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Large-scale microsimulations are increasingly resourceful tools for analysing in detail citywide effects and alternative scenarios of our policy decisions, approximating the ideal of ‘urban digital twins’. Yet, these models are costly and impractical, and there are surprisingly few published examples robustly validated with empirical data. This paper, therefore, presents a new large-scale agent-based traffic microsimulation for the Barcelona urban area using SUMO to show the possibilities and challenges of building these scenarios based on novel fine-grained empirical big data. It combines novel mobility data from real cell phone records with conventional surveys to calibrate the model comparing two different dynamic assignment methods for getting an operationally realistic and efficient simulation. Including through traffic and the use of a stochastic adaptive routing approach results in a larger 24-hour model closer to reality. Based on an extensive multi-scalar evaluation including traffic counts, hourly distribution of trips, and macroscopic metrics, this model expands and outperforms previous large-scale scenarios, which provides new operational opportunities in city co-creation and policy. The novelty of this work relies on the effective modelling approach using newly available data and the realistic robust evaluation. This allows the identification of the fundamental challenges of simulation to accurately capture real-world dynamical systems and to their predictive power at a large scale, even when fed by big data, as envisioned by the digital twin concept applied to smart cities.

In urban corridors, roundabouts often operate in close proximity to signalized intersections, yet the safety implications of their mutual interaction remain insufficiently explored. This study combines field measurements and VISSIM (PTV VISSIM Academic 2023, SP 5) microsimulation with the Surrogate Safety Assessment Model (SSAM) to analyze roundabout–signalized intersection pairs under varying outer radii (12–22 m), spacings (40–160 m), signal red times (17–27 s), and traffic distributions. A multiple linear regression model for predicting the total number of conflicts is developed and partially validated using calibrated real-site models for corridors in Osijek and Poreč, Croatia. Small spacings (40 m) increase the total number of conflicts by 40–60% for small roundabouts (R = 12 m) and 20–40% for larger radii compared with isolated operation. Increasing the outer radius (inscribed circle radius) from 12 to 17 m reduces conflicts by up to about 90%, while longer red times further lower conflicts, especially for small roundabouts. The final regression model, based on spacing, red time, and outer radius, explains about 80% of the variance in conflicts and shows good agreement with SSAM estimates within its applicability range, providing a practical tool for safety-oriented design of urban roundabout–signalized intersection corridors, thereby contributing to the goals of developing a sustainable transport system in a complex urban environment.

Recently, the number of traffic safety studies involving connected and autonomous vehicles (CAVs) has been increasing. Due to the lack of information regarding the real behaviour of CAVs in mixed traffic flow, traffic simulation platforms are used to provide a reasonable approach for testing various scenarios and fleets. It is necessary to analyse how traffic safety is affected when key parameter assumptions are changed. The current study conducts a sensitivity analysis to identify the parameters used in CAV calibration that have the highest influence on traffic safety. Using a microsimulation-based surrogate safety assessment model approach (SSAM), traffic conflicts were identified, and a ceteris paribus analysis was conducted to measure the effect of gradually changing each parameter on the number of conflicts. Afterwards, a two-at-a-time sensitivity analysis was performed to explore the influence of simultaneously varying two parameters. The results revealed that reaction time, clearance, maximum acceleration, normal deceleration, and the sensitivity factor are key parameters. Studying these parameters two at a time revealed that low maximum acceleration, when combined with other parameters, consistently resulted in the highest number of conflicts, while combinations with short reaction time always yielded the best traffic safety results. This investigation broadens the understanding of CAV behaviour for future implementation for both manufacturers and researchers.

