Comparing the Efficiency and Effectiveness of Different Train-Following Control Algorithms for Fleets of Heavy-Haul Freight Trains under Moving Blocks - Roscoe, Geordie; Tyler Dick, C. (Transportation research record, 2023)
With the nationwide installation of positive train control (PTC) technology recently completed, freight railroads are searching for ways to leverage the technology to improve their operations beyond increasing safety. One possibility is reducing train headways through developing and installing a PTC-based moving-block system, thus increasing the capacity of railroad mainlines without adding costly track infrastructure. Though operating trains at shorter headways can theoretically increase line capacity, effectively controlling following trains becomes more difficult compared with a fixed-block system, requiring a fast-reacting train crew or control algorithm that effectively minimizes headway and fuel consumption while attenuating all fluctuations in lead-train speed. Otherwise, rapid changes in throttle and brake settings may be required, reducing following-train fuel efficiency, generating in-train forces, and diminishing the expected capacity gains from shortened headways. This research, sponsored by the Federal Railroad Administration, aims to better understand how closely following freight trains respond to different throttle- and brake-control algorithms. Using insights from connected automobile and truck platooning technology, the project team developed several train-following control algorithms, analyzed their stability, and simulated their performance with fleets of freight trains subject to different speed profiles. While moving-block systems do require additional train spacing beyond the minimum safe braking distance to account for train control actions, effective train-following control algorithms can minimize this distance. Also, the developed control laws exhibit a trade-off between minimizing train headway and fuel consumption, potentially allowing railway operators to choose an optimal balance.