Search Results (3,079)

Shared autonomous vehicle (SAV) services are gaining attention as an innovative urban transportation paradigm due to their potential to lower travel costs and improve operational efficiency. Unlike manually operated vehicles, SAVs exhibit unique behavioral dynamics, including safe passenger pick-up and drop-off processes, as well as strategic repositioning and autonomous parking to anticipate future travel demands. Consequently, effective and dynamic route planning is paramount to optimizing SAV safety and operational efficiency. This study proposes a novel traffic information, termed Autonomous Driving Stress (ADS), designed to enhance the safety and efficiency of SAV route planning by quantitatively capturing the level of driving challenge encountered during autonomous operation. To predict ADS, a machine learning framework was developed, utilizing microscopic traffic simulation data that incorporates a comprehensive set of 22 input features describing SAV driving behavior, roadway characteristics, and prevailing traffic conditions. Among five machine learning algorithms evaluated, Random Forest exhibited superior predictive performance, achieving an accuracy of 80.9%. The proposed framework enables real-time ADS level prediction by continuously integrating streaming traffic data into the trained model. The dissemination of this real-time ADS information to SAVs supports proactive, informed, and dynamic route planning decisions, thereby enhancing operational safety, traffic flow, and the sustainability of SAV operations within urban mobility systems. Full article

The increasing number of vehicles in cities reduces the efficiency of parking infrastructure and increases traffic congestion, making it challenging to achieve sustainable transportation goals. This situation necessitates a re-evaluation of urban mobility systems in conjunction with spatial organization and digital technologies. This article examines smart parking systems as “data-oriented spaces”, analyzing their impact on urban mobility, energy efficiency and spatial organization from a multidimensional perspective. The research adopts a qualitative, multi-level approach, structured through a comprehensive literature review, a comparative analysis of five international case studies and a conceptual synthesis of the findings. The data obtained were evaluated using criteria such as technological infrastructure, spatial structure, sustainability performance and user interaction. The findings reveal that smart parking systems not only serve as vehicle storage but can also function as digital–spatial interfaces that direct urban data flows. This study presents a conceptual framework that treats smart parking systems as data-oriented architectural spaces, offering a holistic approach to the design of sustainable urban mobility infrastructures. This perspective allows for redesigning parking structures as adaptable, data-oriented architectural systems that optimize circulation patterns, reduce search-related emissions, increase spatial efficiency and support sustainable urban mobility networks. Full article

Accurate freeway Improvements in traffic state prediction accuracy and enhanced stability enable more proactive traffic control and demand management strategies, thereby reducing congestion spillover effects, unnecessary acceleration–deceleration cycles, and the resulting fuel consumption and emissions. Yet, this remains challenging due to the interplay between deterministic traffic flow mechanisms and stochastic disturbances. Purely data-driven models suffer from error accumulation under out-of-distribution conditions, while physics-based models lack flexibility in capturing nonlinear deviations. This paper proposes MDURP, a physics-constrained residual learning framework that reformulates prediction as a residual-space learning problem. A calibrated Cell Transmission Model generates a physically admissible baseline; deep learning models are then restricted to learning the residuals. Wavelet decomposition and GARCH volatility modeling address the multi-scale and heteroskedastic characteristics of these residuals. Experimental results demonstrate that MDURP consistently outperforms baseline models, reducing MAE by an average of 6.8%, RMSE by an average of 4%. The framework also suppresses long-term error accumulation, with MAPE escalation slowing from 0.79% to 0.58% per step. These gains confirm that anchoring deep learning within a physics-defined residual space enhances both accuracy and stability. Full article

To address the issues of safety risk analysis and conflict assessment for integrated flight of manned aircraft and fixed-wing unmanned aerial vehicles (UAVs) in low-altitude mixed-operation airspace, this study enhances the foundational Event model. By incorporating UAV characteristics such as geometric features and aerodynamic mechanisms, alongside design dimensions and onboard performance metrics, an improved collision risk model is developed—the Enhanced Event-Based Framework for Multidimensional Geometry and Quasi-Monte Carlo Analysis of Flight Performance (EMGF-M). This enhancement rectifies the limitations of the basic model regarding parameter coverage and scenario adaptability, thereby improving the reliability and validity of the computational results. Experimental results demonstrate that, in accordance with the target safety level for airspace conflicts set by the International Civil Aviation Organization (ICAO), the application of the improved Event collision model yields quantifiable assessments of safety risks and safe separation distances for integrated operations in low-altitude mixed-use airspace. Utilizing these computational results for integrated flight procedure design at a general airport in Southwest China, the study shows that the air traffic flow in the low-altitude mixed-operation airspace increased from 9.2 to 20.9 operations per hour. The practical significance of this method lies in its guidance for accurately assessing safety risks in mixed airspace operations and for determining quantifiable separation minima for integrated flight trajectory planning. Full article

