Search Results (45)

Control subarea division is essential for coordinated signal control, but methods based mainly on local correlation or static topology may not adequately capture traffic-state propagation under dynamic traffic loading. This study proposes a control subarea division method that explicitly models traffic-state propagation by integrating state-guided colored random walk and path entropy analysis. Intersection correlation degree and traffic state are used to construct a state-guided colored random walk process, in which transition probabilities are updated according to network connectivity and traffic-state consistency. Path entropy characterizes propagation uncertainty, and control subareas are identified by minimizing the distribution discrepancy between node-level and subarea-level path responses. To compare partitioning schemes, five complementary metrics were adopted: variance reduction rate of spatial delay, delay reduction rate, congestion mitigation index, stop reduction rate, and queue reduction rate. A VISSIM microsimulation model with dynamic traffic loading was developed to compare the proposed method with the Whitson and Fast Newman methods. The proposed method achieved the best performance across all five metrics, with values of 41.47%, 23.77%, 25.96%, 23.59%, and 15.08%, respectively. These results indicate that the proposed method improves spatial balance and network efficiency while mitigating bottlenecks, reducing stops, and suppressing queue accumulation. Full article

Road disasters such as subsidence and bridge failures pose severe threats to traffic safety. Existing warning distance calculation methods typically assume homogeneous traffic flow and overlook the spatial heterogeneity of vehicle responses across different vehicle types, limiting their applicability for geospatial early warning systems. This paper proposes a dynamic warning distance model that integrates mixed-traffic flow composition—comprising human-driven vehicles (HDVs), Level 2 advanced driver-assistance system vehicles (ADASVs), and automated vehicles (AVs) of Level 3 and above—within a geospatial risk propagation framework. The model introduces vehicle-type weighting coefficients to quantify response differences, incorporates interaction delays calibrated through SUMO microsimulations, and accounts for cascading reaction delays caused by abrupt HDV braking. The methodology is illustrated using a counterfactual reconstruction of the 2024 Meizhou–Dapu Expressway collapse in China (52 fatalities). Based on reconstructed traffic conditions (80% HDVs, 15% ADASVs, 5% AVs; average speed 27.5 m/s; flow 1800 veh/h), the calculated dynamic warning distance is 153 m, which is 12% shorter than the speed-matched conventional stopping sight distance of 174 m (computed under consistent wet-pavement assumptions). Sensitivity analyses reveal that warning distance decreases substantially with increasing AV penetration (to 42 m in AV-dominated scenarios, a potential reduction of up to 74% compared with the HDV-dominated baseline, provided that residual HDVs are supported by V2X-based alerting) and varies monotonically with traffic flow, demonstrating the model’s adaptive capability. The proposed framework provides a theoretical foundation for adaptive geospatial disaster warning strategies and offers practical guidance for infrastructure development in the era of mixed-traffic automation. Full article

This study evaluates the On-Demand All-Red Interval (ODAR) at signalized intersections to address red-light running (RLR) issues. Traditional fixed all-red intervals fail to adapt to dynamic traffic conditions, leading to potential safety risks and unnecessary delays. This study introduces a novel approach for dynamically extending the all-red interval on demand to enhance intersection efficiency while maintaining safety by eliminating unnecessary clearance intervals when no risk exists. Utilizing software-in-the-loop simulation, the study assesses the effectiveness of the ODAR method compared to conventional fixed-duration and Dynamic All-Red Extension (DARE) methods, allowing realistic controller testing without field deployment. The ODAR method adapts to real-time traffic conditions by incorporating vehicle speed and signal timing, ensuring vehicles with high collision risk clear the intersection safely. The study is conducted using a microsimulation model based on the Washington Street arterial network in Lake County, Illinois, validated against real traffic conditions. The results demonstrate that ODAR increases throughput and, in specific scenarios, reduces delays and stop occurrences compared to FAR and DARE strategies, based on a field-calibrated microsimulation dataset of a real-world arterial corridor. Importantly, these efficiency improvements are achieved while maintaining comparable intersection safety outcomes, as measured by red-light-running events, conflict frequency, and conflict severity. Full article

