Urban mobility data analysis in Montevideo, Uruguay

Transportation systems play a major role in modern urban contexts, where citizens are expected to travel in order to engage in social and economic activities. Understanding the interaction between citizens and transportation systems is crucial for policy-makers that aim to improve mobility in a city. Within the novel paradigm of smart cities, modern urban transportation systems incorporate technologies that generate huge volumes of data in real time, which can be processed to extract valuable information about the mobility of citizens. This thesis studies the public transportation system of Montevideo, Uruguay, following an urban data analysis approach. A thorough analysis of the transportation system and its usage is outlined, which combines several sources of urban data. The analyzed data includes the location of each bus of the transportation system as well as every ticket sold using smart cards during 2015, accounting for over 150 GB of raw data. Furthermore, origin-destination matrices, which describe mobility patterns in the city, are generated by processing geolocalized bus ticket sales data. For this purpose, a destination estimation algorithm is implemented following methodologies from the related literature. The computed results are compared to the ndings of a recent mobility survey, where the proposed approach arises as a viable alternative to obtain up-to-date mobility information. Finally, a visualization web application is presented, which allows conveying the aggregated information in an intuitive way to stakeholders

The Welfare Effects of Restricting Off-Highway Vehicle Access to Public Lands

Off-highway vehicle (OHV) use is a rapidly growing outdoor activity that results in a host of environmental and management problems. Federal agencies have been directed to develop travel management plans to improve recreation experiences, reduce social conflicts, and diminish environmental impacts of OHVs. We examine the effect of land access restrictions on the welfare of OHV enthusiasts in Utah using Murdock’s unobserved heterogeneity random utility model (Murdock 2006). Our models indicate that changing access to public lands from fully “open†to “limited†results in relatively small welfare losses, but that prohibiting access results in much larger welfare losses.off-highway vehicles, recreational access, unobserved heterogeneity, random utility model, Environmental Economics and Policy,

Accessibility and Mobility: Enriching and Transforming Existing Big Datasets for Public Transport Analysis

The central contribution of this thesis is to adapt and re-use public transport provider data to develop novel measures of accessibility to, and ridership of, public transport. It does so by benchmarking long term trends in provision and usage and then comparing these with the disruptive effects of the COVID-19 Pandemic and subsequent recovery from it. An initial assessment of provision and access leads to the creation of dynamic accessibility metrics from bus timetables, OpenStreetMap road and footpath data, NHS healthcare facilities locations and other retail locations. The methodology establishes travel times over a broad time window to derive average accessibility over any typical day rather than any specific time slice. The spatial, social and demographic implications of provision are analysed relative to the distribution of services. Individual level Smart Card Transaction records assembled by the English National Concessionary Travel Scheme (ENCTS) are then analysed in an ISO27001 secure data environment. They are used to investigate the mobility patters of eligible elderly or disabled transport users. Transaction data are linked to demographic registration data to examine use of public transport and to compare ridership patterns. Accessibility indices are calculated and compared against observed ridership patterns. Novel application of cohort survival analysis is used to model post-Pandemic recovery in ridership by different types of Scheme users. The results quantify the return to public transport by these groups following the Pandemic and assesses the geographic implications for public transport provision. Potentially disclosive transaction data for the study area are thus used for the first time to address issues of social exclusion and public transport provision for the elderly and disabled. As such, the thesis overcomes ethical issues of potential disclosure to liberate new and novel data resources pertaining to ridership. The results enable better understanding of issues of social equity that are of wide concern to all of the UK’s public transport authorities. The development of methodologies for matching public transport usage datasets to service datasets points towards the opportunities for redesigning data standards to ensure that these datasets can be linked together in future and over long periods of time