Reliable traffic load characterization remains a critical challenge in many African countries due to the lack of continuous field measurements. This study developed an integrated dynamic traffic monitoring and weigh-in-motion system on representative highways in Kenya to obtain long-term, multi-source traffic data. Traffic operations were quantified across hourly, weekly, and monthly scales, including flow variability, vehicle class composition, axle loads, overload behavior, and speed distributions. Results indicate that the spatiotemporal characteristics of traffic volume show pronounced short-term fluctuations but strong long-term stability. Despite their lower proportion, multi-axle heavy trucks dominate structural loading, with overload ratios exceeding 80% and gross weights approaching 100 t. Over 60% of vehicles operate at medium-to-low speeds (20–60 km/h), extending load duration and increasing pavement damage potential. These combined effects indicate that average indicators alone underestimate true loading demand. The proposed framework provides field-based traffic load spectra and a transferable methodology for traffic monitoring and pavement design optimization across developing regions in Africa. Full article

Accurate perception and interpretation of the road environment are essential for safe driving. Vertical traffic signs play a key role in communicating warnings, regulations, and guidance to road users, thereby supporting safe and efficient traffic flow. However, their effectiveness depends not only on proper design and placement but also on how accurately and promptly they are perceived by drivers, which may be influenced by factors such as attention, cognitive workload, physical and mental condition, and fatigue. This study evaluates the contribution of selected vertical traffic signs to driving safety along a designated roadway section in Şanlıurfa, Türkiye. Face-to-face surveys were conducted with 480 active road users. Drivers’ knowledge, compliance behavior, safe route preferences, perceived visibility, and the effects of missing or inadequate signage were analyzed. The results indicate that driving exposure, education level, and experience significantly influence knowledge and perception of traffic signs, while compliance shows limited variation. These findings suggest that knowledge alone does not necessarily translate into behavioral compliance and underscore the importance of considering both driver-related factors and infrastructure characteristics in traffic safety strategies. The study provides practical insights for improving the visibility, placement, and overall effectiveness of vertical traffic signs in rapidly developing urban environments. Full article

Social media platforms have been established as relevant sources of real-time information for urban traffic analysis. This study proposes an intelligent framework for the classification and spatiotemporal analysis of traffic incidents based on semi-synthetic data streams constructed from historical geolocated seeds for controlled validation, utilizing real reports from platforms such as X and Telegram. The approach integrates adaptive machine learning and incremental density-based clustering. An Adaptive Random Forest (ARF) incremental classifier is used to identify the type of incident, allowing for continuous updating of the model in response to changes in traffic flow and concept drift. The classified events are then processed using DenStream, a clustering algorithm that incorporates a temporal decay mechanism designed to identify dynamic spatial patterns and discard older information. The evaluation is performed in a controlled streaming simulation environment that replicates the dynamics of cities such as Panama and Guayaquil. The proposed framework demonstrated robust quantitative performance, achieving a prequential accuracy of up to 86.4% and a weighted F1-score of 0.864 in the Panama scenario, maintaining high stability against semantic noise. The results suggest that this hybrid architecture is a highly viable approach for urban traffic monitoring, providing useful information for Intelligent Transportation Systems (ITS) by processing authentic social signals. Full article

Traffic flow prediction plays an important role in intelligent transportation systems. However, accurately modeling traffic dynamics remains challenging due to complex temporal correlations and spatial interactions across road networks. In this work, we propose a Mamba-based macro–micro spatio-temporal model for traffic flow prediction. Unlike graph-based approaches that rely on predefined adjacency matrices to model spatial relationships, our method treats sensor nodes as sequence elements and applies Mamba blocks along the spatial dimension. Through the global receptive field of the structured state space model, spatial dependencies are implicitly learned without requiring explicit graph structures. The proposed architecture consists of stacked spatio-temporal blocks, each composed of two Macro Feature Blocks and one Micro Feature Block. The Macro Feature Blocks are designed to capture global temporal dependencies and spatial interactions across all nodes, while the Micro Feature Block focuses on modeling localized spatio-temporal patterns at a finer granularity. By applying structured state space modeling along both temporal and spatial dimensions, the model is able to capture long-range temporal dependencies and global spatial correlations without relying on explicit graph structures. Experiments conducted on four real-world datasets demonstrate that the proposed model achieves competitive or improved performance compared with existing baseline methods under standard evaluation metrics. Full article

