Search Results (132)

Urban business-to-business distribution in Casablanca relies heavily on light commercial vehicles (LCVs) operating in a constrained street environment where loading/unloading access, intersection capacity, and recurring bottlenecks jointly shape performance and environmental impacts. However, high-resolution freight origin–destination (OD) observations and junction calibration data are limited, which complicates direct estimations of congestion and externalities attributable to commercial activity. This study develops a reproducible, large-scale modeling workflow that couples tour-based freight demand generation in order units with simulation-based traffic assignment (SBA) on a metropolitan network and translates network performance into emissions and monetary losses. Warehouses are modeled as primary producers and commercial activity zones as attractors via sector-tagged production and attraction functions; the resulting order distribution is converted to OD vehicle trips using the tour-based trip generation procedure with the mean targets-per-tour fixed to one to ensure numerical stability, yielding a direct-shipment approximation appropriate for stress–response analysis. Junction impedance is represented through turn-type volume–delay relationships and node-level impedance procedures, and congestion is evaluated using vehicle kilometers traveled/vehicle hours traveled (VKT/VHT)-based indicators, delay-intensity measures, and link/node bottleneck rankings. Across demand-scaling scenarios, VKT increases from 302,159 to 1,017,686 veh·km/day, while network delay rises nonlinearly from 392.5 to 2738.4 veh·h/day, indicating saturation-driven amplification of time losses. The Handbook of Emission Factors for Road Transport (HBEFA)-compatible emission estimates scale with activity: total carbon dioxide (CO₂) increases from 154.1 to 519.5 t/day, and nitrogen oxides (NOx) and particulate matter (PM_2.5) totals rise proportionally under fixed fleet assumptions. Monetizing delay with a purchasing-power-adjusted value-of-time range yields a congestion cost per trip that increases from approximately 0.20 to 0.41 Moroccan dirham, MAD/trip (at 60 MAD/veh·h), consistent with rising delay intensity. Bottleneck extraction shows welfare losses to be structurally concentrated on a small persistent corridor set, led by ‘Boulevard de la Résistance’, with recurrent hotspots including ‘Rue d’Arcachon’ and ‘Rue d’Ifni’. The framework supports policy-relevant reporting of congestion, emissions, and welfare impacts under data scarcity, with explicit sensitivity bounds. Full article

Full-duplex (FD) technology holds great promise for enhancing the spectral efficiency of Mobile Ad Hoc Networks (MANETs) and Wireless Sensor Networks (WSNs). However, the practical performance gain of FD over Half-Duplex (HD) is highly sensitive to the dynamic nature of traffic loads and residual self-interference. Existing Optimal Dynamic Selection Strategies (ODSS) often rely on static workload assumptions within a single time window, failing to capture long-term traffic fluctuations. Consequently, applying instantaneous switching strategies in highly bursty environments necessitates excessively frequent mode switching (e.g., the switching frequency can approach the total number of time windows), incurring prohibitive signaling overhead and unignorable MAC-layer adaptation delays. To overcome these concrete bottlenecks, this paper proposes a comprehensive traffic-aware adaptive duplex mode selection framework. First, we model the multi-scale dynamic workload using Dynamic Activated Probability in Short-term (DAPS) and Long-term (DAPL), effectively characterizing both bursty traffic (via Beta distribution) and Markov-modulated stable traffic. Second, by integrating physical layer performance analysis, we define the Break-even Workload Point (BWP) to partition traffic into Oversaturated (OZ) and Unsaturated (UZ) Workload Zones (WZs). Furthermore, to handle unknown future traffic with low complexity, we propose the Pre-scheduling Duplex selection based on the Workload zone Estimation (PDWE) algorithm. PDWE leverages a Hidden Markov Model (HMM) combined with a Rollout algorithm to estimate hidden traffic states and adaptively pre-schedule duplex modes. Simulation results demonstrate that the proposed strategy achieves near-optimal throughput (approximately 91% of the ideal ODSS) while reducing the duplex switching frequency by two orders of magnitude compared to instantaneous switching strategies. This approach offers a robust cross-layer solution for next-generation self-organizing networks. Full article