This paper proposes a comprehensive framework for integrating Digital Twins (DT) with real-time traffic optimization systems to enhance urban mobility management in Smart Cities. Using the Pobitno Roundabout in Rzeszów as a case study, we established a calibrated microsimulation model (validated via the GEH statistic) that serves as the core of the proposed Digital Twin. The study goes beyond static scenario analysis by introducing an Adaptive Inflow Metering (AIM) logic designed to interact with IoT sensor data. While traditional geometrical upgrades (e.g., turbo-roundabouts) were analyzed, simulation results revealed that geometrical changes alone—without dynamic control—may fail under peak load conditions (resulting in LOS F). Consequently, the research demonstrates how the DT framework allows for the testing of “Software-in-the-Loop” (SiL) solutions where Python-based algorithms dynamically adjust inflow parameters to prevent gridlock. The findings confirm that combining physical infrastructure changes with digital, real-time optimization algorithms is essential for achieving sustainable “green transport” goals and reducing emissions in congested urban nodes. Full article

Background/Objectives: Obesity is a major global public health and economic challenge. Governments worldwide have implemented nutrition-focused policies such as sugar-sweetened beverage taxes, front-of-pack labeling, food assistance reforms, and school nutrition standards to improve diet quality and reduce obesity. Because large-scale randomized controlled trials are often infeasible and conventional epidemiologic methods overlook population heterogeneity and behavioral feedback, microsimulation modeling has become a key tool for evaluating long-term and distributional policy impacts. This scoping review examined the application of microsimulation to obesity-related nutrition policies, focusing on model structure, behavioral parameterization, and integration of economic and equity analyses. Methods: Following PRISMA guidelines (PROSPERO CRD42024599769), five databases were searched for peer-reviewed studies. Data were extracted on policy mechanisms, model design, parameterization, and equity analysis. Study quality was assessed using a customized 21-item checklist adapted from CHEERS and NIH tools. Results: Twenty-nine studies met the inclusion criteria, with most policy settings based in the United States. Most employed dynamic, stochastic, individual-level microsimulation models with diverse behavioral assumptions, obesity equations, and calibration approaches. While most studies stratified outcomes by socioeconomic or demographic group, only one used a formal quantitative equity metric. Conclusions: Microsimulation modeling provides valuable evidence on the long-term health, economic, and distributional impacts of nutrition policies. Future work should strengthen methodological transparency, standardize equity assessment, and expand application beyond high-income settings to improve the comparability, credibility, and policy relevance of simulation-based nutrition policy research. Full article

Background/Objectives: Europe’s demographic shift is putting increasing pressure on long-term care (LTC) systems and raising concerns about the sustainability of LTC financing. In this paper, we analyse Austria’s LTC system, particularly its universal long-term-care allowance (LTCA), and aim to project LTCA expenditure under different future scenarios. Methods: We use a dynamic microsimulation model to project LTCA expenditure under four scenarios up to the year 2080. Combining LTCA statistics with pooled data from the Survey of Health, Ageing and Retirement in Europe (SHARE), we estimate care needs and prevalence rates across all seven care allowance levels. This enables us to project both public spending and individual lifetime costs, disaggregated by sex and education. Results: Although total LTCA expenditure is projected to rise due to population ageing, scenario comparisons show that compositional shifts—such as higher educational attainment, which is linked to lower care needs and gains in healthy life expectancy accompanying mortality improvements—can significantly mitigate cost growth. The projected total expenditure increases range from 29% in a scenario where increasing life expectancy—as assumed in official population projections—is neglected to 185% in a scenario accounting for rising life expectancy but no future health gains. The findings also highlight the impact of longevity and education on the distribution of individual lifetime costs. Conclusions: Beyond its policy implications for LTC planning, this study demonstrates the advantages of dynamic microsimulation in capturing individual-level heterogeneity, offering a significant improvement over traditional macrosimulation approaches. Full article

Urban congestion causes further increases in travel times, fuel consumption and greenhouse-gas emissions. In this regard, we conduct a systematic study of a Genetic Algorithm (GA) for real-time routing in an urban scenario in Bethlehem City, based on a SUMO microsimulation that has been calibrated using real data from the field. Our work makes four main contributions: (i) the implementation of a reproducible GA framework for dynamic routing with explicit constraints and adaptive termination criterion; (ii) design of a weight sensitivity study for studying a multi term fitness function with travel time and waiting time, and optionally fuel usage; (iii) an edge-assisted distributed architecture on roadside units (RSUs) supported by cloud services; and (iv) specifying and refining the data set description and experimental protocol with a planned statistical analysis. Empirical evidence from the Bethlehem case study shows a consistent decline in total travel time under high congestion cases. Variations in the waiting time between different scenarios are exhibited, reflecting the trade-offs in the fitness weighting scheme. We recognize that we have some limitations, including the manual resolution of data and the inherent problem of differences between simulations and real world, and we are proposing a road-map towards a pilot deployment that handles these issues. Rather than proposing a new GA variant, we present a deployment-oriented framework-an edge- assisted GA with explicit protocols and a latency envelope, and a reproducible multi-objective tuning procedure validated on a city-scale network under severe congestion. Full article