Traffic congestion in urban areas is a pressing challenge, with roundabouts and signalized intersections offering different operational benefits. This study explores the integration of these two intersection types, focusing on the optimal distance between them to ensure efficient traffic flow. Using traffic microsimulations in VISSIM, the research examines multiple scenarios involving isolated roundabouts and those adjacent to signalized intersections, considering variables such as peak-hour traffic volume, flow distribution, and intersection spacing. Results indicate that shorter distances (<50 m) between roundabouts and signalized intersections lead to increased traffic indicators due to congestion spillback. In contrast, distances exceeding 100 m mitigate these inefficiencies, approaching the performance of isolated roundabouts. Balanced traffic distribution between approaches (50:50) enhance system performance at lower volumes but exacerbate congestion at higher volumes. A novel aspect of this study is the development of a regression model that integrates microsimulation outputs to predict travel time based on peak-hour traffic volume, flow ratios, and intersection distance, demonstrating a 90.9% explanatory power. These findings emphasize the need for strategic planning in integrating roundabouts and signalized intersections to balance operational efficiency.

This study introduces a data-driven approach to calibrate microscopic traffic simulation models like VISSIM using high-resolution trajectory data, aiming to improve simulation accuracy and fidelity. The study focuses on a highway segment of NJ-3 and NJ-495 in Hudson County, New Jersey, selected as a case study for its high traffic volume and strategic significance. Trajectory data from 338 connected vehicles, sourced from the Wejo dataset, a global provider of anonymized, high-resolution vehicle movement data, along with traffic volume data from Remote Traffic Microwave Sensors (RTMS), served as inputs. The trajectories produced by the simulation model were compared to the ground truth to measure discrepancies. By adjusting driving behavior parameters (e.g., car-following and lane-changing behaviors) and other factors (e.g., desire speed), a Genetic Algorithm was adopted to minimize these differences. Results showed significant improvements, including a 14.19% reduction in mean error, an 18.27% reduction in median error, and a 22.57% reduction in the 75th percentile error during calibration. In the validation phase, the calibrated parameters yielded a 32.68% reduction in mean error, demonstrating the framework’s robustness. This study presents a scalable calibration framework using connected vehicle data, providing tools for accurate simulation, real-time traffic management, and infrastructure planning.

On-street parking is a commonly used form of parking facility as part of transportation infrastructure. However, the emergence of connected and autonomous vehicles (CAVs) is expected to significantly impact parking in the future. This study aims to investigate the impacts of on-street parking regulations for CAVs on the environment, safety and mobility in mixed traffic fleets. To achieve this goal, a calibrated and validated network model of the city of Leicester, UK, was selected to test the implementation of CAVs under various deployment scenarios. The results revealed that replacing on-street parking with driving lanes, cycle lanes, and public spaces can lead to better traffic performance. Specifically, there could be a 27–30% reduction in travel time, a 43–47% reduction in delays, more than 90% in emission reduction, and a 94% reduction in traffic crashes compared to the other tested measures. Conversely, replacing on-street parking with pick-up/drop-off stations may have a less significant impact due to increased stop-and-go events when vehicles pick-up and drop-off passengers, resulting in more interruptions in the flow and increased delays. The paper provides examples of interventions that can be implemented for on-street parking during a CCAM era, along with their expected impacts in order for regional decision-makers and local authorities to draw relative policies. By replacing on-street parking with more efficient traffic measures, cities can significantly improve mobility, reduce emissions, and enhance safety.

Autonomous vehicles (AVs) have been a rapidly emerging phenomenon in recent years, with some automated features already available in vehicles. AVs are expected to potentially revolutionize the existing inefficient state of urban transportation and be a step closer to environmental sustainability. This study focuses on simulation modeling in assessing the potential effects of autonomous vehicles (AVs) and on mobility and safety by developing a framework model based on traffic microsimulation for a real network located in Al-Madinah, Saudi Arabia. The market penetration rates (MPRs) will not reach 100% in the near future; instead, penetration will progressively increase. As a result, in our study, we investigated the potential effect of AV technology in five different AV market penetration rates: 0% (baseline), 25%, 50%, 75%, and 100%. The results suggest that Avs significantly improve the network’s safety and operational performance at high penetration rates. Specifically, estimated vehicle delays decreased by 26%, 34.4%, 63.7%, and 74.2% for 25%, 50%, 75%, and 100% AV penetration rates, respectively. Finally, we think this study will help decisionmakers over in the long-term in their attempts to achieve sustainable development through the optimal integration of innovative and novel technologies.