Vehicular Ad-hoc Networks (VANETs) must sustain heterogeneous real-time content services, yet static roadside-unit (RSU) roles lead to congestion, coverage voids, and inefficient content delivery under bursty, concurrent demand. To address this issue, we propose a PSO-Based dynamic RSU role assignment framework named PDRA that dynamically adapts roles, coverage, and replication of RSU to current network conditions. A telemetry-based suitability estimator aggregates location, link stability, resource availability, traffic load, and content sensitivity at each RSU and feeds a Particle Swarm Optimization routine that assigns RSUs to Leader/Helper/Inactive roles while enforcing spatial separation between Leaders. An adaptive sectoring mechanism then resizes each cluster RSU’s communication scope—contracting under overload to protect local latency and expanding during slack to assist neighbors—thereby suppressing void areas and balancing service density. On top of the physical layer of RSUs, Leader RSUs cooperatively form a virtual Replication Layer that maintains global visibility of load and content locality to steer requests and replicate popular data near demand, reducing backhaul reliance. Finally, a load- and energy-aware reconfiguration policy orchestrates staged assist/offload, selective Helper activation, PSO-based Leader reassignment, and sleep scheduling for underutilized RSUs, preserving resilience and sustainability. NS-3 urban scenarios corroborate that PDRA improves packet delivery, lowers end-to-end delay, reduces backhaul traffic, and increases fairness over strong baselines. By jointly optimizing role assignment, coverage control, and replication, PDRA offers a scalable and robust solution for VANET content delivery under dynamic, multi-user conditions. Full article

To address the travel delay caused by start-up lag in queued traffic at signalized inter-sections and fill the research gap of unseparated start-up loss characteristics between human-driven vehicles (HDVs) and autonomous vehicles (AVs), this study proposes an analytical start-up loss delay model applicable to both unsaturated and saturated traffic states. The model explicitly quantifies queue-length-dependent cascading propagation effects of start-up loss, and integrates vehicle type-specific parameters based on traffic queue theory and mixed traffic flow field observation data. Conventional models are limited by underestimated delay from neglecting platoon-level start-up loss propagation and failure to account for intrinsic HDV-AV start-up mechanism differences; to resolve these, we first distinguished the two vehicle types’ start-up behaviors (reaction time, acceleration, platoon coordination), then decoupled their start-up loss mechanisms and quantified their delay contributions via theoretical derivation, with validation against field test data and comparison with classical Webster and Clayton models. Field results revealed an order-of-magnitude difference in start-up response: HDVs had an average 2.05 s reaction time with large individual variability, while AVs maintained a stable 0.3–0.5 s response; HDV platoons reached saturated flow at the sixth vehicle, versus the third for AV platoons due to consistent acceleration and shorter headways. Model validation showed that under unsaturated conditions, red light duration significantly affects HDV delay, and the AV mix ratio is exponentially negatively correlated with additional delay. Under saturated conditions, green light duration increases start-up loss delay for both vehicle types, yet the growth rate of AVs (3.1–12.3%) is far lower than that of HDVs (18.2–67.5%), and arranging AVs in the leading position of mixed platoons can further reduce delay. The proposed model improves the accuracy of delay estimation in mixed HDV-AV traffic environments, and provides a theoretical basis for the optimized design of signal control strategies and the efficient management of intersection travel delay. Full article

This study presents a systematic review of Passenger Car Equivalency (PCE) at signalized intersections. The review focuses on comparing PCE calculation methods, examining PCE values across methods, and identifying the key influencing factors. Following the PRISMA methodology, 40 relevant studies were identified. The analysis revealed several critical calculation factors, including road geometry and vehicle composition. These studies employed seven major methods for PCE estimation: (1) headway ratio, (2) regression, (3) delay-based, (4) area occupancy, (5) queue-based, (6) capacity-based, and (7) optimization (Theil’s Coefficient). The findings indicate that PCE values vary substantially across studies, with motorcycle values ranging from $-1.056$ to 1.02, three-wheeler values from 0.22 to 1.51, and heavy vehicle values from 1.13 to 5.06. Cross-study comparisons revealed that this variation exists not only between countries but also between cities within the same country. This variability is attributed to traffic volume, traffic composition, approach width, and differences in driver behavior. The results support treating PCE as a dynamic parameter rather than static, as fixed values from national guidelines may not adequately represent local traffic conditions. Full article

