Trunked radio systems: traffic prediction based on user clusters

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

Exploratory Study on Approaches for Traffic count Prediction; Using Toll-Way Traffic Count

With mining traffic patterns, we evaluate the performance impact on wireless mesh networks. Genuine traffic traces are collected from the wireless mesh networks testbed, which tends to exhibit long-range dependent behavior under several Hurst index estimators. We analyze traffic traces and use clustering techniques to characterize patterns of individual users' behavior. After extracting traffic data from the raw data logs, we identify session clusters by employing the AutoClass tool and the K-means algorithm. Modeling and simulation were performed using the NS-2 tool. Based on the identified session clusters, we introduce source model based on wavelet. Simulation results indicate that traffic traces, compared to traditional traffic models, predict longer queues and, thus, require larger buffers in the network dimensioning.

The design of the Motorola Trunked Radio System in use in the 800 MHz band in the United States is reviewed. The performance of the Message, Transmission, and Quasi-Transmission trunked channel assignment strategies is compared. Based on studies of trunked systems in operation in the US, Transmission and Ouasi-Transmission trunking are shown to be more efficient than Message trunking. The signaling channel of the Trunked System is used to receive service requests from mobile and portable units and transmit back channel assignments. If all voice channels are busy, requests are queued. Capacity requirements for the signalling channel used in Transmission Trunked-based systems are shown to be higher than those for Message Trunked Systems.

Traffic prediction is of significant importance for telecommunication network planning and network optimization. In this paper, the traffic series from a certain mobile network in Heilongjiang province in China is studied. The characteristics in respect of both trend and periodicity are explored with autocorrelation function. Based on the characteristics exhibited in the traffic series, multiplicative seasonal autoregressive integrated moving average model (ARIMA) is employed to make traffic series prediction. Average daily traffic per month for the province as well as its every sub-region from July to December in 2009 is forecasted and compared with the actual operation data. The mean absolute percentage error (MAPE) for one-step ahead prediction is 1.382%, and MAPE for the 6 steps is within 6%. The prediction result is of high precision. Furthermore, the cause for the big prediction error in 2 regions is analyzed, and the appropriateness of the model is testified on the opposite aspect. This paper also provides an effective method by using correlation coefficients to analyze the cause for significant prediction errors which do not happen in time series prediction applications rarely.

Accurate traffic prediction is crucial to alleviating traffic congestion in cities. Existing physical sensor-based traffic data acquisition methods have high transmission costs, serious traffic information redundancy, and large calculation volumes for spatiotemporal data processing, thus making it difficult to ensure accuracy and real-time traffic prediction. With the increasing resolution of UAV imagery, the use of unmanned aerial vehicles (UAV) imagery to obtain traffic information has become a hot spot. Still, analyzing and predicting traffic status after extracting traffic information is neglected. We develop a framework for traffic speed extraction and prediction based on UAV imagery processing, which consists of two parts: a traffic information extraction module based on UAV imagery recognition and a traffic speed prediction module based on deep learning. First, we use deep learning methods to automate the extraction of road information, implement vehicle recognition using convolutional neural networks and calculate the average speed of road sections based on panchromatic and multispectral image matching to construct a traffic prediction dataset. Then, we propose an attention-enhanced traffic speed prediction module that considers the spatiotemporal characteristics of traffic data and increases the weights of key roads by extracting important fine-grained spatiotemporal features twice to improve the prediction accuracy of the target roads. Finally, we validate the effectiveness of the proposed method on real data. Compared with the baseline algorithm, our algorithm achieves the best prediction performance regarding accuracy and stability.

