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This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns’ dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods.

Maritime traffic pattern recognition plays a major role in intelligent transportation services, ship monitoring, route planning, and other fields. Facilitated by the establishment of terrestrial networks and satellite constellations of the automatic identification system (AIS), large quantities of spatial and temporal information make ships’ paths trackable and are useful in maritime traffic pattern research. The maritime traffic pattern may vary with changes in the traffic environment, so the recognition method of the maritime traffic pattern should be adaptable to changes in the traffic environment. To achieve this goal, a dynamic maritime traffic pattern recognition method is presented using AIS data, which are cleaned, compressed, partitioned, and clustered online. Old patterns are removed as expired trajectories are deleted, and new patterns are created as new trajectories are added. This method is suitable for processing massive stream data. Experiments show that when the marine traffic route changes due to the navigation environment, the maritime traffic pattern adjusts automatically.

Urban traffic pattern reflects how people move and how goods are transported, which is crucial for traffic management and urban planning. With the development of sensing techniques, accumulated sensor data are captured for monitoring vehicles, which also present the opportunities of big transportation data, especially for real-time interactive traffic pattern analysis. We propose a three-layer framework for the recognition and visualization of multiscale traffic patterns. The first layer computes the middle-tier synopses at fine spatial and temporal scales, which are indexed and stored in a geodatabase. The second layer uses synopses to efficiently extract multiscale traffic patterns. The third layer supports real-time interactive visual analytics for intuitive explorations by end users. An experiment in Shenzhen on taxi GPS trajectories that were collected over one month was conducted. Multiple traffic patterns are recognized and visualized in real-time. The results show the satisfactory performance of proposed framework in traffic analysis, which will facilitate traffic management and operation.

IoT-based applications require effective anonymous authentication and key agreement (AKA) protocols to secure data and protect user privacy due to open communication channels and sensitive data. While AKA protocols for these applications have been extensively studied, achieving anonymity remains a challenge. AKA schemes using one-time pseudonyms face resynchronization issues after desynchronization attacks, and the high computational overhead of bilinear pairing and public key encryption limits its applicability. Existing schemes also lack essential security features, causing issues such as vulnerability to ephemeral secret leakage attacks and key compromise impersonation. To address these issues, we propose two novel AKA schemes, PUAKA and RCAKA, designed for different IoT traffic patterns. PUAKA improves end device anonymity in the periodic update pattern by updating one-time pseudonyms with authenticated session keys. RCAKA, for the remote control pattern, ensures anonymity while reducing communication and computation costs using shared signatures and temporary random numbers. A key contribution of RCAKA is its ability to resynchronize end devices with incomplete data in the periodic update pattern, supporting continued authentication. Both protocols’ security is proven under the Real-or-Random model. The performance comparison results show that the proposed protocols exceed existing solutions in security features and communication costs while reducing computational overhead by 32% to 50%.

Recognition of ship traffic patterns can provide insights into the rules of navigation, maneuvering, and collision avoidance for ships at sea. This is essential for ensuring safe navigation at sea and improving navigational efficiency. With the popularization of the Automatic Identification System (AIS), numerous studies utilized ship trajectories to identify maritime traffic patterns. However, the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior, such as ship static and maritime geospatial features, resulting in insufficient precision in ship traffic pattern recognition. This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity (STPMTS), which considers ship static feature, dynamic feature, port geospatial feature, as well as semantic relationships between these features. First, A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements. Subsequently, to quantify the degree of similarity of ship trajectories, a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features. Furthermore, trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters. The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns. Finally, the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China, ranging from September to November 2016. Compared with other methods, the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features, dynamic features, and adherence to navigation rules. This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.

The problem of detecting anomalies in network traffic caused by the distributed denial of service (DDoS) attack so far has mainly been investigated in terms of detection of illegitimate DDoS traffic generated by conventional terminal devices (PCs, laptops, mobile devices, tablets, servers). Technological development has resulted in the emergence of the Internet of Things (IoT) concept, whose implementation implies numerous terminal devices with a low level of implemented protection. The large growth and prediction of future growth is noticeable in the environment of a smart home and smart office. IoT devices in such environments are increasingly being used as a platform for generating DDoS traffic due to its numeracy and low level of protection. The aim of this research is to propose a novel approach for detection of DDoS traffic generated by IoT devices in a form of conceptual network anomaly detection model. Proposed conceptual model is based on device classes which are dependent on individual device traffic characteristics.