Urban traffic congestion is a significant challenge faced by cities globally, resulting in delays, increased emissions, and diminished quality of life. This study introduces an innovative traffic load-balancing algorithm developed as part of the IN2CCAM Horizon 2020 project, which was specifically tested in the city of Vigo, Spain. The proposed method incorporates short-term traffic forecasting through machine learning models—primarily Long Short-Term Memory (LSTM) networks—alongside a dynamic routing algorithm designed to equalize travel times across alternative routes. Historical speed and volume data collected from Bluetooth sensors were analyzed and modeled to predict traffic conditions 15 min ahead. The algorithm was implemented within the PTV Vissim microsimulation environment to assess its effectiveness. Results from 20 distinct traffic scenarios demonstrated significant improvements: an increase in average speed of up to 3%, an 8% reduction in delays, and a 10% decrease in total standstill time during peak weekday hours. Furthermore, average emissions of CO₂, NO_x, HC, and CO were reduced by 4% to 11% across the scenarios. These findings highlight the potential of integrating predictive analytics with real-time load balancing to enhance traffic efficiency and promote environmental sustainability in urban areas. The proposed approach can further support policymakers and traffic operators in designing more sustainable mobility strategies and optimizing future urban traffic management systems. Full article

Integrating advanced technologies, such as Connected Autonomous Vehicles (CAVs) and Connected Vehicles (CVs), represents new strategies and solutions in urban mobility, particularly during unexpected urban events. Vehicle connectivity facilitates real-time communication between vehicles and infrastructure, enhancing traffic management by enabling dynamic rerouting to minimize delays and prevent bottlenecks. This study employs the SUMO (Simulation of Urban Mobility) microsimulation to analyze the impact of dynamic rerouting strategies during urban disruptions within the IN2CCAM project’s Turin Living Lab. The Living Lab integrates simulation with real-world testing, including autonomous shuttle operations, to evaluate new mobility solutions. In the initial phase, offline simulations examine street, lane, and intersection closures along shuttle routes to assess how penetration levels of CVs and CAVs influence mobility. The results indicate that higher connectivity penetration improves traffic flow, with the greatest benefits observed at increased levels of autonomous vehicles. These findings highlight the potential of dynamic routing strategies, supported by vehicle connectivity and autonomous driving technologies, to enhance urban mobility and effectively respond to real-time traffic conditions. Additionally, this work demonstrates the capabilities and flexibility of SUMO for simulating complex urban traffic scenarios. Full article

As automation technologies continue to advance within the automotive industry, urban road traffic is gradually shifting from conventional driving toward fully autonomous. This transition is supported by the progressive integration of partially automated functions, such as Adaptive Cruise Control (ACC) and lane-keeping assistance, which are already implemented in commercial vehicles and increasingly affect both individual driving behavior and overall traffic flow dynamics. The main purpose of this research is to evaluate the impact of automated vehicles presence in a complex signalized intersection under mixed traffic conditions, considering different penetration rates and demand levels. A review of previous modeling approaches from the literature was conducted, highlighting critical aspects to be considered in the design and simulation of road traffic. Field traffic data were collected and used as input for a microsimulation model developed in AIMSUN. A base scenario and a 20% growth scenario were analyzed to assess the impact of AV-ACC penetration, varying the AV-ACC’s rates in traffic composition. The results indicate that increased AV-ACC penetration rates, especially beyond 50%, contribute significantly to improving traffic stability and efficiency. Full article

Emerging transportation challenges necessitate cutting-edge technologies for real-time infrastructure and traffic monitoring. To create a dynamic digital twin for intersection monitoring, data gathering, performance assessment, and calibration of microsimulation software, this study presents a state-of-the-art platform that combines high-resolution LiDAR sensor data with VISSIM simulation software. Intending to track traffic flow and evaluate important factors, including congestion, delays, and lane configurations, the platform gathers and analyzes real-time data. The technology allows proactive actions to improve safety and reduce interruptions by utilizing the comprehensive information that LiDAR provides, such as vehicle trajectories, speed profiles, and lane changes. The digital twin technique offers unparalleled precision in traffic and infrastructure state monitoring by fusing real data streams with simulation-based performance analysis. The results show how the platform can transform real-time monitoring and open the door to data-driven decision-making, safer intersections, and more intelligent traffic data collection methods. Using the proposed platform, this study calibrated a VISSIM simulation network to optimize the driving behavior parameters in the software. This study addresses current issues in urban traffic management with real-time solutions, demonstrating the revolutionary impact of emerging technology in intelligent infrastructure monitoring. Full article

