Spatiotemporal modeling and prediction of road network traffic flow are essential components of intelligent transport systems (ITS), aimed at effectively enhancing road service levels. Sustainable and reliable traffic management in smart cities requires the use of modern algorithms based on a comprehensive analysis of a significant number of dynamically changing factors. This paper designs a Crested Porcupine Optimizer (CPO)-CNN-LSTM-Attention time series prediction model, which integrates machine learning and deep learning to improve the efficiency of traffic flow forecasting in the condition of urban roads. Based on historical traffic patterns observed on Paris’s roads, a traffic flow prediction model was formulated and subsequently verified for effectiveness. The CPO algorithm combined with multiple neural network models performed well in predicting traffic flow, surpassing other models with a root-mean-square error (RMSE) of 17.35–19.83, a mean absolute error (MAE) of 13.98–14.04, and a mean absolute percentage error (MAPE) of 5.97–6.62%. Therefore, the model proposed in this paper can predict traffic flow more accurately, providing a solution for enhancing urban traffic management in intelligent transportation systems, and thus offering a research direction for the future development of smart city construction.

To improve the management and operational efficiency of Intelligent Transportation Systems (ITS), address the nonlinear complexity of short‐term traffic flow, mitigate the issue of significant noise in traffic flow datasets, and tackle the challenges in determining parameters for Support Vector Machines (SVM) and Long Short‐Term Memory (LSTM) networks, this paper proposes a short‐term traffic flow prediction model based on Variational Mode Decomposition (VMD) and Least Squares Support Vector Machine (LSSVM) integrated with an attention mechanism. Multiple intrinsic mode functions (IMFs) decomposed by VMD are input into the LSSVM model, and the parameters and weights of the model are automatically adjusted using the attention mechanism. Experimental results on the Italian highway traffic flow dataset show that the prediction accuracy of the VMD‐LSSVM‐Attention model is improved by an average of about 38.6% compared with the traditional VMD‐SVM, VMD‐LSTM‐Attention, VMD‐LSSVM and LSSVM‐Attention models, and the model is more stable. Furthermore, in generalisation validation experiments on the Rotterdam and Madrid datasets, the model improved prediction accuracy by 5.17% to 20.97% compared to the best‐performing advanced models. This model provides a prediction method for the traffic flow prediction module in the intelligent transportation system (ITS) architecture.

This paper compares the effectiveness of support vector machine (SVM), convolutional neural network-long short-term memory (CNN-LSTM), and support vector machine-long short-term memory (SVM-LSTM) models for traffic flow prediction in intelligent transportation systems (ITS). This research aims to explore the application scenarios of different machine learning and neural network models in traffic flow forecasting, focusing on verifying the effectiveness of the CNN-LSTM and the SVM-LSTM models designed in this paper in integrating spatial feature extraction with time series modeling. Experimental validation is conducted on real-world long- and short-term traffic flow datasets, and the performance of each model is systematically evaluated in terms of the number of prediction errors, computational efficiency, and robustness. Through a comprehensive analysis of metrics such as the coefficient of determination (R2) and root mean square error (RMSE), thisresearch provides a basis for the appropriate selection of prediction models in ITS and offers theoretical support for future research in multimodal traffic data fusion modeling. Purpose. Through systematic comparative studies, a more efficient and reliable model is screened out for the traffic flow prediction subsystem in ITS, and the effectiveness of the hybrid model in integrating multi-dimensional features is explored, thus providing an empirical basis for further optimization of model accuracy in the future. Materials and methods. This research used a long-term traffic flow dataset from France and a short-term traffic flow dataset from Italy. Prediction experiments were conducted in the MATLAB environment using support vector machines (SVMs), CNN-LSTM models, and an SVM-LSTM model with a loss function. The method for determining model effectiveness is based on linear regression theory, focusing on calculating the number of error data and evaluating the data fit using metrics. The method for determining model effectiveness is based on linear regression theory, focusing on calculating the number of error data and evaluating the data fit using metrics. Results. Experimental results based on real-world traffic flow datasets show that the SVM-LSTM model exhibits the best overall performance in long-term traffic flow prediction. The CNN-LSTM model demonstrates excellent time series modeling capabilities in short-term traffic flow prediction. In terms of computational efficiency, the SVM-LSTM model improves prediction accuracy by 10.2% compared to the CNN-LSTM model. Therefore, the fusion of SVM and LSTM combines the advantages of spatial feature extraction and time series modeling, and its deployment in ITS can improve traffic flow prediction efficiency.

The increasing deployment of autonomous vehicles in port logistics requires safety assessment methods that remain valid in mixed traffic environments. This study evaluates the safety of mixed automated guided vehicle (AGV) and human-driven vehicle (HDV) traffic in a seaport terminal connected to an external urban road network. A microscopic traffic model was developed in AIMSUN Next to represent gate areas, internal roads, storage-yard access, berth interfaces, and external container-truck traffic. HDVs were modeled using a Gipps-based car-following model, whereas AGVs were represented through an Adaptive Cruise Control framework. Vehicle trajectories were exported to the Surrogate Safety Assessment Model (SSAM), where Time-to-Collision (TTC) and Post-Encroachment Time (PET) were used to detect and classify conflicts. Six staged fleet-composition scenarios were evaluated in 36 simulation runs, ranging from fully human-driven operation to full automation. Total conflicts decreased from 89 in the fully human-driven scenario to 43 in the fully automated scenario (−51.7%), while rear-end conflicts decreased from 70 to 30 (−57.1%). Crossing conflicts remained relatively stable across scenarios. At the same time, mean TTC decreased from 0.80 to 0.24 s and mean PET from 1.57 to 0.38 s, indicating tighter but more coordinated interactions under automated control. These results show that automation improves longitudinal safety performance in port traffic, but also that conventional TTC and PET thresholds calibrated for human-driven traffic may not be directly applicable to automated port operations. Automation-sensitive surrogate safety criteria are therefore needed for seaport mixed-traffic evaluation.