Development of a new approach for predicting tram track degradation based on passenger ride/comfort data

These days tram as a type of the public transport system has become popular because of its attractive features such as road usage efficiency, low emission of pollutants, reduction in traffic congestion and efficiency in capital costs and maintenance expenses compared to private cars. For the case study, the Melbourne tram network, which is the longest tram network in the world, has been targeted. Melbourne tram system consists of 493 trams, 24 routes, and 1,763 tram stops. According to the operator of the Melbourne tram network, the total number of patronage in 2017-2018 was 206.3 million. In parallel with the annual increase in tram demand and patronage, tram infrastructure systems need to bear more stresses and traffic pressure. Track degradation is a common problem in the area of tram track infrastructure. One of the main aspects of track degradation is the presence of irregularity in track geometric parameters. In order to deal with degradation problems, tram track infrastructure maintenance management systems have been developed for design and implementation of maintenance works and renewal activities. Track degradation prediction models are the core and the main part of these management systems. Without accurately predicting the future condition of tram tracks, designing and providing preventive maintenance strategies are not feasible. In this research, the collected data which cover six sequential years (2010 to 2015) have been analysed and influencing parameters in tram track degradation have been identified. Gauge and twist were identified as the influencing track geometry parameters in the tram track degradation. Besides that, track surface and rail support as structural parameters were identified as significant parameters in prediction of future track geometry parameters and consequently tram track degradation. In order to develop tram track degradation prediction models and according to the successful experience of the previous studies, three types of prediction models including Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest Regression (RFR) models have been created. According to the results, RFR models provide better predictions in terms of the performance indicators including the coefficient of determination and Root Mean Squared Error (RMSE) compared to the ANN and SVM models. In this research, based on the Melbourne tram track dataset, a new track degradation index has been proposed. Track degradation indices can be used as an indicator of rail condition concerning the risk of damage or failure over a period of time. The index can be applied in establishing a sustainable tram track maintenance management system. The new index composed of two main parts including the mean value of the geometry deviation and the average differential geometry deviation. The proposed index has been compared with three major track geometry degradation indices. For this purpose, the predictability performance of the indices has been considered. In this regard, the Pearson correlation analysis was applied to previous and current values of the indices. According to the results, the correlation coefficient of the proposed index was higher than the other indices. The finding of the evaluation presented that the proposed index can be used as an effective measure for the assessment of the geometric condition of tram tracks. In this research, a new approach has been proposed to predict the tram track degradation were which is cost-effective and can be carried out repeatedly without imposing delay to tram services. Conventional approaches are mainly based on the previous track geometry parameters which have been discussed in this research. In the new approach, passenger ride comfort data or acceleration data has been used to predict the future condition of track geometry parameters which has been represented by the tram track degradation index. For developing the degradation prediction models, the previous models which have been used to predict the degradation based on the track geometry parameters were applied. The future degradation index has been targeted as the target variable and acceleration parameter besides the structural parameters have been used as the explanatory variables. According to the results of the evaluation, the RFR model can predict the future degradation index with approximately 10 percent higher R2 and 9 percent lower prediction error compared to other developed models. In this research two methods for predicting the future tram track degradation index, first was the method based on the previous track geometry parameters and the second was the method based on the acceleration data, have been presented. According to the results of the degradation index prediction based on the previous track geometry parameters, RMSE was 0.35 and R2 value was 0.95. On the other hand, for the prediction based on the acceleration data, RMSE was 1.04 and R2 value was 0.74. The comparison of these methods shows that although the prediction error has been increased and R2 value has been decreased in the latest method, the values of the performance indicators are still in acceptable ranges. These results imply that the prediction of tram track degradation based on the acceleration data can be considered as a reliable method along with conventional tram track degradation prediction method for maintaining tram tracks. The proposed method can provide more predictions of potential future faults by reducing inspection costs and inspection intervals

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Development of a tram track degradation prediction model based on the acceleration data

Although vibration is considered as one of the important factors in passenger ride comfort, yet it has not been applied for predicting tram track degradation in tram network. Rail track degradation prediction models form an essential part of the rail infrastructure maintenance management systems. Vibration can be measured by acceleration signals. The acceleration signal is derived from the movement of railway vehicles on rail structure. In this study, vehicle acceleration data along with other track structural parameters have been used to predict tram track degradation index which can be considered as a representative of tram track quality. The index used in this study has been developed based on a mixture of tram track geometry deviations of several years. Three types of machine learning models have been employed for creating the prediction models. In this study, Melbourne tram network data have been applied for developing as well as predicting the degradation index. Based on the evaluation results, the proposed random forest regression model made more accurate predictions on track degradation compared to other developed models. The results of this study can help tram track managers to deploy cost-effective maintenance strategies by applying vehicle acceleration data in their decision-making processes

A review on existing sensors and devices for inspecting railway infrastructure

This paper presents a review of sensors and inspection devices employed to inspect railway defects and track geometry irregularities. Inspection of rail defects is an important task in railway infrastructure management systems, and data derived from inspections can feed railway degradation prediction models. These models are utilised for predicting potential defects and implementing preventive maintenance activities. In this paper, different sensors for detecting rail defects and track irregularities are presented, and various inspection devices which utilise these sensors are investigated. In addition, the classification of the sensors and inspection devices based on their capabilities and specifications is carried out, which has not been fully addressed in previous studies. Non-Destructive Testing (NDT) sensors, cameras and accelerometers are among sensors investigated here. Correspondingly, trolleys, Condition Monitoring Systems (CMS), hi-rail vehicles and Track Recording Vehicles (TRV) are among major inspection devices that their capabilities are studied. Furthermore, the application of new devices, including smartphones and drones, in railway inspection and their potential capabilities are discussed. The review of previous and recent approaches shows that CMSs are more cost-effective and accessible than other railway inspection methods, as they can be carried out on in-service vehicles an unlimited number of times without disruption to normal train traffic. In addition, recently smartphones as a compact inspection device with a variety of sensors are employed to measure acceleration data, which can be considered as an indicator of rail track condition

