Category: Systems and Connectivity

Moving goods on trains instead of trucks provides positive externality by reducing congestion and greenhouse gas emissions. Freight rail industry can increase economic competitiveness of regional and local economies. Access to rail network is often a critical component of a logistics center that serves as an anchor for the local economy. However, impacts of rail freight on the economy of local areas through which railroads move goods, operate terminals, manage operations, and carry out corporate business activities are not well understood beyond anecdotal and/or isolated cases. This study will examine impacts of railroad facilities and operations on local economy using cross-sectional and longitudinal analysis of large data. While the main focus of the study will be quantifying the impacts on local jobs and business establishments, selected study areas will be analyzed in detail to document the process by which the presence of freight rail affect the economy.

Direct vehicle warnings offer great potential toward improved safety by preventing or warning highway vehicles entering crossings at the time of train arrivals. However, this requires high ontime locational accuracy and timely communications. This project aims to work towards improved accuracy by developing an advanced, uncertainty-aware prediction model for grade crossing Signal Phase and Timing (SPaT) using train trajectory prediction. Accurate prediction of train arrival and departure times at highway-rail grade crossings remains challenging due to operational uncertainties such as varying speeds, dwell times, train length, and network interactions. Existing systems rely on Kalman filtering using real-time train location data from the head-of-train (HOT) and end-of-train (EOT), but these approaches have limited capability in
capturing nonlinear dynamics and uncertainty.

Building on prior work, the research team has already developed a Kalman-filter-based baseline model and collected train trajectory datasets from the Transportation Technology Center (TTC), Pueblo, CO. This project will leverage these datasets along with simulation data to develop and evaluate a hybrid probabilistic prediction framework.

The proposed framework integrates track-constrained state-space modeling with a Temporal Fusion Transformer (TFT) for multi-horizon forecasting. To explicitly address uncertainty, the model incorporates quantile prediction and conformal calibration to generate probabilistic predictions with calibrated confidence levels.

Rather than producing a single deterministic estimate, the system will generate multiple likely train trajectories and associated confidence levels. These predictions are used to derive SPaT at grade crossings. All evaluations will be conducted in a simulation environment using TTC datasets to assess prediction accuracy, robustness, and uncertainty calibration.

By leveraging existing datasets and focusing on simulation-based validation, this project reduces development risk while advancing beyond current methods. The outcomes will improve SPaT reliability, enhance situational awareness, and reduce safety risks and traffic congestion at highway-rail grade crossings.

Demand for Intercity Passenger Rail (IPR) transportation in the US continues to grow as the National Railroad Passenger Corporation (Amtrak) sets record annual ridership figures year after year. Since forecasted ridership is critical to developing and evaluating proposals for new or expanded IPR service across the country, an improved understanding of what factors drive origin destination (O-D) passenger travel demand for IPR service is essential for planners, stakeholders, and operators. The O-D of intercity passenger train riders can be obtained from ticket sales, but comprehensive O-D US IPR ridership data, or the trip purposes of riders – which drives their
travel decision making – are not. Therefore, additional analysis and modeling is required to make the leap from station-level demand to ridership along each segment of an IPR corridor and to tailor and market intercity rail services to maximize ridership for any given level of investment.

This research addresses this gap through two methodological approaches. Nationally, it leverages various metrics for station-level ridership, corridor passenger-miles, average trip lengths, and the relative ridership rankings of O-D station pairs that are publicly available for various US IPR corridors and services. The work will be complemented by a detailed collection of ridership data on the three intercity passenger services offered in the State of Michigan, first as an online survey accessed via mobile devices to be completed by passengers during active train rides, and then through in-depth analysis of in-person interviews by Michigan Tech researchers with passengers at rail stations and aboard the train. A geospatial analysis will be conducted with the survey results to identify the origins, destinations, and travel mode choices for riders to and from rail stations.

The results from this research can provide stakeholders involved in the development of passenger rail corridors a better understanding of ridership factors compared to the limitations of the current station-only approach. The comparison of modeling framework with actual case study outcomes helps to build confidence in the model and the resulting model will help inform trade-offs between making investments to add frequencies or infill stations to existing services or developing new routes and corridors.