The data plane and the control plane are targets for Distributed Denial of Service (DDoS) attacks in the Software-Defined Internet of Things (SDIoT). Currently available studies rely on observations from a single network layer which limits the cross-layer attack analysis. This paper presents a synchronized, phase-aware, and a multi-layer traffic collection framework mimicking SDIoT environments under diverse DDoS attack scenarios. The data collected are the metrics captured at host, switch, and controller layers during normal, attack, and post-attack phases with strict temporal alignment. For capturing diverse DDoS attack behaviors in SDIoT environments, representative data plane attacks including volumetric flooding and switch-level flow table saturation were used. Control plane level attack targeting the SDN controller was implemented. The evaluation was done using a Mininet-based SDIoT testbed with a POX controller. Each scenario is executed across five independent runs with statistical validation. The proposed framework enables reproducible and time-aligned multi-layer analysis through standardized orchestration and automated logging. Results indicate that SDIoT DDoS behavior demonstrates differently across traffic, state, and resource-level metrics, and that accurate characterization benefits from temporally aligned multi-layer monitoring rather than relying solely on packet rate analysis. Full article

Background: Smart urban logistics has emerged as a key element of sustainable city development, with direct effects on economic performance, environmental quality, and urban livability. Issues with traffic, pollutants, infrastructure strain, and last-mile delivery efficiency have become more pressing due to rapid urbanization and the expansion of e-commerce. In this regard, underground or enclosed corridor-based tube-based freight transit systems have surfaced as a viable smart infrastructure option for automated and low-impact commodities delivery. Methods: This study adopts an analytical literature review complemented by a structured case study analysis to examine the potential role of tube-based freight transport systems in future urban logistics. Key technological concepts, including pneumatic tubes, automated capsule transport, and integration with digital platforms, the Physical Internet, and smart city management systems, are examined through a structured analytical review of the literature. Results: The outcome of the reviewed studies indicates that tube-based systems can contribute to congestion alleviation, emission reduction, and improved delivery reliability by shifting selected freight flows away from surface transport networks. However, governance frameworks, infrastructure integration, and institutional coordination mechanisms continue to have a significant impact on claimed performance outcomes. Conclusions: Tube-based freight systems represent a promising but conditional pathway toward smarter and more sustainable urban logistics. Their large-scale deployment is forced by high capital costs, standardization challenges, regulatory uncertainty, and social acceptance issues. Coordinated investment plans, encouraging legal frameworks, and integrated urban planning techniques in line with smart city goals are needed to overcome these obstacles. Full article

Sustainable urban development increasingly depends on understanding how digital activity is distributed across space and time, yet the spatiotemporal dynamics of the urban digital landscape remain poorly mapped by conventional data sources. This study uses Beijing as an empirical testbed, applying a multi-dimensional analytical framework to massive mobile network traffic data to decode the metabolic rhythms, distributional laws, and functional organization of the urban digital landscape. The results reveal three findings. First, the urban digital landscape exhibits a sleepless trapezoidal temporal rhythm characterized by continuous saturation without a midday trough and a quantifiable weekend activation lag, indicating that digital metabolism is structurally decoupled from physical mobility patterns. Second, digital traffic follows a skew-normal distribution consistent with a 20/70 rule of spatial polarization, in which the top 20% of super-connector nodes sustain approximately 70% of total urban digital flow, yielding a Gini coefficient of 0.68 as a measurable indicator of infrastructure inequality and systemic vulnerability. Third, four distinct functional prototypes are identified—ranging from continuously active metropolitan cores to inverse-tidal ecological peripheries—empirically validating Beijing’s polycentric transformation through the lens of digital flows. These findings demonstrate that large-scale mobile network traffic data offers a replicable and structurally distinct lens for sustainable urban digital governance, supporting resilient network planning, equitable allocation of digital resources, and evidence-based monitoring of urban functional transformation in rapidly growing megacities. Full article

