Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks

Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters

Leveraging connected vehicle platooning technology to improve the efficiency and effectiveness of train fleeting under moving blocks

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

Railroad-Highway Crossing Safety Improvement Evaluation and Prioritization Tool

R27-218The expected crash frequency model of Illinois Department of Transportation\u2019s 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\u2019 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\u2019s 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\u2019s 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