Long-term insight into maritime traffic is critical for port authorities, logistics companies, and port operators to proactively formulate suitable policies, develop strategic plans, allocate budget, and preserve and improve competitiveness. Forecasting freight rate is a spotlight in port traffic literature, but relatively little research has been directed at forecasting long-term vessel traffic trends. Based on forecast long-term freight rate input provided by the recent 10-year strategic planning of the port of Rajaee, the largest port of Iran, the paper implements seasonal autoregressive integrated moving average (SARIMA) and neural network (NN) models to forecast its container vessel traffic between 2020 and 2025. A database consisting of monthly container traffic data for this port from 1999 to 2019 is utilized. The comparison between the two forecasting models is fulfilled by benchmarking the naïve method. The results reveal the superiority of the NN model over SARIMA in this practice. Considering NN model outputs, the port should expect a significant increase in Panamax and Over-Panamax vessels in the future, and, if not timely addressed, this would result in a systemic queue in the port of Rajaee. That said, the approach can be implemented in port planning and design to avoid under- or over-estimations in such capital-intensive projects.

Dynamic multi-scale spatial–temporal graph convolutional network for traffic flow prediction

Traffic prediction plays a crucial role in smart cities. As concerns over data privacy grow, direct data sharing is increasingly restricted, prompting substantial interest in Federated Graph Learning for traffic prediction. Nonetheless, most existing methods follow batch learning paradigms, which are unsuitable for dynamic, streaming traffic data. In practical applications, traffic data arrives continuously, calling for online learning techniques. Moreover, current methods often rely on split learning to capture spatio-temporal dependencies, which increases communication overhead and latency, limiting their real-time applicability. To this end, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u>wift <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</u>nline <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fed</u>erated Graph Learning Framework for Traffic Streaming Data Prediction (<monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sofed</monospace> for short). We introduce an early training strategy to equip clients with the up-to-date local temporal models and dynamic spatial features, enabling rapid adaptation to traffic fluctuations while minimizing client response time. Additionally, we pre-train a masked data generation module to synthesize multi-step data, effectively decoupling the server from real-time ground-truth dependency and enhancing model generalization. Lastly, the client model is optimized for lightweight deployment by offloading specific components to the server. Extensive experiments on real-world datasets demonstrate that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sofed</monospace> achieves state-of-the-art performance with significantly reduced client response time and communication cost.

The intelligent transportation system seeks to reduce traffic and improve the driving experience. They give us a lot of data that we can use to improve services for both the public and transportation officials by feeding it into machine learning systems. Most importantly, Traffic environment refers to everything that might have an impact on how much traffic is moving down the road, including traffic signals, accidents, protests, and even road repairs that might result in a backup. A motorist or rider can make an informed choice if they have previous knowledge that is very close to approximate all the above and many more real-world circumstances that can affect traffic. Additionally, it aids in the development of driverless vehicles. Traffic data have been growing dramatically in recent decades, and we are moving toward big data concepts for transportation. The current approaches for predicting traffic flow use some traffic prediction models, however they are still inadequate to handle practical situations. We thus aimed to focus on the traffic flow forecast problem using the traffic data and prediction models. The proposed model called DRGNN, a dilated recurrent graph neural network framework aims to effectively analyze and predict the traffic pattern by considering the spatial (space) and temporal (time) aspects of the real-time traffic data considering social relationships between internet of vehicles which indeed produced accurate and valuable insights that could help in deploying the model in any suitable real-time traffic monitoring and prediction system.

From the past to the future: Modeling the temporal instability of safety performance functions

Fast and accurate traffic load prediction is a pivotal component of the Intelligent Transport System. It will reduce time spent by commuters and save our environment from vehicle emissions. During the COVID-19 pandemic, people prefer to use private transportation; thus predicting the traffic load becomes more critical. In these years, researchers have developed some traffic load prediction models and have applied these models successfully on data from the US, China or Europe. However, none of these models has been applied to traffic data in Australia. Considering that Australia bears different political, geographical, and climate conditions from other countries, these models may not be suitable to predict the traffic load in Australia. In this paper, we investigate this problem and proposes a multi-modal method that is capable of using Australia-specific data to assist traffic load prediction. Specifically, we use daily social media data together with traffic data to predict the traffic load. We illustrate a protocol to pre-process raw traffic and social media data and then propose a multi-modal model, namely DM2T, which accurately make time-series prediction by using both time-series data and other media data. We validate the effectiveness of our proposed method by a case study on Brisbane city. The result shows that with the help of Australia-specific social media data, our proposed method can make more accurate traffic load prediction for Brisbane than conventional methods.KeywordsTraffic load predictionDeep learningNatural Language Processing (NLP)

