The Human Factors Program is housed within the Center
for Transportation Safety at the Texas A&M Transportation
Institute (TTI). The goal of the program is to conduct basic and
applied research to measure driver performance and behavior
for varied driving situations, vehicle characteristics and roadway
environments. Researchers design and implement experiments with human subjects (including field and simulator studies) and survey subjects to identify driver safety issues, such as those related to traffic control devices, distraction and fatigue.
TTI’s experimental psychologists and industrial engineers have
conducted numerous studies related to driver response to
roadway geometric design; visibility and driver comprehension
of traffic control devices; driver distraction; and automotive
adaptive equipment for disabled drivers, older drivers and
short-statured drivers.
The Center for Transportation Computational Mechanics
at the Texas A&M Transportation Institute (TTI) is a university-based center established by the Federal Highway Administration (FHWA). Due to the significant costs associated
with crash testing, engineers are relying more on sophisticated
analytical models and non-linear finite element codes to evaluate, design and analyze roadside safety features. Although the Center’s primary focus is roadside safety, its
activities are not limited to this area. Researchers at the Center actively work on projects in other areas, such as design and evaluation of physical perimeter security devices and dynamic analysis of bridge structures, soils and pavements.
The TTI Center for Transportation Safety is home to a Realtime Technologies, Inc. (RTI) driving simulator that provides measurements of drivers’ responses to roadway situations, in-vehicle technologies, and driving-related tasks. RTI’s
SimCreator® and SimVista® software tools provide a library of different roadway cross-sections and interchanges, as well as a variety of roadway objects, buildings, and ambient traffic. In addition, custom roadway tiles can be programmed to match a specific roadway segment. This allows for in-house development of a wide range of rural and urban roadway scenarios, making it possible to inexpensively test multiple variations and placements of roadway devices or in-vehicle
signals and displays. Using the driving simulator, researchers can test a wider variety of roadway geometries and traffic conditions than are typically possible in a test-track study or fiscally practical in a field study.
Researchers design and implement experiments with human subjects (including field and simulator studies) and survey subjects to identify driver safety issues, such as those related to traffic control devices, distraction and fatigue. TTI’s experimental psychologists and industrial engineers have conducted numerous studies related to driver response to roadway geometric design; visibility and driver comprehension of traffic control devices; driver distraction; and automotive adaptive equipment for disabled drivers, older drivers and short-statured drivers.
TTI’s Connected and Automated Vision for the Future
The Texas A&M Transportation Institute (TTI) shares an industry vision where no vehicles collide and people can use connected and automated transportation to transform how they live, work and interact with their environment. To achieve this vision, research, development and testing are needed on how vehicles, users and transportation infrastructure all work together. While automated vehicles are emerging and connected vehicle research is progressing, TTI believes the most significant gains in safety and mobility will occur at the nexus of these areas. TTI is creating a world-class research environment on the Texas A&M University campus where researchers can collaborate, new transportation paradigms can be created, and future mobility and safety can be showcased.
RSITS: Road safety Intelligent Transport System in Deep Federated Learning As...Abdullah Raza
In smart cities, the frequency of traffic collisions has been rising daily.
More than half of all traffic-related deaths and injuries happen to people most vulnerable on the road. According to the World Health Organization report published
in 20182022, 1.3 million people died due to accidents on the road due to vehicles, and
30 to 50 million were injured. To address this issue, we present the road safety intelligent transport system (RSITS) based on deep federated learning-assisted fog cloud
networks. RSITS offers mobile LiDAR sensors and vehicle LiDAR sensors enabled
applications to alert road safety mechanisms. To deal with the complex features of
road safety, we trained the large pedestrian and vehicle detection dataset on different
road safety fog servers and aggregated them on the centralized cloud. To ensure that
constraints such as safety, the accuracy of alarms, response times, security, and deadlines are met, we present a deep federated learning scheduling scheme (DFLSS) that
consists of different components: Initially, we bound all applications so that emergency tasks, such as moving an object within 5 meters, should be processed locally
with the minimum response time. Due to resource constraints and the limitations of
devices, other tasks of applications are offloaded to the centralized cloud for processing. To ensure security, each computing node must encrypt and decrypt data before
offloading and processing it in DFLSS. Simulation results show that the proposed
DFLSS outperformed all existing approaches regarding accuracy, response time, and
deadline for road safety applications.