Mid-sized Philippine cities commonly rely on fixed-time traffic signal plans that cannot respond to short-term, demand-driven surges, resulting in measurable idle time at stop lines, increased delay, and unnecessary emissions, while adaptive signal control has demonstrated performance benefits, many existing solutions depend on centralized infrastructure and high-bandwidth connectivity, limiting their applicability for resource-constrained local government units (LGUs). This study reports a field deployment of TrafficEZ, a lightweight edge AI signal controller that reallocates green splits locally using traffic-density approximations derived from cabinet-mounted cameras. The controller follows a macroscopic, cycle-level control abstraction consistent with Transportation System Models (TSMs) and does not rely on stationary flow–density–speed (fundamental diagram) assumptions. The system estimates queued demand and discharge efficiency on-device and updates green time each cycle without altering cycle length, intergreen intervals, or pedestrian safety timings. A quasi-experimental pre–post evaluation was conducted at three signalized intersections in El Salvador City using an existing 125 s, three-phase fixed-time plan as the baseline. Observed field results show average per-vehicle delay reductions of 18–32%, with reclaimed effective green translating into approximately 50–200 additional vehicles per hour served at the busiest approaches. Box-occupancy durations shortened, indicating reduced spillback risk, while conservative idle-time estimates imply corresponding CO₂ savings during peak periods. Because all decisions run locally within the signal cabinet, operation remained robust during backhaul interruptions and supported incremental, intersection-by-intersection deployment; per-cycle actions were logged to support auditability and governance reporting. These findings demonstrate that density-driven edge AI can deliver practical mobility, reliability, and sustainability gains for LGUs while supporting evidence-based governance and performance reporting. Full article

Effective disaster management depends on rapidly understanding earthquake damage, yet traditional methods struggle to operate at scale and rely on expert inspections that become difficult when access is limited or time is critical. Satellite-based damage detection also faces limitations, particularly under adverse weather conditions and delays associated with satellite overpass schedules. This study introduces a machine learning-based approach to assess post-earthquake building damage using real observations collected after the event. The aim is to develop fast and reliable estimation techniques that can be deployed immediately after the mainshock by integrating structural, seismic, and geographic data. Three machine learning models—Random Forest, Histogram Gradient Boosting, and Bagging Classifier—are evaluated across both reinforced concrete and masonry buildings and across multiple spatial levels, including building, district, and city scales. Damage is categorized using practical three-class (traffic light) and detailed four-class systems. The models generally perform better in simpler classifications, with the Bagging Classifier offering the most consistent results across different scales. Although detecting severely damaged buildings remains challenging in some cases, the three-class system proves especially effective for supporting rapid decision-making during emergency response. Overall, this study demonstrates how machine learning can provide faster, scalable, and practical earthquake damage assessments that benefit emergency teams and urban planners. Full article

Traffic state estimation (TSE) is crucial for intelligent transportation systems, as it provides unobserved parameters for traffic management and control. In this paper, we propose a novel physics-guided graph neural network for TSE that integrates traffic flow theory into an estimation framework. First, we constructed wave-informed anisotropic temporal graphs to capture the time-delayed correlations across the road network, which were then merged with spatial graphs into a unified spatiotemporal structure for subsequent graph convolution operations. Then, we designed a four-layer diffusion graph convolutional network. Each layer is enhanced with squeeze-and-excitation attention mechanism to adaptively capture dynamic directional correlations. Furthermore, we introduced the fundamental diagram equation into the loss function, which guided the model toward physically consistent estimations. Experimental evaluations on a real-world highway dataset demonstrated that the proposed model achieved a higher accuracy than benchmark methods, confirming its effectiveness in capturing complex traffic dynamics. Full article

Accurate estimation of branch-level traffic flows at urban Y-intersections from limited mainline measurements remains a critical challenge in intelligent transportation systems. Y-intersections, with their symmetric geometric configuration where multiple branches converge, pose unique challenges from flow coupling, signal-induced periodicity, and merging delays. This study develops a hybrid mathematical modeling framework that integrates piecewise linear segments with periodic components for each branch flow. The model enforces physical constraints including flow conservation, non-negativity, and segment continuity, while incorporating operational features such as signal timing and merging delays. Parameter estimation employs a two-stage optimization approach combining least-squares fitting with constrained nonlinear programming, utilizing sparse mainline detector data and minimal historical priors. Experimental validation across five progressive problem formulations demonstrates robust performance, achieving RMSE values of 3.3432 and 5.4467 for complex multi-branch scenarios while accurately capturing 10-min green/8-min red signal cycles and 2-min merging delays. The method successfully reconstructs branch flow profiles at required time points (07:30 and 08:30), reducing observation requirements by 60–80% while maintaining estimation accuracy. The proposed framework provides a practical and interpretable solution for branch flow estimation under sparse sensing conditions, bridging physics-based modeling with data-driven techniques and offering transportation agencies a deployable tool for intersection monitoring without extensive instrumentation. Full article

