Enhance traffic flow prediction with Real-Time Vehicle Data Integration
| dc.contributor.author | Jain, R | |
| dc.contributor.author | Dhingra, S | |
| dc.contributor.author | Joshi, K | |
| dc.contributor.author | Rana, A | |
| dc.contributor.author | Goyal, N | |
| dc.date.accessioned | 2024-04-16T04:34:53Z | |
| dc.date.available | 2024-04-16T04:34:53Z | |
| dc.date.issued | 2023-08 | |
| dc.description.abstract | This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/1406 | |
| dc.language.iso | en | en_US |
| dc.title | Enhance traffic flow prediction with Real-Time Vehicle Data Integration | en_US |
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