The Center for Transportation Computational Mechanics
at the Texas A&M Transportation Institute (TTI) is a university-based center established by the Federal Highway Administration (FHWA). Due to the significant costs associated
with crash testing, engineers are relying more on sophisticated
analytical models and non-linear finite element codes to evaluate, design and analyze roadside safety features. Although the Center’s primary focus is roadside safety, its
activities are not limited to this area. Researchers at the Center actively work on projects in other areas, such as design and evaluation of physical perimeter security devices and dynamic analysis of bridge structures, soils and pavements.
The TTI Center for Transportation Safety is home to a Realtime Technologies, Inc. (RTI) driving simulator that provides measurements of drivers’ responses to roadway situations, in-vehicle technologies, and driving-related tasks. RTI’s
SimCreator® and SimVista® software tools provide a library of different roadway cross-sections and interchanges, as well as a variety of roadway objects, buildings, and ambient traffic. In addition, custom roadway tiles can be programmed to match a specific roadway segment. This allows for in-house development of a wide range of rural and urban roadway scenarios, making it possible to inexpensively test multiple variations and placements of roadway devices or in-vehicle
signals and displays. Using the driving simulator, researchers can test a wider variety of roadway geometries and traffic conditions than are typically possible in a test-track study or fiscally practical in a field study.
Researchers design and implement experiments with human subjects (including field and simulator studies) and survey subjects to identify driver safety issues, such as those related to traffic control devices, distraction and fatigue. TTI’s experimental psychologists and industrial engineers have conducted numerous studies related to driver response to roadway geometric design; visibility and driver comprehension of traffic control devices; driver distraction; and automotive adaptive equipment for disabled drivers, older drivers and short-statured drivers.
TTI’s Connected and Automated Vision for the Future
The Texas A&M Transportation Institute (TTI) shares an industry vision where no vehicles collide and people can use connected and automated transportation to transform how they live, work and interact with their environment. To achieve this vision, research, development and testing are needed on how vehicles, users and transportation infrastructure all work together. While automated vehicles are emerging and connected vehicle research is progressing, TTI believes the most significant gains in safety and mobility will occur at the nexus of these areas. TTI is creating a world-class research environment on the Texas A&M University campus where researchers can collaborate, new transportation paradigms can be created, and future mobility and safety can be showcased.
RSITS: Road safety Intelligent Transport System in Deep Federated Learning As...Abdullah Raza
In smart cities, the frequency of traffic collisions has been rising daily.
More than half of all traffic-related deaths and injuries happen to people most vulnerable on the road. According to the World Health Organization report published
in 20182022, 1.3 million people died due to accidents on the road due to vehicles, and
30 to 50 million were injured. To address this issue, we present the road safety intelligent transport system (RSITS) based on deep federated learning-assisted fog cloud
networks. RSITS offers mobile LiDAR sensors and vehicle LiDAR sensors enabled
applications to alert road safety mechanisms. To deal with the complex features of
road safety, we trained the large pedestrian and vehicle detection dataset on different
road safety fog servers and aggregated them on the centralized cloud. To ensure that
constraints such as safety, the accuracy of alarms, response times, security, and deadlines are met, we present a deep federated learning scheduling scheme (DFLSS) that
consists of different components: Initially, we bound all applications so that emergency tasks, such as moving an object within 5 meters, should be processed locally
with the minimum response time. Due to resource constraints and the limitations of
devices, other tasks of applications are offloaded to the centralized cloud for processing. To ensure security, each computing node must encrypt and decrypt data before
offloading and processing it in DFLSS. Simulation results show that the proposed
DFLSS outperformed all existing approaches regarding accuracy, response time, and
deadline for road safety applications.
www.uolds.leeds.ac.uk
The University of Leeds Driving Simulator (UoLDS) continues to be one of the most technically advanced driving simulators in use within a research environment in the world today, exploiting leading-edge motion base technology to create
a high fidelity and dynamic simulated driving environment.
The simulator is developed and managed by a multidisciplinary group of academics from the Safety and Technology group at The Institute for Transport Studies. Using funding from UK and European government grants and private organisations, the group studies the interaction of drivers with new technologies, typically before they are fully implemented on roads and in the vehicle.
