What We Do
Advanced Transportation Operations
The Advanced Transportation Operations program improves mobility through expertise, innovative technologies and performance-driven solutions. Research spans the full operations arena, focusing on technology deployments and the collection, analysis and communication of data to both system operators and users. Research areas include intelligent transportation systems (ITS), traveler information, incident management, performance metrics and transportation systems management and operations (TSMO).
Connected Infrastructure
The Connected Infrastructure program integrates infrastructure into the cooperative and connected environment to create systems that are self-aware, reactive to prevailing conditions, and support and enhance information exchange between roadside, devices and vehicles. Research is conducted across a broad range of infrastructure including roadside devices, traffic signals, signage, work zone environments and more. Through this broadly connected paradigm, the program seeks to improve safety and mobility as well as address environmental, economic and efficiency concerns.
Our Focus Areas
System Operations
Applying multimodal transportation strategies to maximize the efficiency, safety and utility of existing and planned transportation infrastructure.
Technology Deployments
Applying technology, test beds and real-world trials to advance the state-of-the practice and the data collected from the system.
Data Analytics
Examining and visualizing data from operations, simulations and deployments to inform what the system is doing at different states.
Performance Management
Utilizing system information to inform users and decision makers and make investment and policy decisions to achieve performance goals.
System Modeling
Employing the use of advanced simulation techniques to model different aspects of the system and different approaches to operations and project the results.
Outreach and Technology Transfer
Enhancing the profession by sharing and transferring knowledge to practitioners through multiple mechanisms such as workshops, technology deployments and reporting.
Artificial Intelligence
Leveraging artificial intelligence methodologies, such as machine learning, to optimize transportation operations, enhance decision-making and accelerate the deployment of intelligent mobility solutions.
Our Work
Nolanville Railroad Artificial Intelligence
As freight trains grow longer, blocked railroad crossings are becoming a more frequent challenge—especially for smaller communities like Nolanville, Texas. Long-term blockages affect both public mobility and emergency response times, particularly since the city’s fire station is located next to the tracks. While a grade separation would be the ideal solution, its cost is prohibitive.
This project introduces a lower-cost, intermediate solution: real-time rail monitoring using TTI’s AI-powered grade crossing detection system. Cameras at two key crossings — including one solar-powered site — will provide live updates to emergency responders and the public via a web platform. The system uses video analytics and deep learning to detect train activity and support smarter route decisions when crossings are blocked.
Campus Transportation Technology Initiative
Organizations that developed new technologies with the capability to improve any aspect of transportation on the Texas A&M University campus were invited to demonstrate their technologies on campus as part of the initiative. These technologies ranged from smart device applications to fully automated vehicles — the goal of each is to best serve the mobility needs of the campus and to use the campus as a test bed for the technology. Texas A&M, TTI and our partners evaluated the technologies and determined how well they work independently or in combination with other technologies in a complementary fashion to solve transportation issues in the campus environment. Through both operational deployments and classroom projects, the initiative applied and evaluated private-sector innovations for their role in the campus environment as well as examined the efficiencies of various campus transportation aspects, such as the transit system. These efforts laid the groundwork for embracing transformative technologies on campus, such as the use of autonomous vehicles.
I-35 Traveler Information During Construction
As part of planned improvements along 200 directional miles of the I-35 corridor in the Texas Department of Transportation’s (TxDOT’s) Waco District, TTI supported a comprehensive operations and outreach effort to enhance safety, mobility and public awareness during construction. Key components included multi-platform traveler information for planned closures, real-time conditions and incidents; a safety-focused work zone management approach; and a mobile end-of-queue warning system that reduced overnight crashes by 60%. The team also developed a districtwide incident management plan with public alerts and after-action reviews, integrated pedestrian and bicycle facility impacts into outreach materials and launched the “Be Safe Be Seen” campaign near Baylor University. Additional efforts included corridor-wide systems integration, field site and software coordination, and extensive stakeholder engagement with local agencies, emergency responders, businesses, schools and health care facilities.
