ITS DataHub has partnered with the Federal Highway Administration's (FHWA's) Next Generation SIMulation (NGSIM) program to make available detailed vehicle trajectory data and supporting data files along with the raw and processed video files from the NGSIM data collection efforts. Researchers for the NGSIM program collected the specified data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, GA. This article provides a brief overview of the NGSIM program data collection as well as what types of data are available on ITS DataHub. Some examples of possible uses for the data and information on how to cite the various NGSIM datasets are also included.

Click “Export” on the right to download the vehicle trajectory data. The associated metadata and additional data can be downloaded below under "Attachments". Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset. For site-specific NGSIM video file datasets, please see the following: - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

As part of the Federal Highway Administration’s (FHWA) Next Generation Simulation (NGSIM) project, video data were collected on November 8th, 2006 on an arterial segment on Peachtree Street located in Atlanta, Georgia. The data represents 30 minutes total, segmented into two periods (12:45 p.m. to 1:00 p.m. and 4:00 p.m. to 4:15 p.m.). The dataset includes files for both raw and processed video data from each of the eight cameras for the two time periods available for download. Camera numbering is in order of southern-most (1) to northern-most (8). The raw video files give the original vehicle movement data and offer users a view of how the section was observed. The processed video files provide videos of the vehicles along with a superimposition of the vehicle identification numbers. These videos can be used alone or can be used for cross referencing of the textual vehicle trajectory data provided in the NGSIM trajectory data with the corresponding video.

For related datasets please see the following: - NGSIM Vehicle Trajectories and Supporting Data: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k

As part of the Federal Highway Administration’s (FHWA) Next Generation Simulation (NGSIM) project, video data was collected on a freeway segment of US 101 (Hollywood Freeway) located in Los Angeles, California on June 15th, 2005. A total of 45 minutes of transcribed data are included in this full data set, segmented into three 15 minute periods representing: 1) 7:50 a.m. to 8:05 a.m., 2) 8:05 a.m. to 8:20 a.m., and 3) 8:20 a.m. to 8:35 a.m. on June 15th, 2005. The dataset includes files for both raw and processed video data from each of the eight cameras for the three time periods available for download. Camera numbering is in order of southern-most (1) to northern-most (8). The raw video files give the original vehicle movement data and offer users a view of how the section was observed. The processed video files provide videos of the vehicles along with a superimposition of the vehicle identification numbers. These videos can be used alone or can be used for cross referencing of the textual vehicle trajectory data provided in the NGSIM trajectory data with the corresponding video.

For related datasets please see the following: - NGSIM Vehicle Trajectories and Supporting Data: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

As part of the Federal Highway Administration’s (FHWA) Next Generation Simulation (NGSIM) project, video data were collected on June 16th, 2005 on an arterial segment on Lankershim Boulevard located in Los Angeles, California. The data represents 30 minutes total, segmented into two periods (8:30 a.m. to 8:45 a.m. and 8:45 a.m. to 9:00 a.m.). The dataset includes files for both raw and processed video data from each of the five cameras for the two time periods available for download. Camera numbering is in order of southern-most (1) to northern-most (5). The raw videos give the original vehicle movement data and offer users a view of how the section was observed. The processed video files provide videos of the vehicles along with a superimposition of the vehicle identification numbers. These videos can be used alone or can be used for cross referencing of the textual vehicle trajectory data provided in the NGSIM trajectory data with the corresponding video.

For related datasets please see the following: - NGSIM Vehicle Trajectories and Supporting Data: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras.NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset.

As part of the Federal Highway Administration’s (FHWA) Next Generation Simulation (NGSIM) project, video data was collected on a segment of Interstate 80 located in Emeryville, California on April 13, 2005. A total of 45 minutes of video data are available, segmented into three 15 minute periods: 1) 4:00 p.m. to 4:15 p.m.; 2) 5:00 p.m. to 5:15 p.m.; and 3) 5:15 p.m. to 5:30 p.m. The dataset includes files for both raw and processed video data from each of the seven cameras for the three time periods available for download. Camera numbering is in order of southern-most (1) to northern-most (7). The raw videos give the original vehicle movement data and offer users a view of how the section was observed. The processed video files provide videos of the vehicles along with a superimposition of the vehicle identification numbers. These videos can be used alone or can be used for cross referencing of the textual vehicle trajectory data provided in the NGSIM trajectory data with the corresponding video.