Traffic accidents at urban intersections represent a major road safety concern, particularly those caused by traffic signal violations. To analyze accident mechanisms and develop effective prevention strategies, this study employs a cellular automata model to investigate the relationship between accident probability $P_{ac}$ and traffic parameters at signalized intersections. Simulation results reveal a nonlinear relationship between Pac and traffic demand. The accident probability reaches a maximum under free-flow conditions and subsequently decreases as congestion increases, eventually stabilizing at a nearly constant level under highly congested traffic. Additionally, collision risk increases with lane-changing probability $P_{chg}$, especially upstream of the intersection. High traffic speeds significantly elevate both accident probability and severity. Finally, the results indicate that extending traffic signal cycle durations is not an effective strategy for reducing accident risk. Overall, the proposed model provides a useful framework for estimating accident risk under different traffic conditions and supporting traffic management, including control decisions aimed at improving road safety. Full article

The network traffic of 3D parallel training in large-scale deep learning, featuring burstiness, hot-spots, and periodic large-bandwidth patterns, severely challenges network efficiency, necessitating a high-performance and flexible optical network solution. To address this, this paper proposes Mercury, a hybrid optical network based on physical optical components: its optical timeslot switching (OTS) subnet uses an arrayed waveguide grating router (AWGR) and tunable lasers for dynamic traffic, while the optical circuit switching (OCS) subnet relies on wavelength selective switches (WSSs) for low-latency high-bandwidth transmission, which is coordinated by selective valiant load balancing (S-VLB) and most efficient path configuration (MEPC) mechanisms. Validated via simulations and FPGA-based testbed experiments, Mercury outperforms the Sirius network by reducing epoch training time (e.g., 179s with five jobs) and relieving OTS congestion through offloading large flows to OCS. This work demonstrates that Mercury provides a flexible, high-performance physical optical solution for 3D parallel training of large-scale deep learning models. Full article

Accurate characterization of inter-vehicle spacing is fundamental for safety assessment and sustainable operation of road networks, particularly on two-lane rural roads where monitoring infrastructure is limited. Unlike temporal headways, vehicle spacing directly reflects physical vehicle interactions and roadway occupancy, making it a more appropriate variable for evaluating collision risk and operational efficiency. This study develops a probabilistic framework for modeling vehicle spacing based on the statistical isomorphism between Event Flows and Linear Fields of Random Points. Using a calibrated microscopic simulation model, spacing distributions are generated for unidirectional traffic over flow rates from 100 to 1300 veh/h. A Pearson Type III distribution is shown to consistently reproduce the observed asymmetry, kurtosis, and non-zero minimum spacing across traffic regimes. Distribution parameters are estimated via maximum likelihood and validated using a heuristic Kolmogorov–Smirnov procedure suitable for large samples. Results demonstrate systematic relationships between spacing distribution parameters and macroscopic traffic variables, enabling estimation of the probability of unsafe spacing conditions from commonly available traffic data. The proposed framework supports sustainability-oriented traffic management by providing a quantitative basis for safety evaluation and operational control without requiring extensive sensing infrastructure. Full article

Accurate and reliable short-term traffic flow prediction is crucial for managing urban congestion but is challenged by the complex spatio-temporal dependencies inherent in traffic systems. Conventional single models, such as Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), often fail to capture these nonlinear dynamics. To address this, we propose a novel Bayesian-Optimized Mixture of Experts (BO-MoE) framework. This hybrid architecture utilizes a Mixture of Experts (MoE) to dynamically integrate multiple specialized deep learning models, allowing it to adapt to diverse and complex traffic patterns. Bayesian Optimization (BO) is further integrated to automate hyperparameter tuning, significantly enhancing predictive accuracy and model efficiency. We evaluated BO-MoE on three real-world traffic datasets. Empirical results demonstrate that our model consistently outperforms strong baselines, including TCN. Specifically, on PEMS04, it reduces MAE, RMSE, and MAPE by 1.97%, 1.19%, and 3.23%, respectively, while on PEMS08, the corresponding reductions reach 3.83%, 1.26%, and 5.49%. On the NZ dataset, BO-MoE also achieves superior performance, with improvements comparable to those on PEMS benchmarks. Full article