To address the challenges of characterizing subway passenger flow fluctuations and overcoming the slow convergence and significant errors of existing intelligent optimization algorithms in tuning deep learning parameters for flow prediction, this study proposes a novel subway passenger flow fluctuation interval prediction model based on a Symmetry-Enhanced FIG-ICPO-XGBoost model. The core innovation is an Improved Cheetah Optimization Algorithm (ICPO), which incorporates enhancements including Circle mapping for population initialization, a hybrid strategy of dimension-by-dimension pinhole imaging opposition-based learning and Cauchy mutation to escape local optima, and adaptive variable spiral search with inertia weight to balance exploration and exploitation. The construction of this methodology embodies the concept of symmetry in algorithm design. For instance, Circle mapping achieves uniformity and ergodicity in the initial distribution of the population within the solution space, reflecting the symmetric principle of spatial coverage. Dimension-by-dimension pinhole imaging opposition-based learning generates opposite solutions through the principle of mirror symmetry, effectively expanding the search space. The adaptive variable spiral search strategy dynamically adjusts the spiral shape, simulating the symmetric relationship of dynamic balance between exploration and exploitation. Utilizing fuzzy-granulated passenger flow data (LOW, R, UP) from Harbin, the ICPO was employed to optimize XGBoost hyperparameters. Experimental results demonstrate the superior performance of the ICPO on 12 benchmark functions. The ICPO-XGBoost model achieves mean MAE, RMSE, and MAPE values of 10,291, 10,612, and 5.8%, respectively, for the predictions of the LOW, R, and UP datasets. Compared to existing models such as CPO-XGBoost, PSO-BiLSTM, GA-BP, and CNN-LSTM, these values represent improvements ranging from 4541 to 13,161 for MAE, 5258 to 14,613 for RMSE, and 2.6% to 7.2% for MAPE. The proposed model provides a reliable theoretical and data-driven foundation for optimizing subway train schedules and station passenger flow management.

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning.

This paper focuses on enhancing the accuracy and efficiency of bus travel speed prediction by improving the optimization process for deep learning model parameters. Existing intelligent optimization algorithms often suffer from slow convergence and substantial errors when tuning parameters for such predictive tasks. To mitigate these shortcomings, this study presents a new predictive framework that synergizes an Improved Atom Search Optimization (IASO) algorithm with a Bidirectional Gated Recurrent Unit (BiGRU) network. The EASO algorithm is developed through three principal modifications: (1) population initialization using a Logistic-Tent composite chaotic map to enhance diversity and initial quality; (2) incorporation of a hybrid operator merging refraction opposition-based learning and Cauchy mutation to broaden the search around promising solutions and alleviate issues of local stagnation and early convergence; and (3) implementation of an adaptive variable spiral search to recalibrate the position update rule, thereby improving the trade-off between extensive exploration and intensive exploitation. Based on the analysis of bus travel speed determinants, the IASO algorithm is applied to optimize the hyperparameters of the BiGRU network, culminating in the proposed IASO-BiGRU predictive model. Validation tests indicate that the devised IASO algorithm shows improved performance in certain aspects compared to several contemporary intelligent optimization techniques in terms of solution accuracy and convergence efficiency. Under the specific experimental conditions of this study, the IASO-BiGRU model achieves MAE, RMSE, and MAPE values of 1.62, 1.80, and 6.70%, respectively, corresponding to an improvement of 1.91–7.56% compared to the baseline models tested. These findings offer valuable data support and a decision-making foundation for bus operation scheduling and passenger travel planning.

The effective recognition of risky driving behaviors holds technical potential for supporting accident prevention and sustainable transportation. However, existing intelligent algorithms for optimizing deep learning models in this field often suffer from slow convergence and high errors. This study proposes a novel hybrid model (ICPO-XGBoost) for risky driving behavior classification. The improved crested porcupine optimizer (ICPO) was developed using logistic-tent composite mapping for population initialization, a hybrid mechanism combining refraction opposition-based learning and Cauchy mutation to avoid local optima, and an adaptive variable spiral search with inertia weight to balance global and local search. The ICPO was then employed to optimize the hyperparameters of the XGBoost classifier. The ICPO demonstrated superior optimization accuracy and convergence speed compared to benchmark algorithms. The ICPO-XGBoost model achieved accuracy, precision, recall, and F1 scores of 96.2%, 95.4%, 95.8%, and 95.6%, respectively, for classifying and identifying risky driving behaviors. Compared to various benchmark models, these results represent increases of 12.7–24.8%, 14.8–31.8%, 14.9–31.0%, and 15.0–32.4%, respectively. For specific driving behavior categories (normal driving, slow driving, short-distance tailgating, sudden acceleration/deceleration, frequent lane changing, and forced lane changing), the precision, recall, and F1 scores of the ICPO-XGBoost model fell within the ranges of 84.8–99.2%, 87.5–100.0%, and 86.2–99.2%, respectively. Compared to benchmark models, these metrics show increases of 1.5–75.8%, 5.8–68.1%, and 3.3–72.6%, respectively. Notably, the model significantly improved accuracy in identifying sudden acceleration/deceleration behaviors. The results of this model facilitate the classification and early warning of risky driving behaviors, thereby reducing the frequency of such behaviors, lowering the risk of traffic accidents, and enhancing road traffic safety.