Position synchronization for track geometry inspection data via big-data fusion and incremental learning

Track geometry inspection data is important for managing railway infrastructure integrity and operational safety. In order to use track geometry inspection data, having accurate and reliable position information is a prerequisite. Due to various issues identified in this research, the positions of different track geometry inspections need to be aligned and synchronized to the same location before being used for track degradation modeling and maintenance planning. This is referred to as “position synchronization”, a long-standing important research problem in the area of track data analytics. With the aim of advancing the state of the art in research on this subject, we propose a novel approach to more accurately and expediently synchronize track geometry inspection positions via big-data fusion and incremental learning algorithms. Distinguishing it from other relevant studies in the literature, our proposed approach can simultaneously address data exceptions, channel offsets and local position offsets between any two inspections. To solve the Position Synchronization Model (PS-Model), an Incremental Learning Algorithm (IL-Algorithm) is developed to handle the “lack of memory” challenge for the fast computation of massive data. A case study is developed based on a dataset with data size of 18 GB, including 58 inspections between February 2014 and July 2016 over 323 km (200 miles) of tracks belonging to China High Speed Railways. The results show that our proposed model performs robustly against data exceptions via the use of multi-channel information fusion. Also, the position synchronization error using our proposed approach is within 0.15 meters (0.5 feet). Our proposed data-driven, incremental learning algorithm can quickly solve the complex, data-extensive, position synchronization problem, using an average of 0.1 s for processing one additional kilometer of track. In general, the data analysis methodology and algorithm presented in this paper are also suitable to address other relevant position synchronization problems in transportation engineering, especially when the dataset contains multiple channels of sensors and abnormal data outliers.     

An optimization model for integrated planning of railway traffic and network maintenance

Railway transportation systems are important for society and have many challenging and important planning problems. Train services as well as maintenance of a railway network need to be scheduled efficiently, but have mostly been treated as two separate planning problems. Since these activities are mutually exclusive they must be coordinated and should ideally be planned together. In this paper we present a mixed integer programming model for solving an integrated railway traffic and network maintenance problem. The aim is to find a long term tactical plan that optimally schedules train free windows sufficient for a given volume of regular maintenance together with the wanted train traffic. A spatial and temporal aggregation is used for controlling the available network capacity. The properties of the proposed model are analyzed and computational experiments on various synthetic problem instances are reported. Model extensions and possible modifications are discussed as well as future research directions.     

Evaluation of CO2 emissions from railway resurfacing maintenance activities

This paper is the world first to investigate the CO₂ impact of railway resurfacing in ballasted track bed maintenance. Railway resurfacing is an important routine maintenance activity that restores track geometry to ensure safety, reliability and utility of the asset. This study consisted of an extensive field data collection from resurfacing machineries (diesel-engine tamping machines, ballast regulators and ballast stabilisers) including travel distances, working distances, fuel consumption and construction methodologies. Fuel consumption was converted to a kg CO₂/m using the embodied energies of diesel. Analyses showed that tamping machines emitted the highest CO₂ emissions of the resurfacing machineries, followed by ballast regulators and ballast stabilisers respectively. Tamping machines processed 4.25 m of track per litre of diesel, ballast regulators processed 6.51 m of track per litre of diesel and ballast stabilisers processed 10.61 m of track per litre of diesel. The results were then compared to previous studies and a rigorous parametric study was carried out to consider long-term resurfacing CO₂ emissions on Australian railway track. The outcome of this study is unprecedented and it enables track engineers and construction managers to critically plan strategic rail maintenance and to develop environmental-friendly policies for track geometry and alignment restoration.     

Field investigation and parametric study of greenhouse gas emissions from railway plain-line renewals

Railway transportation is becoming increasingly important in many parts of the world for mass transport of passengers and freight. This study was prompted by the industry’s need to systemically estimate greenhouse gas emissions from railway construction and maintenance activities. In this paper, the emphasis is placed on plain-line railway maintenance and renewal projects. The objective of this study was to reduce the uncertainties and assumptions of previous studies based on ballasted track maintenance and renewal projects. A field-based data collection was carried out on plain-line ballasted track renewals. The results reveal that the emissions from the materials contribute more than nine times the CO₂-e emissions than the machines used in the renewal projects. The results show that extending the lifespan of rail infrastructure assets through maintenance is beneficial in terms of reducing CO₂-e emissions. Analysis was then carried out using the field data. Then the results were compared to two ballastless track alternatives. The results show that CO₂-e emissions per metre from ballasted track were the least overall, however, the maintenance CO₂-e emissions are greater than those of ballastless tracks over the infrastructure lifespan, with ballasted track maintenance emitting more CO₂-e emissions at the 30 and 60 year intervals and the end of life when compared to the ballastless track types. The outcome of the study can provide decision makers, construction schedulers, environmental planners and project planners with reasonably accurate GHG emission estimates that can be used to plan, forecast and reduce emissions for plain-line renewal projects.     

