An interval-valued Pythagorean fuzzy approach to mitigate traffic congestion in densely populated cities with implications for sustainability

Abstract

Traffic congestion (TC) disrupts everyday life, elevating stress levels, extending commute durations, diminishing productivity, hampering air quality, and reducing the overall quality of life. Congestion exacerbates already-existing problems in densely populated cities and makes efficient urban planning more difficult. Hence, reducing TC is necessary to create sustainable and livable communities. Therefore, this study employs a novel hybrid multi-criteria decision-making (MCDM) framework, integrating the interval-valued Pythagorean fuzzy (IVPF) theory with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to analyze the challenges to mitigate TC in the densely populated urban areas of an emerging economy like Bangladesh. The challenges were identified through a review of existing literature, which was later validated by a panel of experts. The findings of the study suggest that the three most significant challenges to TC mitigation are “Lack of efficient coordination and management of traffic signals,” “Insufficient choices for public transportation,” and “Lack of integration of advanced data analytics and IoT-based technologies.” The anticipated impact of this study lies in its substantial contribution to future innovation and development in urban planning and management. This study aims to alleviate TC in densely populated cities and promote urban sustainability by aiding policymakers, urban planners, and stakeholders in formulating long-term strategies.