Transfer Learning for Transportation Demand Resilience Pattern Prediction Using Floating Car Data
Ningkang Yang,
Qing-Long Lu,
Cheng Lyu
et al.
Abstract:Understanding the response of a transportation system to disruptive events is significant for evaluating the resilience of the system. However, data collection during such events is always challenging, and the data volume is insufficient for building a robust model. Transfer learning provides an effective solution to this problem. In this study, we propose a floating car data (FCD) driven transfer learning framework for predicting the resilience of target transportation systems to similar disruptive events to … Show more
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