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.

While much scholarly attention has been paid to ways in which metropolitan areas are politically structured and operated to achieve a dual goal, economic growth, and equality, relatively less is known about the complex relationship between metropolitan governance structures and growth–inequality dynamics. This study investigates how and to what extent metropolitan governance structures shape regional economic growth and inequality trajectories using data for 267 US metropolitan areas from 1990 to 2010. Findings from a two-stage least squares regression analysis suggest that economic growth is associated with governance structures in a nonlinear fashion, with relatively more rapid growth rates in both highly centralized and decentralized metropolitan areas. However, these regions are also found to experience a larger increase in income inequality, indicating an important trade-off to be considered carefully in exploring ways to reform existing governance settings. These findings further suggest that the so-called growth–inequality trade-off may exist not only in their direct interactions but through their connections via governance or other variables.

부동산은 공간적인 특성을 가지고 있다는 점에서 다른 자산과 차별되는 특성을 갖는다. 공간적인 측면으로 접근하여, 본 연구에서의 데이터는 2013년 1분기부터 2023년 2분기까지의 서울시 3,000세대 이상의 아파트 단지이며, 방법론은 딥러닝 기반의 시계열 분석 기법인 RNN, LSTM, GRU이다. 이를 통해 3,000세대 이상의 아파트 단지들이 특정 지역구의 아파트 단지 가격에 어느 정도 영향을 미치는 지 확인할 수 있다. 모델별 평균 정확도를 MAPE를 통해 산출한 결과, RNN이 10.91, LSTM이 11.44, GRU가 11.12로서 RNN이 가장 우수했고, LSTM과 GRU가 비슷하였다. 추후 본 연구에서 제안하는 모형을 활용하여 높은 정확도의 부동산 가격 예측을 하는데 도움이 될 것으로 기대한다.