The team has over 20 years’ experience developing cutting-edge, innovative
scenarios suited to the needs of its funders. Realistic and repeatable scenarios allow studies on driver behaviour to be conducted in a safe and controllable environment,
substantially reducing the costs associated with the development of real systems, infrastructures or prototypes.
Results from studies conducted on UoLDS have had substantial influence on National and International policy.
For example, research on the simulator
has shaped the understanding of how driver distraction affects road safety, providing guidelines for the implementation of speed advisory systems.
Review of Environment Perception for Intelligent VehiclesDr. Amarjeet Singh
Overview of environment perception for intelligent
vehicles supposes to the state-of-the-art algorithms and
modeling methods are given, with a summary of their pros
and cons. A special attention is paid to methods for lane and
road detection, traffic sign recognition, vehicle tracking,
behavior analysis, and scene understanding. Integrated lane
and vehicle tracking for driver assistance system that
improves on the performance of both lane tracking and
vehicle tracking modules. Without specific hardware and
software optimizations, the fully implemented system runs at
near-real-time speeds of 11 frames per second. On-road
vision-based vehicle detection, tracking, and behavior
understanding. Vision based vehicle detection in the context of
sensor-based on-road surround analysis. We detail advances
in vehicle detection, discussing monocular, stereo vision, and
active sensor–vision fusion for on-road vehicle detection. The
traffic sign detection detailing detection systems for traffic
sign recognition (TSR) for driver assistance. Inherently in
traffic sign detection to the various stages: segmentation,
feature extraction, and final sign detection.
The vision for the Smart Roadside Initiative is to create an environment where commercial vehicles, motor carriers, enforcement resources, highway facilities, intermodal facilities, toll facilities, and other nodes on the transportation system collect data for their own purposes and share the data seamlessly with the relevant parties, in order to improve motor carrier safety, security, operational efficiency, and freight mobility.
Horizon Europe Clean Transport Webinar - Cluster 5 Destination 5 | PitchesKTN
This webinar co-organised by KTN Global Alliance in partnership with the Foreign, Commonwealth and Development Office (FCDO) in Germany, UK Science and Innovation Network and UK National Contact Points (NCPs) from Innovate UK as well as European NCPs focussed on pitching of project ideas and brokering partnerships for European Research and Innovation collaborations and networking.
TTI Research Scientist, Katie Turnbull, presented on this active research project at the 2016 Smart Transport Symposium in Austin, Texas. Learn more by visiting the TxDOT project page: http://library.ctr.utexas.edu/Presto/content/Detail.aspx?q=MC02ODc1&ctID=M2UxNzg5YmEtYzMyZS00ZjBlLWIyODctYzljMzQ3ZmVmOWFl&rID=MzYy&qcf=&ph=VHJ1ZQ==&bckToL=VHJ1ZQ==&
Every person in this world is concerned about being safe. Increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. In driving assistance systems, obstacle detection especially for moving object detection is a key component of collision avoidance and also detect signs boards. The most frequently used principal approach to detect road boundaries and lanes using vision system on the vehicle. In proposed system, we can implement of lane Object detection and also detect Signs board with the help of AI and Deep learning Technique. Input as Dataset to trained using CNN algorithm and creat beast model. Using these model to classify or detect output. Harshada Annasaheb Nikam | Pranav Ashokrao Survase | Ketan Patole | Mansi Sanjay Gore "Lane Detection and Object Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-3 , June 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd57393.pdf Paper URL: https://www.ijtsrd.com.com/engineering/information-technology/57393/lane-detection-and-object-detection/harshada-annasaheb-nikam
Vehicle Speed Estimation using Haar Classifier Algorithmijtsrd
An efficient traffic management system is needed in all kinds of roads, such as off roads, highways, etc... Though several laws and speed controller has been attached to the vehicles, Speed limit may vary from road to road. Still Traffic management system faces different kinds of challenges everyday and its being a research area though number of proposals has been identified. Many numbers of methods has been proposed in computer Vision and machine learning approaches for object tracking. In this paper vehicles are identified and detected using a videos that taken from surveillance camera. The objective of the present work is to identification of the vehicles is done by using Computer vision technique and detection of vehicles using Haar cascade classifier. Detecting the vehicles using machine learning and estimating speed is tough but beneficial task. For the past few tears Convolution Neural Network CNN has been widely used in computer vision for vehicle detection and identification. This method manages to track multiple objects at real time using dlibs. P. Devi Mahalakshmi | Dr. M. Babu "Vehicle Speed Estimation using Haar Classifier Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29482.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/29482/vehicle-speed-estimation-using-haar-classifier-algorithm/p-devi-mahalakshmi