For related datasets please see the following: - NGSIM Vehicle Trajectories and Supporting Data: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

This dataset contains the trajectories of all vehicles traveling on a section of the U.S. Highway 101 (Hollywood Freeway) in Los Angeles, CA from 7:50-8:05 AM on June 15, 2005 (a Wednesday). The trajectories were collected at a rate of 10 Hz. More details on the study section and method of collecting the trajectories are contained in the "data-analysis-report".

This data may be used to create models of driving behavior and is useful for studying phenomena such as traffic congestion and shockwaves. It includes both an on-ramp and an off-ramp as well.

Acknowledgements

Both the raw data and analysis report were provided by U.S. FHWA's Next Generation SIMulation (NGSIM) project.

Basic information of NIGSIM data.

nextGEMS is a collaborative European project. Funded by the EU’s Horizon 2020 programme, it will tap expertise from fourteen European Nations to develop two next generation (storm-resolving) Earth-system Models. Through breakthroughs in simulation realism, these models will allow us to understand and reliably quantify how the climate will change on a global and regional scale, and how the weather, including its extreme events, will look like in the future. See further details at https://nextgems-h2020.eu/ and https://cordis.europa.eu/project/id/101003470.

first-multi-decadal-numerical-closed-loop-simulations next-generation-gravity-mission-nggm recoverability-of-long-term-climate-variability-in-tws satellite-gravity-mission-simulations simulations-on-observing-techniques-of-trends-in-total-terrestrial-water-storage-tws

Sensitivity analysis results of node Typ.

This dataset contains outputs from the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) and accompanies the paper "Needham, J.F., Arellano, A., Davies, S.J., Fisher, R.A., Hammer, V., Knox, R., Mitre, D., Muller-Landau, H.C., Zuleta, D., Koven, C.D. Tree crown damage and its effects on forest carbon cycling in a tropical forest, 2022, Global Change Biology". Data are unprocessed netcdf file outputs from simulations that were run to test the effect of a new crown damage module in FATES. Specifically, this data package contains a sensitivity analysis to the carbon cushion parameter damage_Ccushion_ensemble_e1b5bd9_bf013ef_2021-09-02.h0.ensemble.sofar.nc, a sensitivity analysis to the root nitrogen stoichiometry parameter damage_Nstoich_ensemble_e1b5bd9_bf013ef_2021-09-02.h0.ensemble.sofar.nc, a sensitivity analysis to parameters controlling crown damage and recovery damage_recovery_ensemble_e1b5bd9_354f0b0_2021-09-02.h0.ensemble.sofar.nc, and a sensitivity analysis to the number of crown damage bins elm_fates_bci_*_damagebins.Eac53ccb80b-F8f994c29.2022-04-19.elm.h0.fullrun.nc. This data package also contains a high root nitrogen configuration of FATES, including both a control, and a crown damage simulation high_root_N_control_e1b5bd9_354f0b0_2021-09-02.clm2.h0.fullrun.nc and high_root_N_damage_e1b5bd9_354f0b0_2021-09-02.clm2.h0.fullrun.nc. There is an analogous low root nitrogen configuration of FATES, including a control, low_root_N_control_e1b5bd9_bf013ef_2021-09-02.clm2.h0.fullrun.nc a damage only simulation low_root_N_damageonly_e1b5bd9_bf013ef_2021-09-02.clm2.h0.fullrun.nc, a damage plus mortality simulation low_root_N_damage_mort_e1b5bd9_bf013ef_2021-09-02.clm2.h0.fullrun.nc, and a mortality only simulation low_root_N_mort_only_e1b5bd9_ef845c8_2021-09-02.clm2.h0.fullrun.nc. Finally, there is a two PFT simulation in which we test the effect of recovery on competitive dynamics, low_root_N_damage_two_pfts_stoichastic_e1b5bd9_bf013ef_2021-09-10.clm2.h0.fullrun.nc. These simulations test the effect of representing crown damage in FATES, compared with simulations that have an equivalent increase in mortality. Jupyter notebooks to analyse these files can be found at https://github.com/JessicaNeedham/Needham_etal_GCB_2022_FATES_crown_damage. This dataset was originally published on the NGEE Tropics Archive and is being mirrored on ESS-DIVE for long-term archival Acknowledgement: Funding for NGEE-Tropics data resources was provided by the U.S. Department of Energy Office of Science, Office of Biological and Environmental Research.

