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The development of a public transport accessibility index for older travellers using total travel time is not a subject of frequent discussion. This study proposes a public transport accessibility index (EPTAI) which considers older peoples' travel time and the populations of the second-smallest statistical areas according to census data. EPTAI identifies the level of access of the elderly to public transport (train, tram, and bus) in an urban area. The time-based EPTAI includes different trip purposes, including shopping trips (trips to shopping centres), medical trips (travel to healthcare centres), education trips (travel to education centres), and recreation trips (e.g., restaurants, parks, and cafes). The developed index is validated using statistical validation methods, including Pearson's chi-square, likelihood ratio, linear-by-linear association, Cramer's V, contingency coefficient, and phi. In addition, the performance of the developed index is compared with household survey data and the public transport accessibility level (PTAL). The results indicate that older adults' public transport access varies depending on travel time, population density, and travel destination. The proposed index can be used for future planning/expansion and modification of public transport networks in urban and regional areas to meet the travel demands of older travellers., Author(s): Kaniz Fatima (corresponding author) [1]; Sara Moridpour [1]; Tayebeh Saghapour [2] 1. Introduction The global number of older persons is increasing rapidly compared with younger age groups [1]. According [...]

Nowadays, road maintenance is a crucial planning task for all road authorities across the world, making them spend vast amounts of money on identification and rehabilitation programmes every year. Thus, many researchers, road engineers, and decision-makers have turned to a system called pavement management system, the critical step of which is pavement inspection. So far, several methods have been proposed for pavement distress data collection and evaluation. These methods have also evolved with the advancement of technology and intelligent transportation systems. Today, researchers have acknowledged autonomous vehicles as a sophisticated tool monitoring right of way for appropriate operating; however, little attention has been paid to using collected pavement condition data for pavement management. This study reviews the technologies and algorithms proposed so far to investigate the feasibility of using the data regularly collected by these vehicles to evaluate pavement conditions. We hope our paper paves the way for further research. [ABSTRACT FROM AUTHOR]

Pavement management systems (PMSs) have a primary role in determining pavement condition monitoring and maintenance strategies. Moreover, many researchers have focused on pavement condition evaluation tools, starting with data collection, followed by processing, analyzing, and ultimately reaching practical conclusions regarding pavement condition. The analysis step is considered an essential part of the pavement condition evaluation process, as it focuses on the tools used to find the most accurate results. On the other hand, prediction models are important tools used in pavement condition evaluation to determine the current and future performance of the road pavement. Therefore, pavement condition prediction has an effective and significant role in identifying the appropriate maintenance techniques and treatment processes. Moreover, pavement performance indices are commonly used as key indicators to describe the condition of pavement surfaces and the level of pavement degradation. This paper systematically summarizes the existing performance prediction models conducted to predict the condition of asphalt pavement degradation using pavement condition indexes (PCI) and the international roughness index (IRI). These performance indices are commonly used in pavement monitoring to accurately evaluate the health status of pavement. The paper also identifies and summarizes the most influencing parameters in road pavement condition prediction models and presents the strength and weaknesses of each prediction model. The findings show that most previous studies preferred machine learning approaches and artificial neural networks forecasting and estimating the road pavement conditions because of their ability to deal with massive data, their higher accuracy, and them being worthwhile in solving time-series problems., Author(s): Amir Shtayat (corresponding author) [1]; Sara Moridpour [1]; Berthold Best [2]; Shahriar Rumi [1] 1. Introduction Road infrastructure facilities have essential and active roles in the advancement of cities [...]

Author(s): Sara Moridpour (corresponding author) [1]; Xiaobo Qu [2]; Nirajan Shiwakoti [3]; Samiul Hasan [4] Transport infrastructure is the lifeline of modern economy and significantly contributes to economic growth and [...]

In recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence (AI). With the introduction of big data by stationary sensors or probe vehicle data and the development of new AI models in the last few decades, this research area has expanded extensively. Traffic congestion prediction, especially short-term traffic congestion prediction is made by evaluating different traffic parameters. Most of the researches focus on historical data in forecasting traffic congestion. However, a few articles made real-time traffic congestion prediction. This paper systematically summarises the existing research conducted by applying the various methodologies of AI, notably different machine learning models. The paper accumulates the models under respective branches of AI, and the strength and weaknesses of the models are summarised., Author(s): Mahmuda Akhtar (corresponding author) [1]; Sara Moridpour [1] 1. Introduction Artificial intelligence (AI) is the most important branch of computer science in this era of big data. AI was [...]

The growth acceleration in the older population has been observed in many countries which creates a significant challenge to transportation. Public transport is an essential component of most people’s lives. This specific group of population growth will require special attention to accessibility and mobility issues in combination with the transport system which undoubtedly can play a key role to support ageing. Good health, quality of life and independence are fundamental values for the life as well as mobility. For people such as senior citizens, public transport needs to be accessible and affordable in order to provide them with the opportunity to fulfil their medical, shopping and recreational needs. Providing access to transport for people with age 65 & over is not a marginal problem and it is expected to grow in the future. The most important concern in accessibility is the relatively short distance and high frequency movements from a permanent home. This study will identify the travel pattern & mode for elderly commuters in greater Melbourne, Australia. The study will also suggest strategies to improve the use of public transport system to promote ageing in place.

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.

The article entitled 'Modeling access to public transport in urban areas' [1] contains similar material from a published journal article 'Public Transport Accessibility in Metropolitan Areas: A New Approach Incorporating [...]

Traffic and transportation science consists of a broad range of topics. Traditional mathematical models and analysis techniques are not capable of translating many real-life problems in traffic and transportation engineering. [...]

Tram is classified as a light rail mode of transportation. Tram tracks experience high acceleration and deceleration forces of locomotives and wagons within their service life and also share their route with other vehicles. This results in higher rates of degradation in tram tracks compared to the degradation rate in heavy rail tracks. In this research, gauge deviation is employed as a representative of track geometry irregularities for the predication of the tram track degradation. Data sets used in this research were sourced from Melbourne's tram system. For model development, the data of approximately 250 km of tram tracks are used. Two different models including a regression model and an Artificial Neural Networks (ANN) model have been applied for predicting tram track gauge deviation. According to the results, the performances of the regression models are similar to the ANN models. The determination coefficients of the developed models are above 0.7., 1. Introduction Nowadays, tram system as an energy-efficient mode of public transit is developed and used in different places to facilitate the movement of people within suburbs, cities, and even [...]

Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work. With the restrictions on financial support, the rail transport authorities are in pursuit of improved modern methods, which can provide a precise prediction of rail maintenance timeframe. The expectation from such a method is to develop models to minimise the human error that is strongly related to manual prediction. Such models will help rail transport authorities in understanding how the track degradation occurs at different conditions (e.g., rail type, rail profile) over time. They need a well-structured technique to identify the precise time when rail tracks fail to minimise the maintenance cost/time. The rail track characteristics that have been collected over the years will be used in developing a degradation prediction model for rail tracks. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use the data in prediction model development. An accurate model can play a key role in the estimation of the long-term behaviour of rail tracks. Accurate models can increase the efficiency of maintenance activities and decrease the cost of maintenance in long-term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curves and straight sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model. The results from the developed model show that it is capable of predicting the gauge values with [R.sup.2] of 0.6 and 0.78 for curves and straights, respectively., 1. Introduction Modern transport organizations have shifted their focus from construction and expansion of the transport infrastructure towards how to intelligently maintaining them. This has taken place due to many [...]