Freight transportation is a significant contributor to greenhouse gas (GHG) emissions in the US. As an emerging technology, truck platooning leverages vehicle-to-vehicle communications to enable trucks to travel in convoys with close proximity, which reduces air drag and consequently truck fuel use and GHG emissions. However, uncertainties remain about how this emerging technology may be adopted and its climate impacts. To this end, this paper investigates the role of truck platooning adoption in mitigating the climate impact of trucking from a system perspective. Considering the dynamic nature of truck platooning adoption, system dynamics (SD) models based on stock and flow diagrams are developed to estimate the potential reduction in fuel use and CO₂ emissions in the US trucking sector when truck platooning technology becomes available. The results show that adopting platooning could save 292 million metric tons of CO₂ emissions in 180 months after the initial introduction of the technology in the US truck sector. The analysis provides insights for accelerating truck platooning adoption while enhancing its environmental impact.

Some frameworks of model uncertainty combine a large number of densities. Computing the quantiles of the combined distribution might be practically infeasible when the number of densities is large. We introduce a numerical procedure that can reduce the computational burden. This consists of proving a sequential update formula that estimates different moments of the combined distribution and a finite moment problem that reconstructs the distribution from the moments. Based on an example on the determinants of long-run economic growth, we show that tracking the first four moments of the combined distribution along with generalized lambda distribution fit, provides a good approximation for the overall shape of the distributions.

This paper leverages emerging highway vehicle platooning technology to improve the efficiency and effectiveness of fleeting trains at minimum headways under moving blocks. The research aims to better understand how closely following trains respond to different throttle and brake control algorithms, and, using insights gained from automobile and truck platooning technology, develop improved train control algorithms balancing fuel efficiency and train headway. To do so, a detailed multi-train performance simulator is developed to evaluate following train control algorithms and then adapt highway vehicle platooning control methods to the heavy haul freight rail domain. Five following train control algorithms under two different communication topologies are formulated to more intelligently consider information on the status of the train ahead when specifying throttle or brake settings for each following train. With string stability, following trains attenuate the actions of preceding trains and each successive train requires less aggressive acceleration and braking rates to maintain headways. The simulation results suggest that certain families of control laws are better than others at managing train separation and fuel consumption within train fleets. The results of this research will allow industry practitioners to develop improved locomotive driver advisory and semi-autonomous adaptive train cruise control systems for the operation of fleets of trains under moving blocks, and railroad operators to make more informed decisions regarding the potential fuel efficiency and capacity benefits of these systems.

The expected crash frequency model of Illinois Department of Transportation’s Bureau of Design and Environment needed improvement to incorporate track circuitry as well as pedestrian exposure at railroad-highway grade crossings to make the model more comprehensive. The researchers developed, calibrated, and validated three models to predict collision rates at public, at-grade railroad-highway crossings in Illinois’ six-county northeast region for prioritizing railroad-highway crossings for safety improvements. The first model updated B-factors in the existing Illinois model, which was last validated with data from 1968. The second model modified B-factors to include circuitry types given the active maximum traffic control device at the crossing and added another factor (i.e., P-factor) to account for pedestrian daily traffic using the crossing. The third model added a P-factor to the existing US Department of Transportation’s web accident prediction system model to account for daily pedestrian traffic. Using year 2018 validation data, the first model had an r2 of 0.20 with reported collision rates. The second model had an r2 of 0.58 with reported collision rates, while the existing BDE model had an r2 of 0.17 with year 2018 reported collision rates. The third model had an r2 of 0.70 with reported collision rates using 2018 validation data whereas the existing US Department of Transportation’s web-based accident prediction system model had an r2 of 0.50 using year 2018 validation data. The three models are presented in this report along with a digital tool using the second model for illustrative purposes.