Quantum-Assisted Traffic Simulation Market Outlook

According to our latest research, the global Quantum-Assisted Traffic Simulation market size is valued at USD 412.5 million in 2024 and is expected to reach USD 3.12 billion by 2033, expanding at a robust CAGR of 24.8% over the forecast period. The rapid growth of this market is primarily driven by the increasing complexity of urban transportation networks and the urgent need for advanced simulation tools capable of handling massive datasets and delivering real-time, actionable insights. As cities and transportation systems become more interconnected and data-driven, quantum-assisted technologies are emerging as a transformative force in traffic simulation, enabling unprecedented levels of modeling accuracy and operational efficiency.

One of the primary growth factors propelling the Quantum-Assisted Traffic Simulation market is the escalating demand for smarter urban mobility solutions. Urbanization continues to accelerate, with more than 55% of the global population now residing in cities, leading to heightened congestion, pollution, and logistical challenges. Traditional simulation tools often struggle to process the vast, dynamic datasets generated by modern transportation networks. Quantum computing, with its ability to solve complex optimization problems exponentially faster than classical systems, is revolutionizing traffic simulation by enabling real-time scenario analysis, predictive modeling, and adaptive traffic control. This technological leap is empowering city planners, transportation authorities, and mobility service providers to optimize traffic flows, reduce congestion, and enhance commuter safety in ways previously unattainable.

Another critical driver is the growing integration of autonomous vehicles and connected transportation infrastructure. The proliferation of self-driving cars, intelligent traffic lights, and vehicle-to-everything (V2X) communication networks is creating an intricate web of interactions that demand sophisticated simulation environments. Quantum-assisted solutions can model these multidimensional systems with high fidelity, accounting for variables such as vehicle behavior, environmental factors, and human-machine interactions. This capability is essential for validating autonomous vehicle algorithms, stress-testing smart infrastructure, and ensuring the safety and reliability of next-generation mobility solutions. As a result, automotive companies, research institutes, and government agencies are increasingly investing in quantum-powered simulation platforms to accelerate the development and deployment of advanced transportation technologies.

Furthermore, the adoption of cloud-based deployment models and the rise of simulation-as-a-service offerings are making quantum-assisted traffic simulation more accessible to a broader range of stakeholders. Cloud infrastructure enables organizations to leverage quantum computing resources without the need for significant capital investment in specialized hardware. This democratization of access is fostering innovation across the transportation ecosystem, from small municipal agencies to large automotive manufacturers. Additionally, the integration of artificial intelligence and machine learning algorithms with quantum simulation engines is enhancing the accuracy and scalability of traffic models, enabling continuous improvement and adaptation to evolving urban landscapes. Such synergies are expected to further accelerate market growth throughout the forecast period.

Regionally, North America and Europe are leading the adoption of quantum-assisted traffic simulation technologies, driven by substantial investments in smart city initiatives, advanced transportation infrastructure, and research and development. The Asia Pacific region, however, is poised for the fastest growth, fueled by rapid urbanization, expanding metropolitan areas, and increasing government focus on sustainable mobility solutions. Latin America and the Middle East & Africa are also witnessing growing interest, particularly in metropolitan centers facing acute congestion and infrastructure challenges. As global cities strive to become more resilient, efficient, and sustainable, the Quantum-Assisted Traffic Simulation market is set to play a pivotal role in shaping the future of urban mobility.