Automated License Plate detection and Speed estimation of Vehicle Using Machi...ijtsrd
A well ordered traffic management system is required in all types of roads, such as off roads, highways, etc. There has been several laws and speed controlled measures are taken in all places with different perspectives. Also Speed limit may vary from road to road. So there are number of methods has been proposed using computer Vision and machine learning algorithms for object tracking. Here vehicles are recognized and detected from the videos that taken using surveillance camera. The aim is to identification of the vehicles and tracking using Haar Classifier, then determine the speed of the vehicle and Finally Detecting the License plate of the vehicle. Detecting the License plate and vehicle speed using machine learning is tough but beneficial task. For the past few years Convolution Neural Network CNN has been widely used in computer vision for vehicle detection and identification. Dlibs are used to track the multiple objects at the same time. P. Devi Mahalakshmi | Dr. M. Babu "Automated License Plate detection and Speed estimation of Vehicle Using Machine Learning - Haar Classifier Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33395.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/33395/automated-license-plate-detection-and-speed-estimation-of-vehicle-using-machine-learning--haar-classifier-algorithm/p-devi-mahalakshmi
A presentation of Driver drowsiness alert system which can identify whether the driver is attentive or sleepy while driving and hence alert them by a beep when the driver is sleepy.Python and open CV are main technologies used here along with hass cascade algorithm for the same.
Texas Pedestrian Safety Forum, July 12, 2018
When Your Urban Core Arrives | University Drive in College Station Presented by James Robertson, Ph.D., P.E., Lee Engineering
www.uolds.leeds.ac.uk
The University of Leeds Driving Simulator (UoLDS) continues to be one of the most technically advanced driving simulators in use within a research environment in the world today, exploiting leading-edge motion base technology to create
a high fidelity and dynamic simulated driving environment.
The simulator is developed and managed by a multidisciplinary group of academics from the Safety and Technology group at The Institute for Transport Studies. Using funding from UK and European government grants and private organisations, the group studies the interaction of drivers with new technologies, typically before they are fully implemented on roads and in the vehicle.
The team has over 20 years’ experience developing cutting-edge, innovative
scenarios suited to the needs of its funders. Realistic and repeatable scenarios allow studies on driver behaviour to be conducted in a safe and controllable environment,
substantially reducing the costs associated with the development of real systems, infrastructures or prototypes.
Results from studies conducted on UoLDS have had substantial influence on National and International policy.
For example, research on the simulator
has shaped the understanding of how driver distraction affects road safety, providing guidelines for the implementation of speed advisory systems.
Review of Environment Perception for Intelligent VehiclesDr. Amarjeet Singh
Overview of environment perception for intelligent
vehicles supposes to the state-of-the-art algorithms and
modeling methods are given, with a summary of their pros
and cons. A special attention is paid to methods for lane and
road detection, traffic sign recognition, vehicle tracking,
behavior analysis, and scene understanding. Integrated lane
and vehicle tracking for driver assistance system that
improves on the performance of both lane tracking and
vehicle tracking modules. Without specific hardware and
software optimizations, the fully implemented system runs at
near-real-time speeds of 11 frames per second. On-road
vision-based vehicle detection, tracking, and behavior
understanding. Vision based vehicle detection in the context of
sensor-based on-road surround analysis. We detail advances
in vehicle detection, discussing monocular, stereo vision, and
active sensor–vision fusion for on-road vehicle detection. The
traffic sign detection detailing detection systems for traffic
sign recognition (TSR) for driver assistance. Inherently in
traffic sign detection to the various stages: segmentation,
feature extraction, and final sign detection.
The vision for the Smart Roadside Initiative is to create an environment where commercial vehicles, motor carriers, enforcement resources, highway facilities, intermodal facilities, toll facilities, and other nodes on the transportation system collect data for their own purposes and share the data seamlessly with the relevant parties, in order to improve motor carrier safety, security, operational efficiency, and freight mobility.
