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 autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition,planning and cost

National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment.Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain.Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value.A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation.     

Principal components analysis and track quality index: A machine learning approach

Track geometry data exhibits classical big data attributes: value, volume, velocity, veracity and variety. Track Quality Indices-TQI are used to obtain average-based assessment of track segments and schedule track maintenance. TQI is expressed in terms of track parameters like gage, cross-level, etc. Though each of these parameters is objectively important but understanding what they collectively convey for a given track segment often becomes challenging. Several railways including passenger and freight have developed single indices that combines different track parameters to assess overall track quality. Some of these railways have selected certain parameters whilst dropping others. Using track geometry data from a sample mile track, we demonstrate how to combine track geometry parameters into a low dimensional form (TQI) that simplifies the track properties without losing much variability in the data. This led us to principal components. To validate the use of principal components as TQI, we employed a two-phase approach. First phase was to identify a classic machine learning technique that works well with track geometry data. The second step was to train the identified machine learning technique on the sample mile-track data using combined TQIs and principal components as defect predictors. The performance of the predictors were compared using true and false positive rates. The results show that three principal components were better at predicting defects and revealing salient characteristics in track geometry data than combined TQIs even though there were some correlations that are potentially useful for track maintenance.     

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.     

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.     

Effective aircraft maintenance schedule adjustment following incidents

In the real world, planned aircraft maintenance schedules are often affected by incidents. Airlines may thus need to adjust their aircraft maintenance schedules following the incidents that occur during routine operations. In tradition, such aircraft maintenance schedule adjustment has been performed manually, a process which is neither effective nor efficient, especially when the problem scale is large. In this study, an aircraft maintenance schedule adjustment model is developed, with the objective of minimizing the total system cost, subject to the related operating constraints. The model is formulated as a zero-one integer program and is solved using a mathematical programing solver. The effectiveness of the model is evaluated by application to a case study using data from an aircraft maintenance center in Taiwan. The test results show the proposed model, as well as the scheduling rules abstracted from the results are useful for the decision maker to adjust good maintenance schedules.     

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.     

Stochastic eco-routing in a signalized traffic network

In this paper, an eco-routing algorithm is developed for vehicles in a signalized traffic network. The proposed method incorporates a microscopic vehicle emission model into a Markov decision process (MDP). Instead of using GPS-based vehicle trajectory data, which are used by many existing eco-routing algorithm, high resolution traffic data including vehicle arrival and signal status information are used as primary inputs. The proposed method can work with any microscopic vehicle model that uses vehicle trajectories as inputs and gives related emission rates as outputs. Furthermore, a constrained eco-routing problem is proposed to deal with the situation where multiple costs present. This is done by transferring the original MDP based formulation to a linear programming formulation. Besides the primary cost, additional costs are considered as constraints. Two numerical examples are given using the field data obtained from City of Pasadena, California, USA. The eco-routing algorithm for single objective is compared against the traditional shortest path algorithm, Dijkstra’s algorithm. Average reductions of CO emission around 20% are observed.     

Estimating the marginal cost of railway track renewals using corner solution models

Economic theory advocates marginal cost pricing for efficient utilisation of transport infrastructure. A growing body of literature has emerged on the issue of rail marginal infrastructure wear and tear costs, but the majority of the work is focused on costs for infrastructure maintenance. Railway track renewals are a substantial part of an infrastructure manager’s budget, but in disaggregated statistical analyses they cause problems for traditional regression models since there is a piling up of values of the dependent variable at zero. Previous econometric work has sought to circumvent the problem by aggregation in some way. In this paper we instead apply corner solution models to disaggregate (track-section) data, including the zero observations. We derive track renewal cost elasticities with respect to traffic volumes and in turn marginal renewal costs using Swedish railway renewal data over the period 1999–2009. This paper is the first attempt in the literature to apply corner solution models, and in particular the two-part model, to disaggregate renewal cost data in railways. It is also the first paper that we are aware of to report usage elasticities specifically for renewal costs and therefore adds important new evidence to the previous literature where there is a paucity of studies on renewals and considerable uncertainty over the effects of rail traffic on renewal costs. In the Swedish context, we find that the inclusion of marginal track renewal costs in the track access pricing regime, which currently only reflects marginal maintenance costs, would add substantially to the existing track access charge. EU legislation requires that access charges reflect the ‘costs directly incurred as a result of operating the train service’, which should include a marginal renewal cost component. This change would also increase the cost recovery ratio of the Swedish infrastructure manager, thus meeting a policy objective of the national government.     

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.     