A decision support approach for condition-based maintenance of rails based on big data analysis

In this paper, a decision support approach is proposed for condition-based maintenance of rails relying on expert-based systems. The methodology takes into account both the actual conditions of the rails (using axle box acceleration measurements and rail video images) and the prior knowledge of the railway track. The approach provides an integrated estimation of the rail health conditions to support the maintenance decisions for a given time period. An expert-based system is defined to analyse interdependency between the prior knowledge of the track (defined by influential factors) and the surface defect measurements over the rail. When the rail health conditions is computed, the different track segments are prioritized, in order to facilitate grinding planning of those segments of rail that are prone to critical conditions. In this paper, real-life rail conditions measurements from the track Amersfoort-Weert in the Dutch railway network are used to show the benefits of the proposed methodology. The results support infrastructure managers to analyse the problems in their rail infrastructure and to efficiently perform a condition-based maintenance decision making.     

Data-driven linear decision rule approach for distributionally robust optimization of on-line signal control

We propose a two-stage, on-line signal control strategy for dynamic networks using a linear decision rule (LDR) approach and a distributionally robust optimization (DRO) technique. The first (off-line) stage formulates a LDR that maps real-time traffic data to optimal signal control policies. A DRO problem is solved to optimize the on-line performance of the LDR in the presence of uncertainties associated with the observed traffic states and ambiguity in their underlying distribution functions. We employ a data-driven calibration of the uncertainty set, which takes into account historical traffic data. The second (on-line) stage implements a very efficient linear decision rule whose performance is guaranteed by the off-line computation. We test the proposed signal control procedure in a simulation environment that is informed by actual traffic data obtained in Glasgow, and demonstrate its full potential in on-line operation and deployability on realistic networks, as well as its effectiveness in improving traffic.     

Co-location decision tree for enhancing decision-making of pavement maintenance and rehabilitation

This paper presents a new decision tree induction method, called co-location-based decision tree (CL-DT), to enhance the decision-making of pavement maintenance and rehabilitation strategies. The proposed algorithm utilizes the co-location characteristics of spatial attribute data in the pavement database. The paper first presented the co-location mining algorithm, including spatial attribute data selection, determination of rough candidate co-locations, determination of candidate co-locations, pruning the non-prevalent co-locations, and induction of co-location rules, and then focused on the development of the co-location decision tree (CL-DT) algorithm, which includes the non-spatial attribute data selection, co-location algorithm modeling, node merging criteria, and co-location decision tree induction. A pavement database covering four counties, which are provided by North Carolina Department of Transportation (NCDOT), is used to verify the proposed method. The experimental results demonstrated that (1) the proposed CL-DT algorithm can make a better decision, and has higher accuracy than the existing decision tree methods do; (2) the training data can be fully played roles in contribution to decision tree induction and the computational time taken for the tree growing, tree drawing and rule generation is largely decreased; (3) quantity and locations of six treatment strategies proposed by the ITRC and by CL-DT is much close for each treatment strategy.     

A multiobjective model for maximizing fleet availability under the presence of flight and maintenance requirements

Every aircraft, military or civilian, must be grounded for maintenance after it has completed a certain number of flight hours since its last maintenance check. In this paper, we address the problem of deciding which available aircraft should fly and for how long, and which grounded aircraft should perform maintenance operations, in a group of aircraft that comprise a combat unit. The objective is to achieve maximum availability of the unit over the planning horizon. We develop a multiobjective optimization model for this problem, and we illustrate its application and solution on a real life instance drawn from the Hellenic Air Force. We also propose two heuristic approaches for solving large scale instances of the problem. We conclude with a discussion that gives insight into the behavior of the model and of the heuristics, based on the analysis of the results obtained.