In smart cities, AI‐driven traffic flow prediction and anomaly detection play a crucial role in optimizing urban mobility and reducing congestion. Traditional traffic management systems (TMS) often struggle with real‐time adaptability, leading to inefficiencies in traffic prediction and failure to detect anomalies accurately. To address these limitations, we propose the Traffic Flow Prediction using Multi‐Agent Approach (TFP‐MAA) framework, which leverages multiple intelligent agents to analyze real‐time traffic data, predict congestion patterns, and detect anomalies with high precision. The framework integrates deep learning models with a decentralized multi‐agent system to enhance prediction accuracy and response efficiency. The proposed method enables adaptive decision‐making for traffic authorities by providing real‐time congestion forecasts and identifying unusual traffic patterns, such as accidents or roadblocks. This enhances proactive traffic management and reduces delays. Experimental results demonstrate that TFP‐MAA significantly outperforms existing models in terms of accuracy (94.7%), response time (96.2%), and anomaly detection efficiency (95.9%). The findings suggest that integrating multi‐agent AI systems into smart city infrastructure can lead to improved traffic flow (97.5%), reduced congestion (28.6%), and enhanced road safety.

For providing drivers with robust traffic information and Optimizing the energy management of Hybrid Electric Vehicles (HEVs), it is important to predict traffic information accurately with past traffic information. As acquisition of the traffic information have been easier by the development of Intelligent Transportation System (ITS), active study on traffic prediction is currently underway. Multi-Layer Perceptron (MLP) model have been widely utilized for predicting traffic information since it is appropriate to represent the non-linear characteristics inherent in traffic prediction. However, the MLP model doesn't reflect local dependencies of traffic data and is prone to noise in traffic data. Convolutional Neural Networks (CNN) based model, on the other hand, can capture the local dependencies of traffic data and is less prone to disturbance in data. In this paper, we use temporal data and speed data collected on main roads in Seoul, South Korea to construct traffic prediction models. The speed data which are collected by every 5 minutes are provided by Ministry of Land, Infrastructure and Transport in South Korea. We construct the CNN based model and two MLP models which predict traffic speed and compare performance of the prediction models in this paper. The comparison results show that the CNN based model's prediction performance is higher than the prediction performance of the other two MLP models.

As a cornerstone of intelligent transportation systems, traffic flow prediction has garnered extensive research attention. However, traffic flow data exhibits pronounced spatio-temporal dynamics, rendering accurate traffic flow prediction a challenging endeavor. Existing methodologies compartmentalize the capture of temporal and spatial dependencies through distinct modules, overlooking latent spatio-temporal heterogeneity within traffic data. Furthermore, they often solely consider spatio-temporal correlations between adjacent historical time steps, neglecting the extraction of temporal dependencies across different time intervals. To address these issues, we propose a novel Multi-scale Fusion Dynamic Graph Neural Network (MFDGNN). MFDGNN employs Temporal Convolutional Networks (TCNs) to generate time series on multiple scales, subsequently extracting spatio-temporal features from these sequences to construct mutually independent fusion graphs. By leveraging fusion graphs to extract features from time series on multiple scales, our model adeptly captures spatio-temporal dependencies of traffic nodes across various time dimensions, thereby achieving enhanced precision in traffic flow prediction. Experimental results conducted on four publicly available datasets demonstrate the superiority of our model compared to other baseline approaches.