Horizon Europe Clean Transport Webinar - Cluster 5 Destination 5 | PitchesKTN
This webinar co-organised by KTN Global Alliance in partnership with the Foreign, Commonwealth and Development Office (FCDO) in Germany, UK Science and Innovation Network and UK National Contact Points (NCPs) from Innovate UK as well as European NCPs focussed on pitching of project ideas and brokering partnerships for European Research and Innovation collaborations and networking.
TTI Research Scientist, Katie Turnbull, presented on this active research project at the 2016 Smart Transport Symposium in Austin, Texas. Learn more by visiting the TxDOT project page: http://library.ctr.utexas.edu/Presto/content/Detail.aspx?q=MC02ODc1&ctID=M2UxNzg5YmEtYzMyZS00ZjBlLWIyODctYzljMzQ3ZmVmOWFl&rID=MzYy&qcf=&ph=VHJ1ZQ==&bckToL=VHJ1ZQ==&
Every person in this world is concerned about being safe. Increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. In driving assistance systems, obstacle detection especially for moving object detection is a key component of collision avoidance and also detect signs boards. The most frequently used principal approach to detect road boundaries and lanes using vision system on the vehicle. In proposed system, we can implement of lane Object detection and also detect Signs board with the help of AI and Deep learning Technique. Input as Dataset to trained using CNN algorithm and creat beast model. Using these model to classify or detect output. Harshada Annasaheb Nikam | Pranav Ashokrao Survase | Ketan Patole | Mansi Sanjay Gore "Lane Detection and Object Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-3 , June 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd57393.pdf Paper URL: https://www.ijtsrd.com.com/engineering/information-technology/57393/lane-detection-and-object-detection/harshada-annasaheb-nikam
Vehicle Speed Estimation using Haar Classifier Algorithmijtsrd
An efficient traffic management system is needed in all kinds of roads, such as off roads, highways, etc... Though several laws and speed controller has been attached to the vehicles, Speed limit may vary from road to road. Still Traffic management system faces different kinds of challenges everyday and its being a research area though number of proposals has been identified. Many numbers of methods has been proposed in computer Vision and machine learning approaches for object tracking. In this paper vehicles are identified and detected using a videos that taken from surveillance camera. The objective of the present work is to identification of the vehicles is done by using Computer vision technique and detection of vehicles using Haar cascade classifier. Detecting the vehicles using machine learning and estimating speed is tough but beneficial task. For the past few tears Convolution Neural Network CNN has been widely used in computer vision for vehicle detection and identification. This method manages to track multiple objects at real time using dlibs. P. Devi Mahalakshmi | Dr. M. Babu "Vehicle Speed Estimation using Haar Classifier Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29482.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/29482/vehicle-speed-estimation-using-haar-classifier-algorithm/p-devi-mahalakshmi
Automated License Plate detection and Speed estimation of Vehicle Using Machi...ijtsrd
A well ordered traffic management system is required in all types of roads, such as off roads, highways, etc. There has been several laws and speed controlled measures are taken in all places with different perspectives. Also Speed limit may vary from road to road. So there are number of methods has been proposed using computer Vision and machine learning algorithms for object tracking. Here vehicles are recognized and detected from the videos that taken using surveillance camera. The aim is to identification of the vehicles and tracking using Haar Classifier, then determine the speed of the vehicle and Finally Detecting the License plate of the vehicle. Detecting the License plate and vehicle speed using machine learning is tough but beneficial task. For the past few years Convolution Neural Network CNN has been widely used in computer vision for vehicle detection and identification. Dlibs are used to track the multiple objects at the same time. P. Devi Mahalakshmi | Dr. M. Babu "Automated License Plate detection and Speed estimation of Vehicle Using Machine Learning - Haar Classifier Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33395.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/33395/automated-license-plate-detection-and-speed-estimation-of-vehicle-using-machine-learning--haar-classifier-algorithm/p-devi-mahalakshmi
A presentation of Driver drowsiness alert system which can identify whether the driver is attentive or sleepy while driving and hence alert them by a beep when the driver is sleepy.Python and open CV are main technologies used here along with hass cascade algorithm for the same.