The US rail network includes shared corridors that feature both freight and passenger train operations. The reliability of these mainline links is critical to ensuring that freight supply chains meet the level of service demanded by industrial customers while also providing performance that meets the mobility needs of commuters and the traveling public. Satisfying both of these rail transport objectives promotes economic development and supports job creation. However, the differing characteristics and performance requirements of freight and passenger trains create numerous operational challenges that impact the reliability of both operations. Optimizing the
level of capital investment in track infrastructure and train control system required to meet the desired reliability of a shared corridor is critical to the economics of these operations.

Previous researchers have used simulation to document the capacity (throughput) and performance (delay) implications of differences in passenger and freight train speed, power, weight, length and priority. Most critically, the capacity consumed by a single train varies across train types, and corridors with increasing levels of train heterogeneity require correspondingly more infrastructure to achieve the same performance and reliability as heterogenous freight-only or passenger-only corridors. However, a key limitation of existing research is that, during the 10 to 15 years since it has been conducted, freight railroads have altered their operations considerably, shifting to widespread deployment of longer trains, relying on locomotive assignments with lower horsepower-per-ton, and reconstructing corridors with longer passing
sidings or stretches of two main tracks in place of single track with frequent short sidings. In addition, the relative priority or dispatching preference given to passenger and freight trains has been raised as a critical issue, but was not explored in previous work. Thus, there is a critical need to update existing relationships to reflect this new operational paradigm.

To quantify the reliability of shared corridors under modern railway operating strategies, the project team will review past relationships and develop appropriate simulation experimental designs incorporating new factor levels to specifically capture important operational changes such as long trains and assigned priority. Using an accepted railway simulation tool, factorial combinations of these factors will be simulated, and results subjected to appropriate statistical analyses to examine changes in Base Train Equivalents over time and between operating conditions. The resulting freight and passenger train performance relationships will be evaluated via case studies to illustrate modern trends in shared corridor reliability.

The financial sustainability and resilience of railroad and transit operations depends on managing both costs and revenues. The rapid growth of artificial intelligence is reshaping both sides of this equation, creating urgent challenges for different stakeholders across the rail ecosystem. AI driven data center expansion has triggered an unprecedented surge in electricity demand: U.S. data center power consumption reached 183 TWh in 2024, over 4% of national electricity, and is projected to more than double by 2030. In the PJM market serving 67 million people across 13 Midwestern and Mid Atlantic states, capacity auction prices surged from $29 per megawatt day
in 2024 to $329 in 2026, an increase largely attributable to data center load growth. For electrified transit agencies and commuter railroads procuring through rolling short term contracts, this volatility amplifies recontracting risk, yet these operators lack tools for quantifying how contract length and terms interact with wholesale market conditions or how instruments such as VPPAs, physical PPAs, behind the meter generation, and demand response could mitigate exposure.

The same AI boom is simultaneously driving extraordinary demand for fiber infrastructure. AI focused data centers require 16 to 36 times more fiber per rack than traditional facilities, and the Fiber Broadband Association projects the U.S. must more than double its installed fiber miles from 160 million to 373 million by 2029. Power transmission co location along railroad corridors is likewise accelerating, as illustrated by the SOO Green HVDC Link following a railroad corridor from Iowa to Illinois. For the freight railroads and commuter agencies that own these corridors, right of way assets are becoming substantially more valuable, yet they lack tools for evaluating this revenue potential across states.

This project develops publicly available interactive dashboards serving these distinct stakeholders. The first allows electrified transit operators to explore how contract length and terms affect recontracting risk and how different procurement instruments can mitigate that risk. The second allows corridor owning railroads to evaluate the revenue potential of fiber and power right of way leasing across states.

Transit agencies, including commuter rail service providers, rely on systemwide customer satisfaction surveys to measure the quality of service so that they can still offer added value to the service they provide and remain competitive to preserve or grow ridership. In return, information gathered those surveys help agencies formulate solutions to increase user satisfaction and prioritize investments to avoid unnecessary expenditures to improve service attributes that have a limited or no impact on user satisfaction. In addition, measuring customer satisfaction over time helps determine changes in priorities and user perception, and evaluate the effectiveness of past investments.

