Individual-level modeling is an essential requirement for effective deployment of smart urban mobility applications. Mode choice behavior is also a core feature in transportation planning models, which are used for analyzing future policies and sustainable plans such as greenhouse gas emissions reduction plans. Specifically, an agent-based model requires an individual level choice behavior, mode choice being one such example. However, traditional utility-based discrete choice models, such as logit models, are limited to aggregated behavior analysis. This paper develops a model employing a deep neural network structure that is applicable to the travel mode choice problem. This paper uses deep learning algorithms to highlight an individual-level mode choice behavior model, which leads us to take into account the inherent characteristics of choice models that all individuals have different choice options, an aspect not considered in the neural network models of the past that have led to poorer performance. Comparative analysis with existing behavior models indicates that the proposed model outperforms traditional discrete choice models in terms of prediction accuracy for both individual and aggregated behavior.

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The collapse of Terra's algorithmic stablecoin UST shocked the cryptocurrency market. This study investigates the underlying causes of the de-pegging event through an in- depth analysis of the token economics of the Terra blockchain. Using on-chain data, this study identifies a misalignment in the economic incentive structure of the blockchain protocol as a key contributor to the de-pegging. It is found that an undercompensation of UST when it was redeemed played a significant role in the de-pegging event, with the UST price on cryptocurrency exchanges following the redeemed value of UST that users could obtain by swapping UST for LUNA and selling it on the market. The results highlight the importance of properly designing the incentive structure of blockchain protocols to ensure their sustainability and security.