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

A comfort index for public transportation riders

Some municipalities cannot succeed at making its citizens use public transportation. The use of private cars is usually preferred and this leads to more congestion, longer commuting time, more fuel consumption and gasses’ emissions. Travel preferences of commuters are commonly estimated with discrete choice methods that consider their socioeconomic characteristics, along with some form of travel cost, failing to incorporate any measure of comfort. This research develops a standardized indicator of comfort for mass transportation systems. The functional form for a proposed index is developed over three key indicators: vehicle vibrations, air quality and noise levels, and the index is illustrated on a case study of the city of Montreal with comparisons to London and Santo Domingo (Dominican Republic). The index was developed in a way that allows an objective calculation, avoiding qualitative judgment from commuters, thus eliminating individuals’ subjectivity, and enabling comparisons among cities and modes. It was found that the automobile is the most comfortable mode, explaining its popularity. The data showed that, the number of stops is the most important factor affecting total vibration levels, and hence the comfort of buses and trains. Noise was found to be linked to vehicle’s vibrations. Newer metro cars in London and Dominican Republic showed better comfort levels, suburban trains in Montreal performed better and close to their counterparts in the United Kingdom. Express bus line was more comfortable than the local bus, performing better in the level of vibrations and noise, but not in terms of air quality

Modelling and optimisation of resource usage in an IoT enabled smart campus

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms

Modelling and optimisation of resource usage in an IoT enabled smart campus

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms

Investigation of bus passenger discomfort and driver fatigue: An electroencephalography (EEG) approach

Efforts to improve urban bus transport systems’ comfort and increase user satisfaction have been made for many years across the globe. Increasing bus users and reducing car users has an economic benefit. Whenever the urban bus share is larger than 25%, there are journey time savings due to lower congestion levels on the network. A driver’s loss of alertness due to fatigue has been recognised to be one of the major factors responsible for road accidents/crashes for many decades. Comfort and fatigue are psychophysiological phenomena. Objective measures of human psychological and physiological factors must be defined, investigated, and evaluated in order to have an indepth understanding of the cause-effect mechanisms regulating psychophysiological factors. Electroencephalography (EEG) developed as bio-sensor equipment to interpret and collate bioelectrical signals was used to gather the time-series quantitative data of urban bus passengers and HGV drivers. This study’s EEG data application was designed to link the brain activity dynamics to dynamic experimental design variables or tasks by correlating increased or decreased measured brain activity by using a baseline for comparisons. Two experiments were conducted in this study. The first sought to understand the influence of driving time and rest breaks on a driver’s psychophysiological response. Therefore, the EEG data was collected, categorised and grouped based on two hours of driving before a 30 minute break, two hours of driving after a 30 minute break and four hours of driving with no break. The Samn-Perelli seven-point scale of fatigue assessment was used to evaluate the influence of the duration of driving time on a driver’s performance decrements. The second experiment investigated bus passenger discomfort by examining experimental design stage-related changes in EEG measured by using a control experiment for comparison. Consequently, datasets in two stages were collected for each subject (passenger), including the stationary laboratory (control) and dynamic onboard bus environment experiments. A subjective evaluation of the average ride comfort on each stage of the experiments was conducted by using the recommended assessment scale of the International Standard ISO 2631-1. The ERP EEG oscillations were evaluated by decomposing the EEG signals into magnitudes and phase information, and then characterising their changes relative to the experimentally designed phases and variables. A two-way analysis of variance (ANOVA) was conducted to test the model’s predictor under different experimental conditions for passenger discomfort and driving fatigue experiments. Efforts to improve urban bus transport systems’ comfort and increase user satisfaction have been made for many years across the globe. Increasing bus users and reducing car users has an economic benefit. Whenever the urban bus share is larger than 25%, there are journey time savings due to lower congestion levels on the network. A driver’s loss of alertness due to fatigue has been recognised to be one of the major factors responsible for road accidents/crashes for many decades. Comfort and fatigue are psychophysiological phenomena. Objective measures of human psychological and physiological factors must be defined, investigated, and evaluated in order to have an indepth understanding of the cause-effect mechanisms regulating psychophysiological factors. Electroencephalography (EEG) developed as bio-sensor equipment to interpret and collate bioelectrical signals was used to gather the time-series quantitative data of urban bus passengers and HGV drivers. This study’s EEG data application was designed to link the brain activity dynamics to dynamic experimental design variables or tasks by correlating increased or decreased measured brain activity by using a baseline for comparisons. Two experiments were conducted in this study. The first sought to understand the influence of driving time and rest breaks on a driver’s psychophysiological response. Therefore, the EEG data was collected, categorised and grouped based on two hours of driving before a 30 minute break, two hours of driving after a 30 minute break and four hours of driving with no break. The Samn-Perelli seven-point scale of fatigue assessment was used to evaluate the influence of the duration of driving time on a driver’s performance decrements. The second experiment investigated bus passenger discomfort by examining experimental design stage-related changes in EEG measured by using a control experiment for comparison. Consequently, datasets in two stages were collected for each subject (passenger), including the stationary laboratory (control) and dynamic onboard bus environment experiments. A subjective evaluation of the average ride comfort on each stage of the experiments was conducted by using the recommended assessment scale of the International Standard ISO 2631-1. The ERP EEG oscillations were evaluated by decomposing the EEG signals into magnitudes and phase information, and then characterising their changes relative to the experimentally designed phases and variables. A two-way analysis of variance (ANOVA) was conducted to test the model’s predictor under different experimental conditions for passenger discomfort and driving fatigue experiments.The variability in the driver’s psychophysiological responses to the duration of driving occurs systematically. The effects appear to be progressive and aligned such that the driving performance was worst during the last 60 minutes of driving for four hours without a break, but better during the first 30 minutes. Data analysis also showed that a pronounced psychophysiological response exists relative to the influence of the road roughness characteristics, the passenger’s postures, and the bus type. Further analysis of passenger discomfort showed that passengers are more strained while in a standing posture than in a seated posture, irrespective of the bus type and the degree of the road’s roughness. The results indicated that passenger comfort deteriorates as the road roughness coefficient increases. Furthermore, the results demonstrated that female passengers express more discomfort/dissatisfaction than males under the same experimental conditions. Therefore, female passengers are more sensitive than males to a deviation from optimal comfort conditions.This study provides opportunities for future research applications of EEG in transport research studies. It also provides a platform for evaluating different Intelligent Transport System (ITS) technologies, particularly passenger’s reactions in autonomous vehicles