Texas Pedestrian Safety Forum, July 12, 2018
When Your Urban Core Arrives | University Drive in College Station Presented by James Robertson, Ph.D., P.E., Lee Engineering
Texas Pedestrian Safety Forum, July 12, 2018
Presentation by Kevin Kokes, Principal Transportation Planner, North Central Texas Council of Governments (NCTCOG)
In 2009, the Texas A&M Transportation Institute (TTI) added a one-of-a-kind Visibility Research Laboratory to its collection
of world class research facilities. The laboratory is located in the Institute’s State Headquarters and Research Building in the Research Park at Texas A&M University in College Station, Texas. The laboratory features a 125-foot-long corridor that is used to test retroreflective materials and coatings, lights and other technologies designed to provide nighttime visibility for
highway drivers.
What is Truck Platooning?
Level 2 truck platooning extends radar and vehicle-to-vehicle, communications-based, cooperative-adaptive cruise control using precise automated lateral and longitudinal vehicle control to maintain a tight formation of vehicles with short following distances. A manually driven truck leads a platoon, allowing the driver(s) of the following truck(s) to disengage from driving tasks and monitor system performance. Level 1 truck platooning has demonstrated the potential for significant fuel savings, enhanced mobility and associated emissions reductions from platooning vehicles. Level 2 automation may increase these benefits while reducing driver workload and increasing safety.
The Transportation Revenue Estimator and Needs Determination System (TRENDS) model funded by the Texas Department of Transportation is designed to provide transportation planners, policy makers and the public with a tool to forecast transportation revenues and expenses based on a user-defined level of investment at both the state and local
level. The user, through interactive windows, can control a number of variables related to assumptions regarding statewide transportation needs, population growth rates, fuel efficiency,
federal reimbursement rates, inflation rates, taxes, fees and other elements. The output is a set of tables and graphs showing a forecast of revenues, expenditures and fund balances for each year of the analysis period based on the
user-defined assumptions. The TRENDS model also includes a local option sub-model for each of Texas’ 25 Metropolitan Planning Organizations. Through the local option model the user can analyze changes in local revenues by creating
or adjusting a local fuel tax, local vehicle miles traveled tax, local vehicle registration fee or the local fuel efficiency rates.
The Travel Forecasting Program at the Texas A&M Transportation Institute (TTI) supports and assists public agencies in the development, implementation and application of
current and emerging technologies in travel demand forecasting.
The purpose of travel forecasting is to help transportation
decision makers, at the local and state levels, improve the overall function of the transportation system. Program staff members accomplish this by developing travel models that predict future transportation patterns based on many variables. The variables used by program staff include comprehensive travel survey data, U.S. Census data, current and projected socio-demographic data, existing and projected transportation system data, and current traffic data.
The Texas A&M Transportation Institute (TTI) Transportation Planning Program conducts research on travel surveys, travel behavior and related data collection methods to support travel models, policy, and air quality analyses. Program researchers have expertise in travel data collection methods and technologies; survey design and sampling, data analysis and interpretation; demographic data preparation for modeling; and corridor management and preservation.
The Texas A&M Transportation Institute (TTI) Transit
Mobility Program provides research and technology transfer expertise in all aspects of public transportation planning, management and operations. Program researchers bring a combination of direct operational skills in all bus and rail modes and nationwide research experience with metropolitan, urban and rural transit systems. Research projects result in practical, actionable recommendations for enhancing transit access, efficiency, effectiveness, safety and funding sustainability. Transit Mobility Program staff are adept at facilitating multi-agency groups in the development of shared transportation objectives, innovative strategies and coordinated services.
The Texas A&M Transportation Institute’s (TTI) Sediment and
Erosion Control Laboratory (SEC Lab) provides the transportation industry with a research and performance
evaluation program for roadside environmental management. Research at the SEC Lab includes stormwater quality improvement, erosion and sediment control, and vegetation
establishment and management.
The Texas A&M University System is creating a new paradigm for the future of applied research, technology development and education. The 2,000 acre RELLIS Campus is conveniently located just 8 miles/15 minutes from Texas A&M University’s main campus. This location has long been a place where Texas A&M has conducted world-class research, technology development and workforce training in areas such as vehicle safety, traffic engineering, law enforcement training, biological materials processing, robotics and unmanned aerial systems.
Freight and passenger rail is a critical component of our nation’s
transportation system. Texas A&M Transportation Institute’s
(TTI) Multimodal Freight Transportation Programs Group
remains active in exploring the future of rail through a variety
of research activities.
