Conjecture Interaction Optimization Model for Intelligent Transportation Systems in Smart Cities Using Reciprocated Multi-Instance Learning for Road Traffic Management
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 eff...
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2025-01-01
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| author | Abdullah Faiz Al Asmari Ahmed Almutairi Fayez Alanazi Tariq Alqubaysi Ammar Armghan |
| author_facet | Abdullah Faiz Al Asmari Ahmed Almutairi Fayez Alanazi Tariq Alqubaysi Ammar Armghan |
| author_sort | Abdullah Faiz Al Asmari |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-18c850a25ecd4ccab1f58fc37d254f3e |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-18c850a25ecd4ccab1f58fc37d254f3e2025-08-20T02:03:42ZengIEEEIEEE Access2169-35362025-01-0113345393456210.1109/ACCESS.2025.354284710891544Conjecture Interaction Optimization Model for Intelligent Transportation Systems in Smart Cities Using Reciprocated Multi-Instance Learning for Road Traffic ManagementAbdullah Faiz Al Asmari0https://orcid.org/0000-0001-5949-8155Ahmed Almutairi1https://orcid.org/0009-0001-7564-9942Fayez Alanazi2https://orcid.org/0000-0003-2975-3711Tariq Alqubaysi3https://orcid.org/0009-0002-5049-1189Ammar Armghan4https://orcid.org/0000-0002-9062-7493Civil Engineering Department, College of Engineering, King Khalid University, Abha, Saudi ArabiaDepartment of Civil and Environmental Engineering, College of Engineering, Majmaah University, Majmaah, Saudi ArabiaCivil Engineering Department, College of Engineering, Jouf University, Sakaka, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Northern Border University, Arar, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka, Saudi ArabiaIntelligent 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.https://ieeexplore.ieee.org/document/10891544/ITSMILreciprocated convergencerouting optimizationsmart city |
| spellingShingle | Abdullah Faiz Al Asmari Ahmed Almutairi Fayez Alanazi Tariq Alqubaysi Ammar Armghan Conjecture Interaction Optimization Model for Intelligent Transportation Systems in Smart Cities Using Reciprocated Multi-Instance Learning for Road Traffic Management IEEE Access ITS MIL reciprocated convergence routing optimization smart city |
| title | Conjecture Interaction Optimization Model for Intelligent Transportation Systems in Smart Cities Using Reciprocated Multi-Instance Learning for Road Traffic Management |
| title_full | Conjecture Interaction Optimization Model for Intelligent Transportation Systems in Smart Cities Using Reciprocated Multi-Instance Learning for Road Traffic Management |
| title_fullStr | Conjecture Interaction Optimization Model for Intelligent Transportation Systems in Smart Cities Using Reciprocated Multi-Instance Learning for Road Traffic Management |
| title_full_unstemmed | Conjecture Interaction Optimization Model for Intelligent Transportation Systems in Smart Cities Using Reciprocated Multi-Instance Learning for Road Traffic Management |
| title_short | Conjecture Interaction Optimization Model for Intelligent Transportation Systems in Smart Cities Using Reciprocated Multi-Instance Learning for Road Traffic Management |
| title_sort | conjecture interaction optimization model for intelligent transportation systems in smart cities using reciprocated multi instance learning for road traffic management |
| topic | ITS MIL reciprocated convergence routing optimization smart city |
| url | https://ieeexplore.ieee.org/document/10891544/ |
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