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- W3176552616 abstract "Problem definition: Service networks with open routing by self-interested customers have drawn attention in the theoretical literature (Arlotto et al. 2019, Parlakturk and Kumar 2004). However, these networks, which range from shopping centers to amusement parks, remain challenging to explore empirically. As choices, customers' physical-movement trajectories simultaneously reflect their aversions to congestion, demand for complementary groups of stations, and dynamic choice of station visit order. As such, large-scale trajectory datasets offer tremendous opportunities to understand customer motivations and behaviors but are complex to analyze. Methodology/results: We develop structural empirical methods to recover customer station preferences and congestion sensitivities from diverse trajectory patterns using machine learning. Specifically, we employ adversarial neural networks to handle combinatorially large choice sets of network trajectories. The methods accommodate customers' dynamic choices of routes to minimize total waiting and place theoretically efficient incentive-compatibility bounds on customers' preferences. Managerial implications: Our methods enable counterfactual studies of service network design, capacity management, and customer targeting. We use Monte Carlo experiments to show that the methods accurately recover customer station preferences and congestion sensitivities." @default.
- W3176552616 created "2021-07-05" @default.
- W3176552616 creator A5041740942 @default.
- W3176552616 date "2021-01-01" @default.
- W3176552616 modified "2023-10-16" @default.
- W3176552616 title "Strategic Choices and Routing Within Service Networks: Modeling and Estimation Using Neural Networks" @default.
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- W3176552616 doi "https://doi.org/10.2139/ssrn.3819117" @default.
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