Component Analysis

The Quantum-Assisted Traffic Simulation market is segmented by component into software, hardware, and services, ea

This dataset contains python scripts to analyse outputs from the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) and accompanies the paper “Needham, J.F., Chambers, J., Fisher, R., Knox, R., and Koven, C. D., Forest responses to simulated elevated CO2 under alternate hypotheses of size- and age-dependent mortality, 2020, Global Change Biology”. These scripts process FATES outputs from size- and age-dependent mortality simulations which were run to test the effect of different mechanisms of mortality on forest response to elevated CO2 (eCO2). Specifically, these scripts will process single plant functional type (PFT) simulations in which mortality is either a constant background rate, size-dependent or age-dependent. In each case, a simulation with constant woody NPP is compared to a simulation in which woody NPP increases by 25% to simulate the growth response of forests to eCO2. In addition, the data package contains scripts to analyse ensemble simulations with size- and age-dependent mortality and two PFTs, that were run to test the impact of different demographic rates on coexistence and the forest response to eCO2. Finally, the dataset includes scripts for processing simulations testing the sensitivity of results to allometry and to the recruitment scheme. This dataset was originally published on the NGEE Tropics Archive and is being mirrored on ESS-DIVE for long-term archival Acknowledgement: Funding for NGEE-Tropics data resources was provided by the U.S. Department of Energy Office of Science, Office of Biological and Environmental Research.

There is a lack of consensus on how next-generation sequence data should be considered for phylogenetic and phylogeographic estimates, with some studies excluding loci with missing data, while others include them, even when sequences are missing from a large number of individuals. Here we use simulations, focusing specifically on RAD sequences, to highlight some of the unforeseen consequence of excluding missing data from next-generation sequencing. Specifically, we show that in addition to the obvious effects associated with reducing the amount of data used to make historical inferences, the decisions we make about missing data (such as the minimum number of individuals with a sequence for a locus to be included in the study) also impact the types of loci sampled for a study. In particular, as the tolerance for missing data becomes more stringent, the mutational spectrum represented in the sampled loci becomes truncated such that loci with the highest mutation rates are disproportionately excluded. This effect is exacerbated further by factors involved in the preparation of the genomic library (i.e., the use of reduced representation libraries, as well as the coverage) and the taxonomic diversity represented in the library (i.e., the level of divergence among the individuals). We demonstrate that the intuitive appeals about being conservative by removing loci may be misguided.

Next Generation Data Management of Large-Scale CFD Simulations, Phase II

Driving Simulator Market Outlook

According to our latest research, the global driving simulator market size was valued at USD 2.21 billion in 2024, with a robust growth trajectory expected over the coming years. The market is projected to expand at a CAGR of 7.8% from 2025 to 2033, reaching an estimated value of USD 4.36 billion by 2033. This growth is primarily driven by the surging demand for advanced training solutions, technological advancements in simulation hardware and software, and the increasing adoption of virtual environments across automotive, aviation, and other transportation sectors. As per our latest research, the market is witnessing significant investments in R&D and a notable shift toward digital transformation in both developed and emerging economies.

The driving simulator market is experiencing a surge in demand due to the growing emphasis on safety and efficiency in vehicle operation across multiple industries. Automotive manufacturers and transportation authorities are increasingly relying on simulators to train drivers, test vehicle functionalities, and conduct research in a controlled, risk-free environment. The ability of driving simulators to replicate real-world scenarios with high fidelity allows for comprehensive driver training, which addresses critical issues like accident prevention and skill assessment. Additionally, the adoption of simulators is being fueled by stringent regulatory mandates for driver training in sectors such as aviation and commercial transportation, where errors can have severe consequences. These factors collectively contribute to the steady growth of the global driving simulator market.

Another significant growth factor for the driving simulator market is the rapid advancement in simulation technologies, including the integration of artificial intelligence, machine learning, and immersive virtual reality. Modern simulators are equipped with sophisticated hardware and software that can mimic complex driving conditions, vehicle dynamics, and environmental variables with remarkable accuracy. This technological evolution not only enhances the training experience but also enables manufacturers to conduct in-depth research and testing of new vehicle models and safety systems. The increasing focus on autonomous vehicle development and the need for extensive simulation-based validation further drive the adoption of advanced driving simulators. As a result, the market is witnessing heightened investments from both public and private sectors to develop and deploy next-generation simulation platforms.

The expansion of the driving simulator market is also supported by the growing trend of digitalization and the adoption of e-learning solutions in the education and corporate sectors. Simulators offer a cost-effective and scalable alternative to traditional on-road training, reducing the risks and expenses associated with real-world driving practice. The entertainment industry, too, is leveraging driving simulators to offer immersive gaming and virtual experiences to consumers, thereby expanding the market's addressable audience. Furthermore, the COVID-19 pandemic accelerated the shift toward remote training and assessment, highlighting the importance of simulation-based solutions in ensuring continuity and safety. These combined factors underscore the market's positive outlook and its potential for sustained growth in the coming years.

