Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.
As for the Internet is grown this much and the massive data of social networks accounts have been accumulated about users' behavior on digital platforms, it is found that this data require to be derived on efficient way to increase the personalization for improving the internet user experience. This paper introduces an ontology-based architecture that extracts user's data from multiple sources such as websites, applications and social networks accounts. The data is then preprocessed and used for developing the domain ontology, which facilitates the structure of user concepts and allows for efficient data integration and intelligent reasoning. The proposed architecture makes the domain ontology applicable in personalized digital marketing, digital cloning, AI (Artificial Intelligence) based virtual assistants and other user-driven applications. In addition, it takes privacy and ethical concerns into account, where user consent mechanisms, data anonymization techniques and compliance with regulations like the General Data Protection Regulation (GDPR) can be utilized. By incorporating such considerations, the architecture ensures responsible use of the collected data for maximum contribution to the digital services, and prevents any form of exploitation against internet users.
The characteristics of social network sensitive data are complex, which leads to the difficulty of detecting social network sensitive data, so to study the sensitive data detection method of social network based on improved Random Forest (RF) algorithm. Simulate login to social network, and capture social network information by means of web crawler and collector. The Topology-Based Hierarchical Trait (TBHT) topology feature logic algorithm optimized by Naive Bayesian (NB) algorithm is used to extract sensitive data features of social networks from social network information. The RF algorithm is improved by adaptive node splitting, and a sensitive data detection model based on the improved RF algorithm is built by combining the characteristics of social network sensitive data. Social network information is input into the model, and relevant detection results are obtained. The experimental results show that the data acquisition mode using web crawler and collector runs stably and has a large amount of data acquisition, and the extracted data features are efficient. The accuracy of the improved RF algorithm in data classification is more than 97.5%. Therefore, this method is a powerful and practical method for detecting sensitive data of social networks.
The Internet has provided great convenience for the development of the education industry, but it has also brought about problems such as difficulties in selecting educational resources, difficulty in searching information, etc. To address these issues, the research constructs an educational resource Recommendation System (RS) grounded on Mahout Collaborative Filtering (CF) hybrid algorithm to provide efficient resource recommendations for users. During the construction of the system, the research also combines Multi-Dimensional Feature Fusion (MDFF) and deep learning personalized course recommendation methods to optimize courses and enhance the system's ability to integrate multiple data. The experiment outcomes indicate that the hybrid algorithm has higher recommendation accuracy compared to CF algorithm, Fuzzy C-Means (FCM) algorithm, and the combination ofknowledge graph completion andRS algorithm. The average recommendation accuracy of the four algorithms is 88.88%, 79.11%, 71.11%, and 65.53%, respectively. In addition, empirical analysis of the constructed educational resource RS reveals that the proposed hybrid algorithm has a lower Receiver Operating Characteristic (ROC) curve area value of 0.8984 and an F1-value of 0.8298, indicating good recommendation performance and superior performance compared to other comparative educational resource RSs. The above information indicates that the educational resource RS grounded on Mahout CF hybrid algorithm has certain stability and can offer customized suggestions for resources tailored to individual users in a timely manner. This research provides a practical method for online education on the Internet, which will help further improve online education in the education platform in the future.
Long-term exposure to crystalline silica dust causes silicosis, an irreversible occupational lung disease that is currently a major global health concern because of its delayed diagnosis and few available treatments. In this work, a new segmentation-driven hybrid framework for automated silicosis staging and detection from chest radiographs, called SilicoNet (Silicosis+Network), is proposed. Two separate experiments were conducted to validate the framework. In the first experiment, SilicoNet was used to segment lungs from pre-processed and augmented chest radiographs, and its performance was compared to that of the conventional U-Net model. In the second experiment, the outputs of the proposed custom Convolutional Neural Network (CNN) model were systematically compared with those of three popular CNN architectures used for classification: MobileNetV2, InceptionV2, and ResNet50. For validation, evaluation criteria such as the Dice similarity coefficient, Jaccard index, precision, recall, f1-score, accuracy, specificity, matthews correlation coefficient, negative predictive value, and training length were used. The findings show that SilicoNet and the customised CNN perform better than standard baselines, achieving a maximum accuracy of 96.40%. This study is distinctive because it combines improved segmentation and optimised classification, which results in better robustness and generalisability than previous models.