However, many efforts aimed to capture service quality and its determinants fall short in extracting the full potential of invaluable information provided by these large scale surveys by just focusing on descriptive statistics of overall ratings or relying on hastily performed analyses.

This project aims to document, assess, and enhance the methods used by major commuter rail service providers to determine service quality and its determinants. The study will further examine the existing customer satisfaction survey datasets from Metra and six other peer agencies to identify the determinants of overall satisfaction ratings using with the help of more rigorous statistical techniques, conduct importance-performance analysis, and investigate the consistency of
the results across key market segments.

Various market segmentation criteria, such as transit dependency, trip purpose, customer tenure, or geography, will be employed to gain a deeper understanding of the variations in customer satisfaction and the underlying drivers of service quality across different market segments.

Based on the findings, the project team will develop recommendations for best practices in conducting a comprehensive and meaningful analysis of customer satisfaction survey data to define effective and prioritized strategies for maintaining and improving commuter rail rider experience. The study’s outcome can also inform the survey design process, enabling better alignment of survey content with key determinants of service quality and objectives of the recommended analysis approach.

Freight rail is a backbone of the U.S. economy, yet system shocks (e.g., pandemic-era volatility, extreme weather, cyber incidents, and labor disruptions) have exposed how failures at a single yard, bridge, or interchange can trigger cascading delays and inventory shortfalls far beyond the rail network. Agencies and railroads lack an integrated map that traces how a disruption at a node (e.g., a Chicago-area yard or an interchange) propagates through upstream suppliers, downstream customers, and alternative rail corridors. This project will develop a data-driven resilience framework that treats the rail network as a critical infrastructure shield and quantifies its built-in economic stabilizer role. The core innovation is to fuse (i) the 2022 Commodity Flow Survey (CFS) expanded coverage of auxiliary establishments (warehouses, distribution centers, and logistics facilities) to represent suppliers’ suppliers and customers’ customers, with (ii) the Surface Transportation Board (STB) Waybill Sample to locate interchange points and high consequence junctions where large shares of high-value and essential SCTG commodities transfer between carriers. Using the Freight Analysis Framework (FAF6) as the national flow baseline, we will construct a multilayer freight network (supply-chain nodes + rail terminals/interchanges + corridors) and estimate commodity-specific dependence on critical junctions.

We will introduce a Redundancy Gap Index (RGI) that scores each node/corridor by the availability and quality of feasible alternative routes for priority commodities (e.g., grain, chemicals/hazardous materials, energy inputs). Scenario simulations will test “what-if” outages (yard closures, bridge failures, flooding, or other capacity disruptions) and quantify impacts on travel time, rerouting distance, capacity feasibility, and downstream exposure. The outcome will be an AI-aided decision tool and guidance for rail agencies and state freight planners to prioritize resilience investments that improve reliability and redundancy, supporting freight planning requirements and enabling more transparent, evidence-based justification of rail resilience projects.

Passenger satisfaction is a primary driver of ridership for rail services, yet most customer satisfaction programs emphasize descriptive scores and year-to-year percent changes without clearly identifying which service attributes should be prioritized for improvement. A low satisfaction score does not necessarily indicate a high-impact problem if the attribute is not important to passengers. While Importance–Performance Analysis (IPA) is widely used to guide prioritization, it has key methodological limitations—most notably the arbitrary placement of quadrant crosshairs and the assumption that all attributes in the same quadrant are equally critical—leading to inconsistent and sometimes misleading prioritization.

This project will develop an evidence-based prioritization and investment framework centered on a continuous metric: the Importance–Performance Deviation Index (IPDI). IPDI integrates (i) each service attribute’s relative importance to passengers and (ii) its performance deviation from the best-observed performance within a rider group or system. The resulting index produces a defensible, rank-ordered list of improvement priorities without relying on quadrant boundaries. Building on IPDI, the project will also develop an investment translation module that links changes in attribute performance (e.g., reliability, crowding, cleanliness, information quality) to expected changes in overall satisfaction and loyalty intent. Using estimated response functions (marginal satisfaction gains per unit of improvement) and agency-provided unit cost ranges, the framework will quantify how much funding is required to reduce high-IPDI gaps and will optimize budget allocation to maximize satisfaction gains under constrained resources.