From a regional perspective, the Asia Pacific region is emerging as a key growth engine for the driving simulator market, driven by rapid urbanization, increasing vehicle ownership, and rising investments in transportation infrastructure. North America and Europe continue to dominate the market in terms of technological innovation and early adoption, with established automotive and aviation industries leading the way. The Middle East & Africa and Latin America are also witnessing gradual market penetration, supported by government initiatives to improve road safety and driver training standards. The interplay of these regional dynamics is expected to shape the competitive landscape and growth trajectory of the global driving simulator market through 2033.

Component Analysis

The driving simulator market is segmented by component into hardware, software, and services, each playing a pivotal role in the overall value chain. Hardware forms the backbone of any driving simulator, encompassing elements such as motion plat

Full Flight Simulator Market Outlook

According to our latest research, the Full Flight Simulator (FFS) market size reached USD 1.97 billion globally in 2024, driven by increasing demand for advanced pilot training solutions and stringent aviation safety regulations. The market is projected to grow at a robust CAGR of 6.2% from 2025 to 2033, reaching an estimated USD 3.37 billion by the end of the forecast period. Major growth factors include the rapid expansion of the commercial aviation sector, rising investments in pilot training infrastructure, and the adoption of next-generation simulation technologies across both civil and defense aviation domains.

One of the primary growth drivers for the Full Flight Simulator market is the sustained increase in global air traffic, which is compelling airlines and flight training centers to invest heavily in state-of-the-art pilot training equipment. The surge in commercial aviation, particularly in emerging economies across Asia Pacific and the Middle East, is creating significant demand for certified simulators that can deliver realistic and regulatory-compliant training experiences. Additionally, the ongoing pilot shortage crisis, exacerbated by the post-pandemic rebound in travel, has prompted airlines to accelerate their training programs, further fueling demand for advanced Full Flight Simulators. Governments and regulatory authorities are also mandating recurrent training and proficiency checks, which is boosting the replacement and upgrade cycles for simulator fleets.

Technological advancements are another key factor propelling the growth of the Full Flight Simulator market. The integration of high-fidelity visual systems, motion platforms, and artificial intelligence-driven analytics is transforming traditional flight simulators into immersive, data-rich training environments. Manufacturers are increasingly focusing on enhancing realism through 4K visual displays, advanced motion cueing systems, and cloud-based simulation management, which not only improve training outcomes but also reduce operational costs. The convergence of virtual reality (VR) and augmented reality (AR) with simulator platforms is opening new avenues for scenario-based training and remote instruction, thereby expanding the addressable market for both commercial and military applications.

The defense and military aviation sector is also playing a pivotal role in shaping the Full Flight Simulator market. Modernization programs across air forces worldwide are emphasizing simulation-based training to ensure operational readiness while minimizing risks and costs associated with live flight training. The adoption of Full Flight Simulators for mission rehearsal, tactical training, and combat scenario simulation is gaining traction, supported by substantial government funding and strategic partnerships between simulator providers and defense agencies. Furthermore, the growing prevalence of unmanned aerial vehicles (UAVs) and next-gen fighter jets necessitates specialized simulators, thereby broadening the marketÂ’s scope beyond traditional pilot training.

The evolution of Flight Simulator technology has been pivotal in enhancing pilot training programs. These simulators have advanced significantly from their early iterations, now incorporating cutting-edge technologies such as virtual reality and artificial intelligence. This evolution allows for a more immersive training experience, enabling pilots to practice complex maneuvers and emergency procedures in a controlled environment. The realism offered by modern Flight Simulators is crucial for preparing pilots to handle real-world scenarios with confidence and precision. As the aviation industry continues to grow, the role of Flight Simulators in ensuring pilot proficiency and safety becomes increasingly indispensable.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market for Full Flight Simulators, underpinned by rapid fleet expansion, government-led aviation initiatives, and the proliferation of low-cost carriers. North America remains the largest market, driven by robust investments in aviation infrastructure and the presence of leading simulator manufacturers. Europe continues to show steady growth, benefitting from stringent EASA regulations and a strong emphasis on aviation safety. Meanwhile, the Middle East

Driving style evaluation index.