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 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 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 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

The dataset includes three documents.

HDV_data_NGSIM_I_80.xlsx

The vehicle trajectory data from Next Generation SIMulation (NGSIM) dataset was collected on eastbound I-80 in the San Francisco Bay area, in Emeryville, CA, on April 13, 2005, from 4:03:56 pm to 4:08:56 pm. Including vehicle id, frame id, the total count of frames of each vehicle, global time, local position, global position, vehicle length, vehicle width, vehicle class, speed, acceleration, lane id, preceding vehicle id, following vehicle id, space headway, time headway, and time.

CAV_data_CARLA_SUMO.xml

The simulated CAV trajectory data with CARLA and SUMO, including vehicle id, position, angle, type, speed, lane id, and slope of each frame.

LTPP_data.csv

The table including 21 columns is calculated from the Long-Term Pavement Performance (LTPP) database.

• IRI The IRI value measured when age was 0. (m/km)
• Cr_Gator Area of alligator cracking in square meters. (m^2)
• Cr_Lwp Length of longitudinal cracks within the defined wheel paths in meters. (m)
• Cr_Lnwp Length of longitudinal cracks not in the defined wheel paths in meters. (m)
• Pt_A Area of patches in square meters. (m^2)
• Pt_N Number of patches in square meters. (m^2)
• Cr_Wp Length of wheelpath cracks in meters. (m)
• Cr_Gt183 Total length of transverse cracks greater than 1.83. (m)
• Rt The depth of rutting in millimeters. (mm)
• Fr Friction number between the vehicle wheel tire and the pavement
• IRI_0 The IRI value measured when age was 0. (m/km)
• Tk_Sb Layer thickness measurement for surface coarse and binder course. (in)
• Md_s Average backcalculated elastic modulus of the surface layer.(psi)
• Hydr Average measured hydraulic conductivity of the specimen. (cm/sec)
• Prcp Average monthly precipitation in millimeters. (mm)
• Fz Average freeze index. (℃/day)
• Esal Annual average ESAL (kESAL)
• Esal_q quadratic form of Kesal (kESAL^2)
• Age Time duration between new construction to roughness survey date. (year)
• Gr Mean specific gravity of asphalt cement
• Pt_Ca Coarse aggregate amount percent by total weight of aggregate in percentage. (%)

Basic information of NIGSIM data.

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

Principal component score coefficient matrix.

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

This dataset contains 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”. Data are unprocessed 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, this data package contains 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. Ensemble simulations with size- and age-dependent mortality and two PFTs test the impact of different demographic rates on coexistence and the forest response to eCO2. Finally, the dataset includes 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.

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

CPT of node Sty.

The simulation results are supplementary material to Schlaak et al. (in prep.) . The simulations are based on a modeled time series of global changes in soil moisture and snow coverage. The corresponding mass transport signal is obtained from future climate projections until the year 2100 following the shared socio-economic pathway scenario 5-8.5. For different mission concepts, including in-line single-pair missions and a Bender double-pair mission, the recoverability of a time variable mass signal is the time-variable signal is represented by the climate signal in terms of TWS. Here, the Geophysical Fluid Dynamics Laboratory (GFDL) model (H. Guo et al., 2018) is used as it has the best mean match compared with GRACE data and the Coupled Model Intercomparison Project Phase 6 (CMIP6) (L. Jensen et al., 2020)., considering realistic noise assumptions, simulated over several decades.

Commercial Aircraft Simulation Training Services Market Outlook

According to our latest research, the global commercial aircraft simulation training services market size reached USD 6.5 billion in 2024, driven by escalating demand for skilled pilots and more stringent aviation safety regulations. The market is projected to expand at a robust CAGR of 7.8% from 2025 to 2033, reaching a forecasted value of USD 12.8 billion by 2033. This growth is underpinned by the increasing adoption of advanced simulation technologies, rising air traffic, and the continuous need for pilot training and certification across the globe.

The primary growth factor for the commercial aircraft simulation training services market is the rapid expansion of the global aviation industry, particularly in emerging economies. As air travel becomes more accessible and affordable, airlines are witnessing a surge in passenger volumes, necessitating the recruitment and training of a larger pool of pilots. This has led to heightened demand for simulation-based training, which offers a safe, cost-effective, and highly realistic environment for pilot instruction. Moreover, regulatory bodies such as the International Civil Aviation Organization (ICAO) and the Federal Aviation Administration (FAA) have increased their focus on safety compliance, mandating recurrent and type-specific simulator training for pilots. These developments are fueling investments in state-of-the-art simulation centers and technologies, further propelling market growth.

Technological advancements are another significant driver shaping the commercial aircraft simulation training services market. The integration of virtual reality (VR), augmented reality (AR), and artificial intelligence (AI) into training modules has revolutionized the way pilots are trained, offering immersive and highly interactive experiences. Full flight simulators (FFS) and fixed base simulators now deliver near-real-life scenarios, enabling pilots to practice complex maneuvers, emergency procedures, and multi-crew coordination in a controlled setting. These innovations not only enhance training effectiveness but also reduce operational costs and downtime associated with traditional in-flight training. As airlines and training organizations strive to optimize resource allocation and improve training outcomes, the adoption of next-generation simulation technologies is expected to accelerate.

Additionally, the market is benefiting from the growing emphasis on sustainability and cost-efficiency within the aviation sector. Simulator-based training significantly reduces the need for fuel consumption and carbon emissions associated with live flight training, aligning with the industry's broader environmental objectives. Airlines are increasingly leveraging simulation training to minimize operational risks, ensure regulatory compliance, and maintain high safety standards without incurring excessive costs. This trend is particularly pronounced among low-cost carriers and regional airlines, which face intense competitive pressures and seek to optimize their training investments. As a result, the commercial aircraft simulation training services market is poised for sustained growth over the forecast period.

From a regional perspective, North America continues to dominate the commercial aircraft simulation training services market, accounting for the largest share in 2024. This leadership is attributed to the presence of major airlines, established training organizations, and advanced simulator manufacturers in the region. Asia Pacific, however, is emerging as the fastest-growing market, fueled by rapid fleet expansion, rising air travel demand, and significant investments in aviation infrastructure. Europe also remains a key market, driven by stringent regulatory requirements and a strong focus on pilot proficiency and safety. The Middle East & Africa and Latin America are witnessing steady growth, supported by increasing air connectivity and government initiatives to develop local aviation talent. Collectively, these regional dynamics are shaping the global landscape of commercial aircraft simulation training services.

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 disproportionat...

Leaf nodes of the tree structure [48].