Data-driven estimation of travel latency cost functions via inverse optimization in multi-class transportation networks
作者： Jing Zhang , Ioannis Ch. Paschalidis
关键词: Variational inequality 、 Truck 、 Latency (engineering) 、 Ranging 、 Engineering 、 Inverse optimization 、 Mathematical optimization 、 Inverse problem 、 Data-driven 、 Inverse
摘要: We develop a method to estimate from data travel latency cost functions in multi-class transportation networks, which accommodate different types of vehicles with very characteristics (e.g., cars and trucks). Leveraging our earlier work on inverse variational inequalities, we datadriven approach the functions. Extensive numerical experiments using benchmark ranging moderate-sized large-sized, demonstrate effectiveness efficiency approach.
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