Optimizing Train Driving to Minimize the Electricity Cost of an Entire Railway Traffic Mesh using Evolutionary Algorithms

Eduardo Pilo de la Fuente; Sudip K. Mazumder; Maria Antonia Simon

cs.NEAug 26, 2026

Optimizing Train Driving to Minimize the Electricity Cost of an Entire Railway Traffic Mesh using Evolutionary Algorithms

Authors: Eduardo Pilo de la Fuente, Sudip K. Mazumder, Maria Antonia Simon

Organizations: Higher Polytechnic School, Universidad Francisco de Vitoria, Ctra. Pozuelo-Majadahonda Km 1,800, 28223, Pozuelo de Alarcón, Madrid., Spain · Electrical and Computer Engineering Department, University of Illinois at Chicago, Chicago, IL, 60607, USA

Abstract

This paper presents a procedure to optimize the way trains are driven, which pursues, in addition to fulfilling operational constraints such as admissible speeds or journey durations, the minimization of the cost related to supplying electrical energy to the trains, including the cost of the energy consumption and the cost of the power capacity utilization. This procedure combines: (i) a traffic model that merges the energy and power footprint of each rail service part of the traffic mesh, and (ii) an evolutionary computation framework that enables searching for the optimal way to drive the trains to achieve an optimal traffic mesh. This procedure is applied to a 450 km long section of the high-speed line from Madrid to Barcelona (Spain).

Trajectory Optimization

Trajectory optimization computes motion or control sequences that minimize costs while satisfying system dynamics, safety limits, and task constraints.

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Jul 27, 2026J/K move · Enter open · S save

Quantum-Inspired Evolutionary Neighborhood Search for Arrival-Departure Track Utilization Adjustment under Short-Term Disturbances

Xiaobin Li, Wuming Lei, Yanbin Gao, Weiguang Wang

School of Transportation Engineering East China Jiaotong University Nanchang, Jiangxi 330038, China

Evolutionary Optimization Intelligent Transportation Systems Metaheuristic Optimization

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Short-term disturbances at major passenger railway stations alter train arrival and departure times as well as the release sequence of station resources. Effective recovery therefore requires coordinated adjustment of arrival-departure track allocation, station resource occupation, and train retiming. This study represents the station resources involved in train arrival, track occupancy, and departure operations as zone-level resource-occupation intervals. An arrival-departure track allocation adjustment model is formulated. Resource compatibility is imposed as the feasibility condition, while train delays and resource reassignment costs are jointly considered. A quantum-inspired evolutionary algorithm combined with neighborhood search (QEA-NS) is proposed to solve the model. Perturbation instances are constructed using GTFS timetable data from Frankfurt Hauptbahnhof, Germany. QEA-NS is compared with CP-SAT under the same candidate resource set and feasibility criteria. Both methods generate solutions satisfying the modeled resource compatibility constraints. QEA-NS yields a total delay of 388 min, compared with 519 min for CP-SAT, representing a reduction of 25.2%. The mean delay of delayed trains decreases from 4.99 to 3.73 min, although QEA-NS requires a longer solution time. Across 10 random perturbation instances, QEA-NS achieves lower total delay in every case. Its mean total delay and standard deviation are 390.5 min and 35.945 min, respectively, compared with 673.8 min and 105.739 min for CP-SAT. The results indicate that, under the adopted resource representation and constraints, QEA-NS improves the delay performance of recovery plans. Its computational efficiency, however, requires further improvement.

Abstract

Trajectory Optimization

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