The final outcome will be a practical toolkit and guidance that rail agencies can use for ongoing performance tracking, prioritization, and cost-effective investment planning.

Intermodal freight terminals are critical nodes in North American supply chains, yet they face persistent congestion, long truck dwell times, and significant local air pollution. Major rail operators manage high-volume terminals where stochastic truck arrivals and limited coordination with yard operations lead to inefficiencies.

The rapid advancement of autonomous modular vehicle technologies (AMVT) creates an unprecedented opportunity: unlike human-driven vehicles, AMVT-based drayage fleets can adjust load capacity, departure times, speeds, and routes dynamically in response to terminal conditions. This controllability enables real-time coordination with rail yard crane operations and train schedules.

In Phase I of this project (5/1/2026 – 5/1/2027), the research team will focus on data collection, including drayage vehicle and train operational data as well as interviews with rail yard operating officers to understand the current state of the practice, challenges and opportunities (e.g., adopting advanced vehicle technologies such as AMVT) in railyard planning and operational decisions. Realistically we target to interview 2-5 railyards across the country. To supplement the small sample size, we plan to use LLM to help generate additional data for railyard operations and technology adoption profiles based on size and geographic location.

In Phase II, the research team will develop an integrated AI-based optimization framework to synchronize AMVT-based drayage operations with rail terminal processes, with the goal of reducing congestion and operating costs. Specifically, A bi-level optimization framework will be developed:
· Upper Level: Autonomous truck dispatch and routing optimization to minimize queueing delay, fuel consumption, and emissions.
· Lower Level: Rail yard operations optimization including crane scheduling, container stacking, and train loading/unloading sequences.

The model will incorporate mixed-integer linear programming (MILP) and reinforcement learning for adaptive control under uncertainty (e.g., demand and financial uncertainty).

This research improves railyard efficiency through integrated dispatch with rail yard scheduling, increased terminal throughput, and reduced truck dwell time by adopting emerging autonomous modular vehicle technology (AMVT).

North American freight railroads uniquely combine safety, speed and efficiency in transporting large amounts of freight long distances over land. Prompted by railroad commitments to reduce emissions, and looming stricter in-use locomotive regulations, US freight railroads face the challenge of reducing or eliminating the roughly 3 billion gallons of diesel fuel consumed each year. To achieve this, North American freight railroads are exploring alternative technologies, including modern options for electrification, batteries, hydrogen fuel cells, and renewable biodiesel.

Previous research involving the University of Texas at Austin developed a tool to help railroads evaluate different locomotive alternatives. The Advanced Locomotive Technology and Rail Infrastructure Optimization System (ALTRIOS) is an open-source simulation framework to model and compare the deployment of conventional and alternative energy locomotive technologies on rail corridors subject to realistic multi-train dispatching and locomotive utilization. The model contains detailed train performance and locomotive powertrain models, and yields outputs quantifying railway operations, costs, energy and emissions.

Previous research has focused on ALTRIOS open-source development, with limited application to case study corridors and a focus on mixed consists of diesel-electric and Battery Electric Locomotives. This research seeks to leverage ALTRIOS to compare a larger set of freight rail propulsion technologies and strategies across a broader group of corridors representative of North American mainline operations.

To accomplish this goal, the project team must research and develop an appropriate ALTRIOS powertrain module to represent the characteristics of hydrogen-powered locomotives, either using fuel cells or direct combustion of hydrogen in a prime mover. Additional refinements to the refueling, cost and emissions modules must also be developed to capture hydrogen and locomotive applications involving renewable biodiesel.

After developing these new ALTRIOS modules, existing modules for battery locomotives and ongoing development of modules for intermittent electrification will be leveraged to conduct a series of case studies comparing multiple locomotive technologies across different corridor topology, topography and train operations. It is expected that different technology deployment strategies will prove to be optimal on certain combinations of corridors and train operations.