Improving rail network velocity: A machine learning approach to predictive maintenance

Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered operating speed due to track/train condition and delays caused by derailments. Railroads have put significant infrastructure and inspection programs in place to avoid service interruptions. One of the key measures is an extensive network of wayside mechanical condition detectors (temperature, strain, vision, infrared, weight, impact, etc.) that monitor the rolling-stock as it passes by. The detectors are designed to alert for conditions that either violate regulations set by governmental rail safety agencies or deteriorating rolling-stock conditions as determined by the railroad.Using huge volumes of historical detector data, in combination with failure data, maintenance action data, inspection schedule data, train type data and weather data, we are exploring several analytical approaches including, correlation analysis, causal analysis, time series analysis and machine learning techniques to automatically learn rules and build failure prediction models. These models will be applied against both historical and real-time data to predict conditions leading to failure in the future, thus avoiding service interruptions and increasing network velocity. Additionally, the analytics and models can also be used for detecting root cause of several failure modes and wear rate of components, which, while do not directly address network velocity, can be proactively used by maintenance organizations to optimize trade-offs related to maintenance schedule, costs and shop capacity. As part of our effort, we explore several avenues to machine learning techniques including distributed learning and hierarchical analytical approaches.     

Failure rates and data driven policies for vehicle safety inspections in Pennsylvania

Rail, truck, commercial bus, and aircraft have federally mandated safety inspection programs in the United States, while inspections of personal vehicles, which make up the majority of passenger miles, are optionally imposed at the state level. In recent years, some states have chosen to eliminate the vehicle safety inspection program because of budget constraints and concerns about program effectiveness. Currently, 26 states have a schedule for conducting safety inspections, but Pennsylvania is one of thirteen states that currently require all personal light duty vehicles to be inspected every year. The remaining states have completely eliminated safety inspection programs. However, as automobiles become safer, Pennsylvania legislators are now pushing to phase out the inspection program to reduce the costs of owning a vehicle. This study combines Pennsylvania vehicle registration data with two large samples of results from state safety inspections. We find that the state safety inspection fail rate for light-duty vehicles is 12–18%, well above the often-cited rate of 2%. Vehicles that are older than three years old or have more than about 30,000 miles can have much higher rates. When analyzing new vehicles, less than or equal to one year old, it is found that even these vehicles have a failure rate greater than zero. Furthermore, while the vehicle fleet appears to be getting safer over the past few years by improvements in technology or other external circumstances, the inspection failure rate does not appear to be trending toward zero in the near future. We also show that accurate inspection data is limited and often incorrectly analyzed. Lastly, the importance of vehicle maintenance over a vehicle’s lifetime is proven to be evident, since regular usage causes vehicles to deteriorate. We conclude that vehicle safety inspections should continue to be implemented in order to keep driving conditions safe.     

高速公路水泥混凝土路面的预防性养护

文章通过对水泥混凝土路面性能的调研,全面评估现有路面的使用性能,并分析路面板损坏的原因,从弯沉检测、路况调查、日常维修保养历史记录、技术性和经济性五方面综合考虑,提出预防性的养护对策。     

液化天然气(LNG)汽车日常检查方法及维护技术综述

天然气是一种优质的替代燃料,具有污染小、安全系数高、运行费用低等优点。天然气已经成为城市公共交通领域应用最为成功和广泛的车辆替代燃料技术,为推动交通运输行业的节能减排做出了显著的贡献。液化天然气汽车,作为天然气汽车的一种类型,与传统汽柴油车相比,液化天然气汽车安装了包括液化天然气气瓶、气管路及各种控制阀门和仪表在内的专用装置,在对液化天然气汽车进行日常检查时需要针对液化天然气汽车的专用装置进行重点检查。本文则针对液化天然气汽车的特点,对液化天然气汽车的正确使用方法、日常检查方法及维护技术要求、以及相关注意事项三个方面进行了解读,为指导液化天然气汽车进行日常检查与定期维护提供了技术参考。     

Modeling household interactions in daily in-home and out-of-home maintenance activity participation

The activity travel patterns of individuals in a household are inter-related, and the realistic modeling of activity-travel behavior requires that these interdependencies be explicitly accommodated. This paper examines household interactions impacting weekday in-home and out-of-home maintenance activity generation in active, nuclear family, households. The in-home maintenance activity generation is modeled by examining the duration invested by the male and female household heads in household chores using a seemingly unrelated regression modeling system. The out-of-home maintenance activity generation is modeled in terms of the decision of the household to undertake shopping, allocation of the task to one or both household heads, and the duration of shopping for the person(s) allocated the responsibility. A joint mixed-logit hazard-duration model structure is developed and applied to the modeling of out-of-home maintenance activity generation. The results indicate that traditional gender roles continue to exist and, in particular, non-working women are more likely to share a large burden of the household maintenance tasks. The model for out-of-home maintenance activity generation indicates that joint activity participation in the case of shopping is motivated by resource (automobiles) constraints. Finally, women who have a higher propensity to shop are also found to be inherently more efficient shoppers.     

