Akademik Veri Yönetim Sistemi
Logistics 4.0: Digital Transformation of Supply Chain Management, Turan Paksoy,Cigdem Gonul Kochan,Sadia Samar Ali, Editör, CRC, New York , Florida, ss.126-135, 2020
This chapter aims to apply the Long Short Term Memory (LSTM) model to predict accurate bus arrival time for public transportation system. It examines the improved methodology for real application utilization. Public transportation is an important issue for the city planner or decision maker. It has a direct impact on the all aspect of the community such as economy, education, health and entertainment activities. Number of transfers, total travel time and cost from origin to destination are important indicators for the passenger. These indicators should be optimized by passenger preferences. The bus arrival time information can decrease the passenger waiting time, make passenger informative and thus able to arrange their trip plans and choose suitable travelling routes. S. Hochreiter and J. Schmidhuber developed the LSTM network as a special kind of recurrent neural network. It has special structures of memory blocks and cells and has been successful in prediction for different application areas.