Public scrutiny and agency accountability are at an all-time
high. Agencies are looking for a better understanding of the issues that are important to their customers. In an era of strained financial resources, it is necessary to order priorities that are important to the people that support the transportation system through taxes and fees. The Public Engagement Planning (PEP) program at the Texas A&M Transportation
Institute (TTI) provides research innovations and coordinated support to sponsors in the areas of public engagement planning and public opinion research.
The Texas A&M Transportation Institute (TTI) was asked by the Texas Department of Transportation (TxDOT) to assist in the application and refinement of prior research to accomplish some key goals during the reconstruction of the I-35 corridor from Hillsboro to Salado (90 miles total). Currently, TxDOT is conducting 10 construction projects along this corridor. More than 30 million drivers, including travelers, shippers and intercity commuters, use the corridor each year.
Intelligent transportation systems (ITS) include a broad range of services and technology solutions that provide and manage information to improve the safety, efficiency and performance of our transportation network.
For more than three decades, the Texas A&M Transportation
Institute (TTI) has been actively involved in the development
and improvement of the Texas Airport System. TTI’s contributions include activities related to planning and programming of airport projects, airport maintenance, and aviation education. TTI researchers have provided valuable guidance on a variety of issues to the Aviation Division at the Texas Department of Transportation (TxDOT) and to small and large airports across the state, including the Dallas-Fort Worth International Airport, Houston’s George Bush Intercontinental Airport and small airports such as Bryan’s Coulter Field.
The Texas A&M Transportation Institute is a leader in multimodal freight research and an innovator in exploring new ways of moving freight across the nation and around
the world.
TTI researchers have expertise in areas such as engineering, planning, economics, policy, public engagement, landscape architecture, environmental sciences, computer science and the social sciences, TTI researchers serve as objective transportation experts. They provide a resource to local, state and national agencies and groups, helping them solve transportation challenges and make informed decisions.
The Texas A&M Transportation Institute (TTI) opened the Environmental and Emissions Research Facility in Bryan, Texas, in January 2010. The development of the facility resulted from competitive grant awards to TTI from the U.S. Environmental Protection Agency (EPA) and the Houston Advanced Research Center (HARC), with additional funding provided by The Texas A&M University System and TTI. The Air Quality Program uses the facility as a part of emissions and fuel efficiency research. The $2.5 million facility is one of the largest drive-in environmental chambers in the country, and the only one based at a university. The EERF can be used to conduct
tests on vehicles as large as a full tractor-trailer or bus.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
1. Saving Lives, Time and Resources tti.tamu.edu
The Human Factors Program is housed within the Center
for Transportation Safety at the Texas A&M Transportation
Institute (TTI). The goal of the program is to conduct basic and
applied research to measure driver performance and behavior
forvarieddrivingsituations,vehiclecharacteristicsandroadway
environments.
Researchers design and implement experiments with human
subjects (including field and simulator studies) and survey
subjects to identify driver safety issues, such as those related
to traffic control devices, distraction and fatigue.
TTI’sexperimentalpsychologistsandindustrialengineershave
conducted numerous studies related to driver response to
roadwaygeometricdesign;visibilityanddrivercomprehension
of traffic control devices; driver distraction; and automotive
adaptive equipment for disabled drivers, older drivers and
short-statured drivers.
Research Areas
• Driver distraction
• Human interaction with in-vehicle systems
• Traffic sign and roadway design (in terms of being
visible, understandable and presentable to drivers)
• Adaptive equipment
• Pavement marking design (in terms of being visible,
understandable and presentable to drivers)
Measurement Tools and Facilities
Driving Studies
• Test tracks: Human factors and safety studies are conducted at the
Proving Grounds Research Facility at the Texas A&M University
Riverside Campus and at the Pecos Research and Testing Facility.
Each closed-course facility is ideal for experimental research and
testing in the areas of vehicle performance and handling, visibility,
distracted driving and driver training.
• Instrumented vehicle: A 2006 Toyota Highlander serves as TTI’s
on-roadresearchvehicle.Theprincipalsystemwithintheinstrumented
vehicle is the Dewetron DEWE5000 data-acquisition integration
system. Essentially a large portable computer, the DEWE5000 serves
as the data-acquisition device for all the peripheral systems in the
vehicle.