Background: Given the rapid global increase in asthma cases, understanding the impact of climate change on respiratory health is necessary for evidence-based policymaking, particularly in low- and middle-income countries (LMICs). Objectives: To estimate the short-term associations between temperature (mean and diurnal range), particulate matter (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), ozone (O₃), and childhood asthma exacerbations in Cape Town, South Africa. Methods: We analysed daily hospital records (n = 7753; 2009, 2014, 2019) alongside citywide air quality and meteorological data using negative binomial mixed-effects models and distributed lag non-linear models to capture delayed effects. Results: NO₂ and PM₁₀ were consistently associated with a higher exacerbation risk, with additional delayed effects observed for PM_2.5, PM₁₀, and NO₂. Mean temperature and diurnal temperature range were also linked to an increased risk at short (lag 0–1) and medium (lag 4–5) delays. Conclusions: Temperature variability and traffic-related air pollution contribute to childhood asthma exacerbations in urban LMIC settings. The findings support child-centred early warning systems and stricter air quality controls aligned with WHO guidance. Full article

Accurate prediction of maximum flow latency is crucial for ensuring the efficient transport of latency-sensitive fronthaul traffic in packet-switched Xhaul networks while maintaining the reliable operation of 5G and beyond Radio Access Networks (RANs). Deterministic worst-case (WC) models provide strict latency guarantees but tend to overestimate actual delays, resulting in resource over-provisioning and inefficient network utilization. To address this limitation, this study evaluates a data-driven Quantile Regression (QR) model for latency prediction in Time-Sensitive Networking (TSN)-enabled packet-switched Xhaul networks operating under dynamic traffic conditions. The proposed QR model estimates high-percentile (tail) latency values by leveraging both deterministic and queuing-related data features. Its performance is quantitatively compared with the WC estimator across diverse network topologies and traffic load scenarios. The results demonstrate that the QR model achieves significantly higher prediction accuracy—particularly for midhaul flows—while still maintaining compliance with latency constraints. Furthermore, when applied to dynamic Xhaul network operation, QR-based latency predictions enable a reduction in active processing-node utilization compared with WC-based estimations. These findings confirm that data-driven models can effectively complement deterministic methods in supporting latency-aware optimization and adaptive operation of 5G/6G Xhaul networks. Full article

Various intersection structures are utilized in city-wide traffic network infrastructure by local transportation authorities to handle the exponentially increasing traffic loads in developing countries. In this regard, numerous studies have considered the notable positive contribution of the modern roundabouts in intersection performance as a prominent method utilized widely in our contemporary world. Properly designed roundabouts are vital components of sustainable transportation planning, as they significantly influence traffic efficiency, safety, and environmental performance. Accurate estimation of roundabout capacity is essential to ensure that they can accommodate anticipated traffic volumes without causing congestion, thereby contributing to energy efficiency and reducing emissions. Moreover, sustainable roundabout design supports the development of safer and more inclusive transportation networks by improving accessibility for all road users, thus strengthening the overall sustainability of urban mobility. The SIDRA (version 8.0), a traffic simulation software, is frequently employed in performance analysis and determining the effects of possible outcomes of different scenarios of roundabouts in today’s world. On the other hand, driver behaviors are found to play a significant role in software performance during the analysis process of roundabout capacity and performance. Therefore, in order to optimize the environmental factor (EF) representing driver behaviors in the SIDRA software, a Differential Evolution Algorithm-Based Bi-Level Calibration Model (DEBCAM) was introduced. Observation data collected from eight different modern-structured roundabouts through drones were run into the SIDRA simulation software; the average delays obtained were employed to estimate optimum EF values through DEBCAM. Observed average delay values were taken into consideration with respect to the delay values obtained as a result of the SIDRA calibration by using the GEH statistics. GEH values indicate the consistency of vehicle delay data obtained via the DEBCAM with observed data. Acquired results clearly suggest that the SIDRA software needs to be calibrated so that it can represent drivers’ behaviors. After determination of the optimum values of the EF parameter for calibration of the SIDRA software, the regression analysis was conducted through the Partial Least Squares (PLS) method. As a result of the analysis, a capacity estimation model was developed, which displayed a significant conformity with the SIDRA capacity estimation results. Our findings suggested that the parameter requirement for the roundabout capacity estimation can be decreased by employing the appropriate EF value for the roundabout that needs to be analyzed. Full article

