Models and Algorithms of Conflict Detection and Scheduling Optimization for High-Speed Train Operations Based on MPC

Abstract

The high-speed train with high transport speed and punctuality rate has become a leading tool of daily traveling. Compared with the general-speed railway transportation, the high-speed railway transportation requires a timely and automatic adjustment capability in the centralized traffic control (CTC) system. In order to improve the capability, this paper mainly explores the models and algorithms of conflict detection and scheduling optimization of high-speed train operations. They are implemented under the mechanism of model predictive control (MPC) over the rolling prediction horizon. A train movement prediction model with conflict resolution is established. The mechanism of conflict detection and scheduling optimization is implemented through a case study. The simulation results demonstrate the effectiveness of the proposed mechanism.

Real-time fleet management decision support system with security constraints

“…Although not directly in trucks and highways, the problem of scheduling transports and detecting possible conflicts that can increase the risk of collision has been studied since the early 1970s in the railway scheduling literature (Szpigel 1973). Because having a single track is commonplace in many older systems, operations research tools have been extensively used to identify conflicts and schedule and reschedule trains with high safety requirements [see Wang et al (2018) and the references therein]. In most of these cases, authors address the problem of blocking a whole part of a track, such that trains flow in a single direction for some time, with the simplification that trains follow a strict and known schedule of stops and known speeds.…”

Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem

“…The most commonly used techniques include depth-first-search, branch and bound, genetic algorithms, tabu search and simulated annealing, see e.g., [3], [4], [17], [18] and references therein. For a comprehensive presentation of related work, see [20].…”

Blockchain‐enabled virtual coupling of automatic train operation fitted mainline trains for railway traffic conflict control

“…A state transition map‐based approach was proposed [16] for traffic state prediction and conflict detection. The authors in [17] explored the model predictive control‐based rolling horizon conflict detection and scheduling optimisation of high‐speed train operations. Based on the delay event categories, the various distribution models such as the lognormal, exponential, gamma, uniform, logistic, and normal distribution were selected to estimate model delay patterns and parameters [18].…”