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Smart city article details
| Title | Conjecture Interaction Optimization Model For Intelligent Transportation Systems In Smart Cities Using Reciprocated Multi-Instance Learning For Road Traffic Management |
|---|---|
| ID_Doc | 15632 |
| Authors | Asmari A.F.A.; Almutairi A.; Alanazi F.; Alqubaysi T.; Armghan A. |
| Year | 2025 |
| Published | IEEE Access, 13 |
| DOI | http://dx.doi.org/10.1109/ACCESS.2025.3542847 |
| Abstract | Intelligent Transportation Systems (ITS) routing in smart cities is planned and maintained based on vehicle communication, demands, and terminal interaction. The influencing factors, such as road and traffic conditions, demand a prior conjecture to improve transportation, navigation comfort, and efficiency. Therefore, a Conjecture Interaction Optimization Model using terminal-communication assistance is introduced in this article. This model accounts for the road's physical condition and traffic density for setting up routing efficiency. The priorities are dynamic and based on the nearest assisting/ communicating terminal to improve the forecast on dynamic vehicle routing. The routing and traffic avoidance decisions are pursued using reciprocated multi-instance convergence learning (RMICL). RMICL algorithm optimizes real-time vehicle communication to analyze the vehicles' active and discarded interactions for minimum delay and route refinement. The MIL algorithm integrates multi-instance learning into the interaction labelling process, enhancing the routing prediction by differentiating between reliable and erroneous data. It considers improved traffic avoidance and ensuring convergence robustness in dynamic traffic management for smart cities. The convergence optimization is used to identify the low latency route decision outcomes from the communicating terminal. The routing identifies the possible combination of available interacting terminals with precision traffic forecast; the convergence must be slight between the conjecture and the actual routing traffic. Contrarily, reverse convergence instances identify traffic locations based on discarded interactions. The process is iterated from low traffic to highly interactive route searches based on location. Therefore, the characteristics of conjectures are updated with the forward and reverse reciprocation instances. This model leverages the ITS decision convergence over traffic and routing efficiencies. The results of the proposed model are identified as follows: this model improves traffic detection by 10.76% through 11.67% high interaction throughput to reduce 12.66% travel time for the distance-covered variant. |
| Author Keywords | ITS; MIL; reciprocated convergence; routing optimization; smart city |
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