The advancement of intelligent road transport represents a promising direction in the evolution of transportation systems, aimed at improving road safety and reducing traffic accidents. The integration of artificial intelligence, sensors, and machine vision systems enables autonomous vehicles (AVs) to rapidly adapt to changes in the road environment, minimizing human error and significantly reducing collision risks. These technologies provide continuous and highly precise control, including adaptive acceleration, braking, and maneuvering, thereby enhancing overall road safety. Connected vehicles utilizing C-V2X (Cellular Vehicle-to-Everything) communication primarily feature real-time operation, safety, and stability. However, communication flaws, such as signal fading, time delays, packet loss, and malicious network attacks, can affect vehicle-to-vehicle interactions in cooperative intelligent transport systems (C-ITSs). This study explores how C-V2X technology, compared to traditional DSRC, improves communication latency and enhances vehicle communication efficiency. Using SUMO simulations, various traffic scenarios were modeled with different autonomous vehicle penetration rates and communication technologies, focusing on traffic conflict rates, travel time, and communication performance. The results demonstrated that C-V2X reduced latency by over 99% compared to DSRC, facilitating faster communication between vehicles and contributing to a 38% reduction in traffic conflicts at 60% AV penetration. Traffic flow and safety improved with increased AV penetration, particularly in congested conditions. While C-V2X offers substantial benefits, challenges such as data packet loss, communication delays, and security vulnerabilities must be addressed to fully realize its potential. Future advancements in 5G and subsequent wireless communication technologies are expected to further reduce latency and enhance the effectiveness of C-ITSs. This study underscores the potential of C-V2X to enhance collision avoidance, alleviate congestion, and improve traffic management, while also contributing to the development of more reliable and efficient transportation systems. The continued refinement of simulation models and collaboration among stakeholders will be crucial to addressing the challenges in CAV integration and realizing the full benefits of connected transportation systems in smart cities.

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.

Rapid urbanization has led to the development of intelligent transport in China. As active safety technology evolves, the integration of autonomous active safety systems is receiving increasing attention to enable the transition from functional to all-weather intelligent driving. In this process of transformation, the goal of automobile development becomes clear: autonomous vehicles. According to the Report on Development Forecast and Strategic Investment Planning Analysis of China’s autonomous vehicle industry, at present, the development scale of China’s intelligent autonomous vehicles has exceeded market expectations. Considering limited research on utilizing autonomous vehicles to meet the needs of urban transportation (transporting passengers), this study investigates how autonomous vehicles affect traffic demand in specific areas, using traffic modeling. It examines how different penetration rates of autonomous vehicles in various scenarios impact the efficiency of road networks with constant traffic demand. In addition, this study also predicts future changes in commuter traffic demand in selected regions using a constructed NL model. The results aim to simulate the delivery of autonomous vehicles to meet the transportation needs of the region.

The paper presents the results of comparative studies of the selection of micromodels of traffic simulation on a regulated network. The accuracy of the calculated values of vehicle speed obtained from the models of catastrophe theory is shown. Based on model studies it was found that, depending on frequency of buses the optimal distance to the stop line changes, from which it is efficient to make decision on priority intersection. The paper estimates the probability of traffic light options with different volume of priority vehicles.

Introduction. With highway congestion increasing, the efficiency of intelligent transportation systems depends on highquality short-term traffic prediction. Statistical methods do not adequately account for nonlinear and dynamic traffic changes. Long short-term memory (LSTM) and support vector machines (SVR) offer more promising solutions. However, they are not ranked in terms of accuracy, as there are no studies comprehensively comparing their adequacy for shortterm traffic flow prediction. The proposed study fills this gap. The research objective is to compare the accuracy of LSTM and SVR, and select the optimal approach for traffic flow prediction on Shenzhen Meiguang Expressway. Materials and Methods . Traffic detector data was collected on the Meiguan Expressway in June 2021. Data preprocessing methods were used, including weighted mean imputation and normalization. Autocorrelation analysis was used for feature extraction, along with the creation of an interaction variable between speed and detector occupancy. Models were trained and tested on data collected from detectors at 5-minute intervals. Results. LSTM performed 17.86% better in terms of root mean square error, 19.82% better in terms of mean absolute error, and 25.78% better in terms of mean absolute percentage error. In periods with the lowest flow rate prediction error, RMSE, MAE, and MAPE for the LSTM model were 36.5%, 34.3%, and 42.3% lower, respectively. In periods with the highest error, RMSE, MAE, and MAPE for the LSTM model were 73.2%, 65.4%, and 64.4% lower, respectively. The Wilcoxon signed-rank test <0.05 confirmed the statistical significance of the differences. Discussion. The superior predictive performance of LSTM stems from its architecture, namely, the combination of interaction variables and lag metrics. LSTM accounts better for flow time dependences, adapts to complex, long-term dynamic changes, and remains accurate even with significant fluctuations. The lower predictive performance of SVR stems from its weak, nonlinear approximation ability. Sudden flow changes increase significantly error rates. Conclusion. When choosing between a neural network and a machine learning model for short-term traffic flow prediction on an expressway, the neural network model, such as LSTM, should be preferred. These research results can be useful in predictive strategies for reducing congestion. Short-term prediction based on LSTM can serve as a basis for optimizing traffic management, reducing congestion and pollutant emissions, and for optimizing intelligent transportation systems. A promising direction is the development of hybrid architectures that integrate contextual data (weather, infrastructure, accidents) to improve real-time predictions.