In recent years, the advancement of sensor technology has led to the generation of heterogeneous Internet-of-Things (IoT) data by smart cities. Thus, the development and deployment of various aspects of IoT-based applications are necessary to mine the potential value of data to the benefit of people and their lives. However, the variety, volume, heterogeneity, and real-time nature of data obtained from smart cities pose considerable challenges. In this paper, we propose a semantic framework that integrates the IoT with machine learning for smart cities. The proposed framework retrieves and models urban data for certain kinds of IoT applications based on semantic and machine-learning technologies. Moreover, we propose two case studies: pollution detection from vehicles and traffic pattern detection. The experimental results show that our system is scalable and capable of accommodating a large number of urban regions with different types of IoT applications.

Delay is an important parameter in optical packet switching networks and it affects the performance of the network. In this communication, a mathematical analysis is carried out to evaluate the delay. Delay values are analysed for variable length packets for various traffic patterns, viz. non-uniform, Poisson and ON–OFF traffic patterns for various service classes using reservation bit algorithm. The results of the class-based models are compared with the existing port-based first-fit wavelength assignment algorithm. Delay values are reduced by 29% in the class-based model than in the port-based model. Furthermore, packet transmission rate in class-based model is higher by 15.4% than port-based model.

Traffic congestion caused by left-turning vehicles in a coordinated corridor is a multifaceted problem requiring tailored solutions. This study explores the impact of shared left-turn lanes within one-way couplets, particularly during peak hours, where high left-turn volumes, limited side street storage, and the overlapped green time between pedestrians and left-turners contribute to queue spillbacks, coordination interruption, and network congestion. The focus of this paper is on the solutions that can be easily analyzed by practitioners, here called “analyzable solutions”. This approach stands in contrast to solutions derived from “non-transparent” optimization tools, which do not allow for a clear assessment of the solution’s adequacy or the ability to predict its impact in real-world applications. This paper investigates the effects of employing two analyzable signal timing strategies: Lagging Pedestrian (LagPed) phasing and Left-Turn Progression (LTP) offsets. Using high-fidelity microsimulation, the authors evaluated different scenarios, assessing pedestrian delays, queue lengths, travel time index, area average travel time index, and environmental impacts such as Fuel Consumption (FC) and CO2 emissions. The effectiveness of the proposed strategies was comprehensively evaluated against the base case scenario, demonstrating considerable improvements in various performance measures, including approximately a 5% reduction in FC and CO2 emissions. Implementation of the LTP strategy alone yields substantial reductions in delays, the number of stops, the queue length for left-turning vehicles, travel times for all road users, and ultimately FC and CO2 emissions. This study offers innovative approach to addressing the complex and multifaceted problem of left-turn-centered congestion in urban grid networks using efficient and down-to-earth analyzable solutions.

A representative urban driving cycle was developed for Quito, Ecuador, using the K-Means clustering method. From 64 samples and 188,713 geospatial and speed data points, a 2870 s driving cycle was constructed to capture real-world traffic characteristics. Key parameters include an average speed of 22.68 km/h, acceleration and deceleration rates of 0.55 m/s2 and −0.57 m/s2, and a dwell time of 9.66%. Due to Quito’s linear urban development, where mobility is limited to north–south/south–north corridors, the driving cycle reflects frequent accelerations and decelerations along congested arterial roads. A comparative analysis with international driving cycles revealed that Quito’s traffic follows a unique pattern shaped by its geographic constraints. The HK cycle in China showed the greatest similarities, although differences in instantaneous speeds highlight the need for localized models. While this study primarily focuses on methodological robustness, the developed driving cycle provides a foundational dataset for future research on traffic flow optimization, emissions estimation, and sustainable urban mobility strategies. These insights contribute to data-driven decision-making for improving transportation efficiency and environmental impact assessment in cities with similar urban structures.