Using appropriate speed tables regarding the speed limit of streets

In the present decade along the increasing trend of using private vehicles, calming the local streets and residential areas has been important for local authorities. There are many unsuccessful experiences of traffic calming implementations because of lacking knowledge and engineered assessment before implementing them. Considering the design speed of traffic, calming measure is an essential factor to employ these measures. Design speed of different size of speed humps is investigated in previous studies because of its circular shape but for speed tables it is unknown. In this research the design speeds of two common speed tables in the city of Tehran have been examined, 6.5 and 8.5 m speed table. For calculating the design speed of the speed tables, we asked 220 drivers to participate in our experiment by installing a GPS tracker in their vehicles and encouraging them to drive normally. Crossing speeds over 6.5 and 8.5 m speed tables have been analysed by collecting totally 220 samples. We pick out 100 correct samples for each speed table and the 85th percentile speed has been calculated for them, consequently the results of 85th percentile calculation of the crossing speeds have been proposed as the design speeds. For 6.5 m speed table, design speed is calculated 41.5 km/h and for 8.5 m speed table, design speed is calculated 47.5 km/h. the comparison of recent findings and past finding of 9.5 m speed table which is used in Denmark with a design speed of 80 km/h reveals that 1 m increasing with the length of a 6.5 speed table plateau will result 3 km/h increase in its design speed. The findings of this research can help traffic calming experts to take in consideration of the relation between speed table physical characteristics and its design speed. Furthermore by finding the design speed of speed tables, we can choose suitable speed tables for streets with different speed limits according to the design speed of speed tables

Integration of genetic algorithm and support vector machine to predict rail track degradation

Gradual deviation in track gauge of tram systems resulted from tram traffic is unavoidable. Tram gauge deviation is considered as an important parameter in poor ride quality and the risk of train derailment. In order to decrease the potential problems associated with excessive gauge deviation, implementation of preventive maintenance activities is inevitable. Preventive maintenance operation is a key factor in development of sustainable rail transport infrastructure. Track degradation prediction modelling is the basic prerequisite for developing efficient preventive maintenance strategies of a tram system. In this study, the data sets of Melbourne tram network is used and straight rail tracks sections are examined. Two model types including plain Support Vector Machine (SVM) and SVM optimised by Genetic Algorithm (GA-SVM) have been applied to the case study data. Two assessment indexes including Mean Squared Error (MSE) and the coefficient of determination (R2) are employed to evaluate the performance of the proposed models. Based on the results, GA-SVM model produces more accurate outcomes than plain SVM model

Developing a web-based advisory expert system for implementing traffic calming strategies

Lack of traffic safety has become a serious issue in residential areas. In this paper, a web-based advisory expert system for the purpose of applying traffic calming strategies on residential streets is described because there currently lacks a structured framework for the implementation of such strategies. Developing an expert system can assist and advise engineers for dealing with traffic safety problems. This expert system is developed to fill the gap between the traffic safety experts and people who seek to employ traffic calming strategies including decision makers, engineers, and students. In order to build the expert system, examining sources related to traffic calming studies as well as interviewing with domain experts have been carried out. The system includes above 150 rules and 200 images for different types of measures. The system has three main functions including classifying traffic calming measures, prioritizing traffic calming strategies, and presenting solutions for different traffic safety problems. Verifying, validating processes, and comparing the system with similar works have shown that the system is consistent and acceptable for practical uses. Finally, some recommendations for improving the system are presented

Development of Random Forests Regression Model to Predict Track Degradation Index: Melbourne Case Study

In rail infrastructure maintenance management systems, Track Degradation Index (TDI) is considered as a representative of quality of rail tracks. This index is usually developed based on the deviation rate or standard deviation of track geometry parameters. In this regard, prediction of future TDI is an important task as it can be employed to determine when and where maintenance and renewal activities must be deployed. In this study, a track geometry data set from Melbourne tram network has been used as the case study and gauge deviation parameter is selected as the main parameter to develop TDI. For prediction of the future TDI, Random Forests (RF) model as a Machine Learning (ML) model is used to predict the future TDI of the data set. Since TDI is a continuous variable, Random Forests Regression (RFR) model is applied. In this study, RF model has added two algorithms to the basic Decision Trees (DT) model including bagging and random subspace method. These algorithms can reduce the overfitting problem and over-focus on special features. Based on the results of this study, adjusted R2 value of the proposed prediction model is 0.96, which demonstrates that the model has the satisfying performance in predicting the TDI

Rail Degradation Predication: Melbourne Tram System Case Study

Nowadays, tram as an accessible and convenient mode of public transport is implemented and used in different cities. Due to high frequency of accelerations and decelerations along their routes and sharing the route with other vehicles, the rate of degradation of tram tracks (light rail) is different from the degradation rate of train tracks (heavy rail). In this paper, track gauge deviation as an indicator of irregularities on the rail-wheel contact surface is used for developing the track degradation model. Data set used in this study includes the curve sections of Melbourne tram network and divided into repaired and unrepaired segments. For data analysis more than 13 km of curved tracks are examined. Annual tonnage, previous gauge deviation and track structural properties like track surface, rail support, rail profile and gauge deviation are considered as the influencing variables on the future gauge deviation. Two different models including a regression model and an Artificial Neural Networks (ANN) model have been developed for predicting tram track gauge deviation. According to the results, the performances of the regression models are not very different from the ANN models. The determination coefficients of the selected models are approximately 0.8 and higher