This research is part of a broader investigation into innovative simulation-based approaches for improving traffic efficiency and road safety in roundabout corridors. These corridors, composed of successive roundabouts along arterials, present systemic challenges due to the dynamic interactions between adjacent intersections. While previous studies have addressed localized inefficiencies or proposed isolated interventions, this paper introduces possible replicable methodology based on a microsimulation and surrogate safety analysis to evaluate roundabout corridors as integrated systems. In this context, efficiency refers to the ability of a road corridor to maintain stable traffic conditions under a given demand scenario, with low delay times corresponding to acceptable levels of service. Safety is interpreted as the minimization of vehicle conflicts and critical interactions, evaluated through surrogate measures derived from simulated vehicle trajectories. The proposed approach—implemented through Aimsun Next and the SSAM tool—is tested on two real-world corridors: Via Aurelia Nord in Pisa (Italy) and Route de Marseille in Avignon (France), assessing multiple intersection configurations that combine roundabouts and signal-controlled junctions. Results show how certain layouts can produce unexpected performance outcomes, underlining the importance of system-wide evaluations. The proposed framework aims to support engineers and planners in identifying optimal corridor configurations under realistic operating conditions. Full article

The assessment of energy impacts associated with autonomous vehicles must extend beyond individual vehicle analysis to encompass mixed fleets with varying degrees of automation. This study presents an integrated simulation framework designed to evaluate fuel efficiency improvements resulting from both full and partial vehicle automation across diverse road types and vehicle categories. By coupling traffic microsimulation with detailed powertrain modeling, the framework captures the intricate interdependencies between automation levels and energy consumption. A comprehensive analysis reveals the complex interactions among powertrain architectures, automation levels, and driving environments in both urban and highway contexts. Results indicate that the increased penetration of Connected and Autonomous Vehicles (CAVs) is generally associated with improved energy efficiency across a range of vehicle technologies. These findings offer critical insights into the broader implications of CAV adoption on energy consumption, emphasizing the nuanced dynamics between vehicle heterogeneity and traffic conditions. Full article

This paper establishes a theoretical link between actuarial neutrality and the Oaxaca–Blinder decomposition to empirically assess vertical equity in public defined-benefit schemes. We demonstrate how this approach can be generalized to non-linear functions, point systems, and notional accounts. We use an aligned dynamic microsimulation model to apply this method to the first pillar of the Swiss pension system and highlight the following three key effects: (1) the impact of the accrual rate on vertical equity; (2) the assessment of actuarial neutrality through the comparison of migrants with the non-migrant population; and (3) vertical equity across marital statuses. Our findings indicate that changing societal trends, such as increased migration, female labor participation, and the rise in non-marital unions, may alter the extent of vertical equity. This has significant implications for actuarial risk management, as a higher degree of vertical equity is associated with increased pension expenses, thereby raising the financial sustainability risk of the pension system. Future research should explore these dynamics to ensure that pension systems remain both equitable and financially sustainable in the face of evolving societal trends. Full article

In recent years, EU member states have faced two significant crises: the COVID-19 pandemic and high inflation, exacerbated by geopolitical instability. These events have led to a decline in household purchasing power. Our study aims to quantify the impact of these crises on household income dynamics in Romania, using data from the EU Survey on Income and Living Conditions and the EUROMOD tax-benefit microsimulation model. We analyze income changes across demographic segments from 2019 to 2021 (COVID-19 period) and 2021 to 2023 (inflation crisis), focusing on household characteristics such as the presence and number of children, the gender of the household head, and the presence of elderly members. Our findings reveal that while disposable income increased from 2019 to 2021, the poorest 10% experienced the smallest gains. In 2022–2023, inflation eroded these increases, and the lower half of the income distribution struggled with reduced earnings and pensions. Despite this, the Gini coefficient for disposable income decreased from 2019 to 2023, indicating a reduced inequality. Our study highlighted that targeted public policy interventions are essential to support vulnerable households and mitigate the effects of economic shocks. Full article