengers with accurate vehicle arrival information through advanced traveler information systems is vital to reducing wait time. Two artificial neural networks (ANNs), trained by link-based and stop-based data, are applied to predict transit arrival times. To improve prediction accuracy, both are integrated with an adaptive algorithm to adapt to the prediction error in real time. The bus arrival times predicted by the ANNs are assessed with the microscopic simulation model CORSIM, which has been calibrated and validated with real-world data collected from route number 39 of the New Jersey Transit Corporation. Results show that the enhanced ANNs outperform the ones without integration of the adaptive algorithm.