The efficiency of intelligent urban mobility increasingly depends on adaptive mathematical models that can optimize multimodal transportation resources under stochastic and heterogeneous conditions. This study proposes a Markovian stochastic modeling and metaheuristic optimization framework for the adaptive management of bus lane capacity in mixed connected traffic environments. The heterogeneous vehicle arrivals are modeled using a Markov Arrival Process (MAP) to capture correlated and busty flow characteristics, while the system-level optimization aims to minimize total fuel consumption through discrete lane capacity allocation. To support real-time adaptation, a Hidden Markov Model (HMM) is integrated for queue-length estimation under partial observability. The resulting nonlinear and nonconvex optimization problem is solved using Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO), ensuring robustness and convergence across diverse traffic scenarios. Numerical experiments demonstrate that the proposed stochastic–adaptive framework can reduce fuel consumption and vehicle delay by up to 68% and 65%, respectively, under high saturation and connected-vehicle penetration. The findings verify the effectiveness of coupling stochastic modeling with adaptive control, providing a transferable methodology for energy-efficient and data-driven lane management in smart and sustainable cities. Full article

Extreme weather events, such as storms, pose significant challenges to the reliability and efficiency of urban road networks, making intersection design and management critical to maintaining mobility. This paper addresses the dual objectives of traffic efficiency and resilience by evaluating the performance of roundabouts, signalized, and two-way stop-controlled (TWSC) intersections under normal and storm-disrupted conditions. A mixed-method approach was adopted, combining a heuristic framework from the Highway Capacity Manual with microsimulations in AIMSUN Next. Three Polish case studies were examined; each was modeled under alternative control strategies. The findings demonstrate the superior robustness of roundabouts, which retain functionality during power outages, while signalized intersections reveal vulnerabilities when control systems fail, reverting to less efficient TWSC behavior. TWSC intersections consistently exhibited the weakest performance, particularly under high or uneven traffic demand. Despite methodological differences in delay estimation, the convergence of results through Level of Service categories strengthens the reliability of findings. Beyond technical evaluation, the study underscores the importance of resilient intersection design in climate-vulnerable regions and the value of integrating analytical and simulation-based methods. By situating intersection performance within urban resilience, this research provides actionable insights for policymakers, planners, and engineers seeking to balance efficiency with adaptability in infrastructure planning. Full article

Accurate prediction of downstream vehicle arrivals is pivotal for intelligent signal control, yet many advanced controllers depend on high-resolution trajectories that are rarely available outside connected-vehicle settings. We present a deployable alternative that converts ubiquitous Automatic License Plate Recognition (ALPR) timestamps into the predictive inputs required by modern controllers. The method couples a Hidden Markov Model (HMM) for separating free-flow samples from signal-induced delays with a dynamic platoon-dispersion model that is re-estimated online in a rolling window to forecast downstream arrival profiles in real time. In a Simulation of Urban Mobility (SUMO) corridor testbed, the proposed framework consistently outperforms fixed-kernel dispersion and fixed-travel-time baselines, reducing RMSE by 57–75% and MAE by 53–73% across demand levels; ablation results confirm that HMM-based filtering is the dominant contributor to the gains. Robustness experiments further show stable parameter estimation under low ALPR matching rates, indicating suitability for real-world conditions where data quality fluctuates. Because it operates with existing roadside cameras and lightweight inference, the framework is readily integrable into adaptive signal strategies and broader smart-city traffic management. By turning discrete ALPR events into reliable arrival predictions, it bridges the gap between advanced signal control and today’s sensing infrastructure, enabling cost-effective real-time signal optimization in data-constrained urban networks. Full article