Innovation Inter-Agency Contract
The work efforts for this inter-agency contract can be generalized into three general activities: investigate, communicate and innovate. Investigating includes developing the definition of innovation to be used for the project, a thorough existing source review, and an inventory of the districts and the divisions across TxDOT. Communicating involves developing information to support an innovations outreach mechanism for the TxDOT web presence, creating project implementation modules in PowerPoint and developing two-page project innovation summaries. The final activity — innovate — involves collaborating with Receiving Agency personnel to identify problems, define project scope, and implement solutions by connecting Performing Agency subject matter experts with those working to put innovations into practice.
Technical Support to the CAV Task Force
The purpose of this interagency contract is for TTI to support the Texas Connected and Autonomous Vehicle (CAV) Task Force. The goal of the Texas CAV Task Force is to be a single point of information and coordination for all CAV activities in Texas. The Texas CAV Task Force will host industry meetings, create a knowledge base for best practices and collaboration, and report out on lessons learned. The Texas CAV Task Force will serve as a clearinghouse of information for those seeking to pursue innovative technology in Texas. The Texas CAV Task Force will be an incubating hub for any policy recommendations that may need to be brought before the Texas Legislature and Governor.
Traffic Optimization for Signalized Corridors (TOSCo)
TOSCo is a vehicle system that uses DSRC communications with an infrastructure subsystem to approach a signalized intersection in an optimized fashion. TOSCo vehicles use data from the infrastructure to plan an approach to the intersection to avoid a stop if possible. This phase of the project involves building the complete system and testing the system in a closed course and on a live corridor. This project also includes simulation of a corridor to assess the potential benefits of the system with various market penetrations.
Emerging Challenges to Priced Managed Lanes
TTI led a National Cooperative Highway Research Program (NCHRP) synthesis study that examined how state departments of transportation and partnering entities addressed challenges to implementing tolling on managed lanes. The synthesis included a survey of all state DOTs that identified goals for pricing programs, challenging audiences for public engagement and mitigation approaches for addressing problems. In addition, the synthesis developed six case studies that served as examples for addressing challenges. The conclusions for the synthesis outlined a summary of lessons learned and provided guidance for future research needs, particularly on the need to develop an outreach toolkit for agencies to define and explain the purpose of their pricing programs.
National Inventory of Specialty Lanes and Highways
TTI led an NCHRP synthesis study that examined how state departments of transportation and partnering entities addressed challenges to implementing tolling on managed lanes. The synthesis included a survey of all state DOTs that identified goals for pricing programs, challenging audiences for public engagement, and mitigation approaches for addressing problems. In addition, the synthesis developed six case studies that served as examples for addressing challenges. The conclusions for the synthesis outlined a summary of lessons learned and provided guidance for future research needs, particularly on the need to develop an outreach toolkit for agencies to define and explain the purpose of their pricing programs.
Vehicle Counting Using AI Technology
Commercial vehicle drivers are governed by hours-of-service rules. These rules ensure drivers have sufficient rest to maintain safe driving habits. Many drivers choose to pull into a rest area to comply and Texas has areas specifically designed for truck parking. These areas become very popular at night and researchers have been investigating methods to keep track of available parking space and communicating the conditions to drivers before they arrive. Traditional methods apply a count-in / count-out solution with the difference being the number of vehicles remaining in the parking area. This can give a count but there is no mechanism to self-correct for any errors made. Additionally, the errors are cumulative as time goes on until a ground truth count is made to reset the system. Another option is to install detectors at marked spaces, but it is widely known that when conditions get busy, trucks park wherever they can.
Enter artificial intelligence (AI). Why not have a solution that counts the trucks within the whole facility rather than at an entry, exit, or marked space? Given proper views, an AI-based system can count the vehicles in the scene. The count can be used to generate messages for inbound message signs and even information directly sent to commercial vehicle onboard systems.
