Urban sustainable transportation planning strategies for livable City's quality of life

Abstract

Urban planning and its relevant transportation deploying have a particularly profound influence on the sustainability and livability of a city, and which also be crucial to the quality of life to urban residents at the same time. It was also suggested that the conception of livability should be extended to embrace the concerns associated with the sustainability. However, planning frameworks or assessment patterns that address the dynamics of urban planning and demand for transportation deploying are relatively rare; there also few public policies in related research fields have discussed the effects of the changes in various assessment indicators over time. Furthermore, following the rising advancements in social communication and computer technologies in modern society, the data collection, storage, and processing capabilities of people have improved substantially. And, the emergence of big data or extendible open data facilitates analysis and prediction availability, and enabled people to find immediate solutions to numerous dilemmas encountered. Therefore, based on the aforementioned intention, treating the city as a dynamic process with the trying of introducing the big data or extendible open data for facilitating urban sustainability and livability is undoubtedly worth to explore in further.The present study intends to initially examine the application of big data in sustainable and livable transportation strategies in Taipei City, Taiwan. Firstly, we investigate previous research on transportation sustainability in various countries to generalize our preliminary list of transportation sustainability indices that satisfy the principles of livable cities. And, key indices were then selected through the Fuzzy Delphi Method by administering a questionnaire to six experts from industrial, governmental, and academic sectors respectively. The research results were applied to develop decision-making strategies for responding to the environmental dynamics of Taipei City's transportation infrastructure system by using the analytic network process combined with a data-mining technique. Thus, big data pertaining to urban transportation were analyzed to predict the future dynamic trends of the key indices and prioritize the sustainable transportation strategies for a livable city under dynamic temporal and spatial changes. Ultimately, the policy implications of this study can not only offer a solution for current needs related to urban planning but also serve as a more transparent decision-making or well selection basis for developing sustainable and livable urban life in near future.