Rapid urbanization in big cities worldwide leads to severe traffic congestion and damages the urban living environment. Intelligent Transportation Systems (ITS) play a crucial role in addressing the growing challenges of urban transportation, such as congestion, pollution, and safety concerns. However, there is a lack of studies to present an overall framework to deploy ITS efficiently in urban areas considering each city's unique conditions. Therefore, this paper develops a three-stage framework to simplify the process of efficient deployment of ITS application services in a specific city. Three stages include identifying potential ITS services, evaluating and ranking the priority of ITS application services, and analyzing legal requirements as well identifying any gaps or areas where the current legal framework may fall short in accommodating ITS technologies. The three-stage framework considers the specific transportation challenges and requirements of each urban area/city, taking into account factors such as traffic patterns, infrastructure limitations, environmental considerations, and future development plans. A case study in Hai Phong city was presented where expert surveys were conducted to assess the current transport situation, evaluate satisfaction and expectation regarding applying ITS services. By providing a comprehensive framework for the efficient deployment of ITS in urban areas, this paper offers valuable insights and guidance to policymakers, transportation planners, and researchers. The framework can be as reference for other similar cities grappling with transportation challenges, facilitating the implementation of effective ITS solutions to transform advanced and sustainable urban transportation systems.
The objective of this study is to identify the existence of spillover and leverage effects from returns and return volatilities of high yield and low yield dividend ETFs on tracing market stock indices, and vice versa. The Generalized Autoregressive Conditional Heteroscedasticity-in-Mean-Autoregressive Moving Average (GARCH-M-ARMA) and the Exponentially Generalized Autoregressive Conditional Heteroscedasticity-in Mean Autoregressive Moving Average (EGARCH-M-ARMA) are utilized by authors. The six ETFs, recognized in Top 100 ETFs of etfdb.com database, with their underlying indices are selected to represent the high and low dividend yield ETFs group. The findings show that the spillover effect in return is more happening in a group of low yield dividend ETFs, while the spillover effect on return volatilities is more dominant in a group of high yield dividend ETFs. In the case of the leverage effect, it exists in all ETFs and the stock Index, in which the negative asymmetric volatility effect more happens when comparing the positive asymmetric volatility effect.
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