Multi-level condition-based maintenance planning for railway infrastructures – A scenario-based chance-constrained approach

This paper develops a multi-level decision making approach for the optimal planning of maintenance operations of railway infrastructures, which are composed of multiple components divided into basic units for maintenance. Scenario-based chance-constrained Model Predictive Control (MPC) is used at the high level to determine an optimal long-term component-wise intervention plan for a railway infrastructure, and the Time Instant Optimization (TIO) approach is applied to transform the MPC optimization problem with both continuous and integer decision variables into a nonlinear continuous optimization problem. The middle-level problem determines the allocation of time slots for the maintenance interventions suggested at the high level to optimize the trade-off between traffic disruption and the setup cost of maintenance slots. Based on the high-level intervention plan, the low-level problem determines the optimal clustering of the basic units to be treated by a maintenance agent, subject to the time limit imposed by the maintenance slots. The proposed approach is applied to the optimal treatment of squats, with real data from the Eindhoven-Weert line in the Dutch railway network.     

Observing individual dynamic choices of activity chains from location-based crowdsourced data

The existing efforts on studying human mobility and activity using location-based crowdsourced data mainly focus on obtaining the activity chain pattern in a region at an aggregate level. To observe individual dynamic choices of activity chains, this paper presents a data-driven approach to estimating individual-specific activity chain set and corresponding choice probabilities for a given person over a 24-h period using crowdsourced data from location-based service apps. We detect an individual-specific stochastic activity set using a contextual-parcel data analysis. Based on the time geography theory, we refine a space-time bicone concept to construct an activity-travel space-time-state network from the stochastic activity set. These space-time bicone constraints define a set of potential activity choices to reduce the search space of activity location and duration choices. We construct an activity state transition graph from the space-time-state network and calculate a Markov matrix for activity choice probabilities. Furthermore, we calculate the probabilities of activity chain choices using the Markov matrix. We also visualize individual-specific activity chain set in a space-time-state network to show the dynamic choices of individual daily mobility and activity. We demonstrate the proposed approach through conducting numerical analyses using crowdsourced data from location-based service apps - Foursquare and Twitter to construct individual-specific activity choice sets and corresponding choice probabilities.     

The dynamic shortest path problem with time-dependent stochastic disruptions

The dynamic shortest path problem with time-dependent stochastic disruptions consists of finding a route with a minimum expected travel time from an origin to a destination using both historical and real-time information. The problem is formulated as a discrete time finite horizon Markov decision process and it is solved by a hybrid Approximate Dynamic Programming (ADP) algorithm with a clustering approach using a deterministic lookahead policy and value function approximation. The algorithm is tested on a number of network configurations which represent different network sizes and disruption levels. Computational results reveal that the proposed hybrid ADP algorithm provides high quality solutions with a reduced computational effort.     

漏磁检测技术在甬沪宁原油管线上的应用

为了在不停输的状态下对原油管道进行腐蚀检测,开发了漏磁管道内检测技术,并成功在多条管线上进行了应用。文中介绍了该检测技术在甬沪宁原油管道上试验过程及结果,并在检测结果中选取了4个腐蚀点进行了开挖检测,验证了检测数据的准确性。通过该次检测,管线全段共发现了缺陷点308处,其中3个缺陷点腐蚀比较严重,需要立即进行维修,该次检测结果为管道的后期维护提供了依据。最后通过对该次检测结果进行分析,确定了影响检测结果精度的因素,为后期检测技术的升级提供了参考。     

Transit stop inspection and maintenance scheduling: A GPU accelerated metaheuristics approach

Bus stops are integral elements of a transit system and as such, their efficient inspection and maintenance is required, for proper and attractive transit operations. Nevertheless, spatial dispersion and the extensive number of bus stops, even for mid-size transit systems, complicates scheduling of inspection and maintenance tasks. In this context, the problem of scheduling transit stop inspection and maintenance activities (TSIMP) by a two-stage optimization approach, is formulated and discussed. In particular, the first stage involves districting of the bus stop locations into areas of responsibility for different inspection and maintenance crews (IMCs), while in the second stage, determination of the sequence of bus stops to be visited by an IMC is modelled as a vehicle routing problem. Given the complexity of proposed optimization models, advanced versions of different metaheuristic algorithms (Harmony Search and Ant Colony Optimization) are exploited and assessed as possible options for solving these models. Furthermore, two variants of ACO are implemented herein; one implemented into a CPU parallel computing environment along with an accelerated one by means of general-purpose graphics processing unit (GPGPU) computing. The model and algorithms are applied to the Athens (Greece) bus system, whose extensive number of transit stops (over 7500) offers a real-world test bed for assessing the potential of the proposed modelling approach and solution algorithms. As it was shown for the test example examined, both algorithms managed to achieve optimized solutions for the problem at hand while there were fund robust with respect to their algorithmic parameters. Furthermore, the use of graphics processing units (GPU) managed to reduce of computational time required.