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Smart city article details
| Title | Integrative Hybrid Information Systems For Enhanced Traffic Maintenance And Control In Bangalore: A Synchronized Approach |
|---|---|
| ID_Doc | 32238 |
| Authors | Jain V.; Mitra A. |
| Year | 2024 |
| Published | Hybrid Information Systems: Non-Linear Optimization Strategies with Artificial Intelligence |
| DOI | http://dx.doi.org/10.1515/9783111331133-012 |
| Abstract | This research introduces a novel methodology for urban traffic management by proposing and executing a hybrid traffic management system. This system has been developed with the aim of improving traffic efficiency, minimizing environmental consequences, and fostering sustainable urban mobility through the integration of geo-graphic information systems (GIS), artificial intelligence (AI), the Internet of things (IoT), and big data analytics. It fits well with the book's focus on hybrid information systems and synergizing multiple data sources and AI techniques for optimized real- world outcomes. Specifically, it showcases the development of an integrative traffic management and control system for Bangalore by combining sensor data, computer vision, machine learning (ML), and rule-based expert systems. The hybrid system exemplifies leveraging diverse data like traffic volume, weather, and events calendars to model and predict congestion hotspots in the city. Deep learning techniques enable processing image and video data for automated traffic pattern analysis. Expert systems incorporate domain knowledge of traffic engineers to refine signals timing based on contextual factors. Overall, it highlights the synchronization of data, models, and knowledge sources to build an intelligent system that can optimize dynamic traffic flows. The results of this study demonstrate noteworthy enhancements in the reduction of emissions, improvements in energy efficiency, and an increase in the public's acceptance of sustainable transportation modes following the implementation of the measures. The system exhibited a reduction of 20% in automobile emissions and a decrease of 15% in energy consumption for traffic control devices. Additionally, there was an observable rise in public transport ridership and bicycle utilization. The aforementioned findings underscore the efficacy of the system in tackling significant urban traffic issues and promoting environmental sustainability. The chapter finishes by engaging in a comprehensive analysis of the theoretical and practical implications derived from the findings. This analysis highlights the considerable potential of technology-driven solutions in the fields of urban planning and policy-making. The study makes a significant contribution to the realm of smart city endeavors, providing vital insights that can inform future advancements in the domain of sustainable urban traffic management. © 2024 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. |
| Author Keywords | GIS; IoT; Smart city; Sustainable mobility; Urban traffic management |
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