• Eyetracker:TheHumanFactorsGroupishometotwodifferenttypes
of eye-tracking devices, the faceLAB® eye-tracking system and the
ViewPointEyeTracker®.Inbothsystems,camerasdetectthereflection
of infrared light from the irises of the eyes and the contrasting lack of
reflection at the pupil. Software allows researchers to mathematically
map the pupil’s location to determine the subject’s point of gaze.
• Mobile surveillance equipment: TTI has a mobile surveillance
system that is a portable version of the Dewetron DEWE5000 data-
acquisition computer with hand light detection and ranging (Lidar)
guns for speed measurement. This portable traffic surveillance system
uses two hidden radar systems that can be repositioned in the probe
vehicle to measure ambient traffic speed and position.
Surveys and Focus Groups
• SuperLab® survey software: Surveys are displayed using
SuperLab® survey software that can be tailored to the needs
of the project. Using the software, researchers are able to
run surveys in remote locations. Surveys can be timed,
randomized or broken into sections to ensure that they are
comprehensive and objective.
• Visibility Research Laboratory: The Visibility Research
Laboratory is used for nighttime surveys that can be con-
ductedduringdaytimehours.Thelabprovidesanidealloca-
tion to simulate a consistent nighttime environment during
all times of day and weather conditions, and can be used to
test sign visibility, comprehension and retroreflectivity for
readability.
• Focusgroups:Focusgroupsareausefulinterviewingtoolto
gain driver insight and form a foundation for study criteria.
Typically, groups of eight to 15 individuals are led by a mod-
erator and asked to provide their opinions and experiences
relating to specific topics or roadway equipment.
• Otherequipment:In-vehicle equipment such as reconfigu-
rable touch-screen displays, peripheral detection lights and
response joysticks simulate tasks collaboratively to create
tests to measure anything from driver response times to
driver awareness tasks. This equipment can also be used in
the simulator.
HUMAN FACTORS PROGRAM
2. Michael Manser
Senior Research Scientist
Center for Transportation Safety
Texas A&M Transportation Institute
College Station, TX 77843-3135
(979) 845-1605
m-manser@tti.tamu.edu
http://tti.tamu.edu
Recent Projects
Test Procedures for Evaluating Distraction Potential in
Connected Vehicle Systems
The purpose of this project was two-fold: to develop test procedures
that can be used with production vehicles and nomadic technologies
to assess distraction potential and usability, and to provide guidelines
for interpretation and decision making about the testing outcomes.
This project consisted of interviewing potential users of the technolo-
gies, conducting driving studies on two low-cost driving simulators
and testing on a closed-course track. Several in-vehicle displays were
installed to replicate potential connected vehicle functions, and
researchers recorded and analyzed the effects on driver performance.
An Investigation of the Effects of Texting While Driving
The primary objective of this project was to assess the distrac-
tion potential of sending and receiving text messages while
driving. The two secondary objectives of this research were to
see if drivers adjust texting behavior based on driving demand,
and to assess the differential effects of age and experience on
theabilitytosendandreceivetextmessageswhiledriving.Data
were collected on a closed-course test track from participants
ages 16 to 54, and the type of cellular device was recorded
(touch screen or raised QWERTY keyboard).
Assessing Driver Distraction due to In-Vehicle Video Systems
Through Field Testing
Existing and emerging in-vehicle technologies — entertainment sys-
tems, communications systems and intelligent transportation systems
— have made travel hours more productive and entertaining, forever
transforming the way drivers interact with the vehicle. This study
used TTI’s instrumented vehicle to examine driver distraction due to
in-vehicle video systems. The vehicle contained cameras to monitor
driverperformance;aneye-trackingsystem;accelerometers;andsensors
for brake, throttle and steering; and used advanced image-processing
software to determine lane position. The project compared driving
performance with and without an entertainment video screen present
on a closed-course track.
Signing Guidelines for Flooding Conditions
In support of a research project examining signing strategies
for flooding or water-crossing situations for the Texas Depart-
ment of Transportation, focus groups and driver surveys were
conducted to provide information about driver responses to
floodedroadwaysingeneralandtovarioussigntypesandmes-
sages warning them about flooding conditions. The surveys
wereconductedusingtheSuperLab®softwaretopresentimages
of flooded roadways and signing treatments to participants.
TTI1726.3896.1116
To solve transportation
problems through
research, to transfer
technology and to
develop diverse human
resources to meet
the transportation
challenges of tomorrow.
ContactTTI’s Mission