Autonomous vehicles (AVs) hold strong potential to redefine traffic operations, yet their impacts at varying penetration levels within mixed traffic remain insufficiently quantified. This study evaluates the influence of SAE Level 5 AVs on traffic performance at two typical urban signalized intersections using a hybrid microsimulation approach that integrates behavioral AV modeling and performance evaluation. The analysis covers two typical intersection layouts, one with two through lanes and another with three, tested under varying traffic volumes and left-turn shares. A total of 324 simulation scenarios were conducted with AV penetration ranging from 0% to 100% (in 20% increments) and left-turn proportions of 15%, 30%, and 45%. The results show that 100% AV penetration lowers the average delay by up to 40% in the two-lane intersection scenario and 32% in the three-lane scenario, relative to the 0% AV baseline. Even 20% AV penetration yields about half of the maximum improvement. The greatest benefits occur with aggressive AV driving profiles, balanced approach volumes, and small left-turn shares. These findings provide preliminary evidence of AVs’ potential to enhance intersection efficiency and support Sustainable Development Goals (SDGs) 11 and 13, offering insights to guide intersection design and AV deployment strategies for data-driven, sustainable urban mobility.

This paper presents a microsimulation-driven framework to analyze the performance of connected and automated vehicles (CAVs) alongside vehicles with human drivers (VHDs), channeled towards assessing project alternatives in road intersection design. The transition to fully automated mobility is driving the development of new intersection geometries and traffic configurations, influenced by increasing market entry rates (MERs) for CAVs (CAV-MERs), which were analyzed in a microsimulation environment. A suburban signalized intersection from the Polish road network was selected as a representative case study. Two alternative design hypotheses regarding the intersection’s geometric configurations were proposed. The Aimsun micro-simulator was used to hone the driving model parameters by calibrating the simulated data with reference capacity functions (RCFs) based on CAV factors derived from the Highway Capacity Manual 2022. Cross-referencing the conceptualized geometric design solutions, including a two-lane roundabout and an innovative knee-turbo roundabout, allowed the experimental results to demonstrate that CAV operation is influenced by the intersection layout and CAV-MERs. The research provides an overview of potential future traffic settings featuring CAVs and VHDs operating within various intersection designs. Additionally, the findings can support project proposals for the geometric and functional design of intersections by highlighting the potential benefits expected from smart driving.

Traffic microsimulation models use the movement of individual driver-vehicle-units (DVUs) and their interactions, which allows a detailed estimation of the traffic noise using Common Noise Assessment Methods (CNOSSOS). The Dynamic Traffic Noise Assessment (DTNA) methodology is applied to real traffic situations, then compared to on-field noise levels from measurement campaigns. This makes it possible to determine the influence of certain local traffic factors on the evaluation of noise. The pattern of distribution of vehicles along the avenue is related to the logic of traffic light control. The analysis of the inter-cycles noise variability during the simulation and measurement time shows no influence from local factors on the prediction of the dynamic traffic noise assessment tool based on CNOSSOS. A multifractal approach of acoustic waves propagation and the source behaviors in the traffic area are implemented. The novelty of the approach also comes from the multifractal model's freedom which allows the simulation, through the fractality degree, of various behaviors of the acoustic waves. The mathematical backbone of the model is developed on Cayley-Klein-type absolute geometries, implying harmonic mappings between the usual space and the Lobacevsky plane in a Poincare metric. The isomorphism of two groups of SL(2R) type showcases joint invariant functions that allow associations of pulsations-velocities manifolds type