Disruption analytics in urban metro systems with large-scale automated data

Urban metro systems are frequently affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. Such disruptions give rise to delays, congestion and inconvenience for public transport users, which in turn, lead to a wider range of negative impacts on the social economy and wellbeing. This PhD thesis aims to improve our understanding of disruption impacts and improve the ability of metro operators to detect and manage disruptions by using large-scale automated data. The crucial precondition of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. In pursuit of this goal, the thesis develops statistical models to detect disruptions via deviations in trains’ headways relative to their regular services. Our method is a unique contribution in the sense that it is based on automated vehicle location data (data-driven) and the probabilistic framework is effective to detect any type of service interruptions, including minor delays that last just a few minutes. As an important research outcome, the thesis delivers novel analyses of the propagation progress of disruptions along metro lines, thus enabling us to distinguish primary and secondary disruptions as well as recovery interventions performed by operators. The other part of the thesis provides new insights for quantifying disruption impacts and measuring metro vulnerability. One of our key messages is that in metro systems there are factors influencing both the occurrence of disruptions and their outcomes. With such confounding factors, we show that causal inference is a powerful tool to estimate unbiased impacts on passenger demand and journey time, which is also capable of quantifying the spatial-temporal propagation of disruption impacts within metro networks. The causal inference approaches are applied to empirical studies based on the Hong Kong Mass Transit Railway (MTR). Our conclusions can assist researchers and practitioners in two applications: (i) the evaluation of metro performance such as service reliability, system vulnerability and resilience, and (ii) the management of future disruptions.Open Acces

Forecasting monthly airline passenger numbers with small datasets using feature engineering and a modified principal component analysis

In this study, a machine learning approach based on time series models, different feature engineering, feature extraction, and feature derivation is proposed to improve air passenger forecasting. Different types of datasets were created to extract new features from the core data. An experiment was undertaken with artificial neural networks to test the performance of neurons in the hidden layer, to optimise the dimensions of all layers and to obtain an optimal choice of connection weights – thus the nonlinear optimisation problem could be solved directly. A method of tuning deep learning models using H2O (which is a feature-rich, open source machine learning platform known for its R and Spark integration and its ease of use) is also proposed, where the trained network model is built from samples of selected features from the dataset in order to ensure diversity of the samples and to improve training. A successful application of deep learning requires setting numerous parameters in order to achieve greater model accuracy. The number of hidden layers and the number of neurons, are key parameters in each layer of such a network. Hyper-parameter, grid search, and random hyper-parameter approaches aid in setting these important parameters. Moreover, a new ensemble strategy is suggested that shows potential to optimise parameter settings and hence save more computational resources throughout the tuning process of the models. The main objective, besides improving the performance metric, is to obtain a distribution on some hold-out datasets that resemble the original distribution of the training data. Particular attention is focused on creating a modified version of Principal Component Analysis (PCA) using a different correlation matrix – obtained by a different correlation coefficient based on kinetic energy to derive new features. The data were collected from several airline datasets to build a deep prediction model for forecasting airline passenger numbers. Preliminary experiments show that fine-tuning provides an efficient approach for tuning the ultimate number of hidden layers and the number of neurons in each layer when compared with the grid search method. Similarly, the results show that the modified version of PCA is more effective in data dimension reduction, classes reparability, and classification accuracy than using traditional PCA.</div

Kelionių grandinėmis pagrįsto susisiekimo poreikių modelio kūrimas ir taikymas miestų susisiekimo tinklo planavimui

This thesis is devoted to the analysis of the advanced and innovative tour-based travel demand modelling approach. Tour-based models explicitly recognise traffic as a derived demand for undertaking activities between homes and destinations. Travel demand of urban residents is modelled as trip sequences, which allows precise modelling of trip origin and destination points. The tour-based approach is deemed as a key step forwards towards even more complex agent-based modelling systems. The thesis is structured around three main chapters that can be summarised succinctly as a revision of the state of the practice and research, description of empirical research of travel behaviour, and tour-based model development. The 1st chapter revises the current state of practice and the research on travel demand modelling. All the building blocks that comprise transport models are discussed, and this lays the theoretical foundation for the following chapters. 1st chapter also gives a thorough comparison of trip-based and tour-based model-ling approaches and presents modelling environment. The 2nd chapter defines the process of conducting an empirical research of the travel behaviour patterns of urban residents. The 2nd chapter defines survey methodology and important mobility parameters such as activity sequences and their probabilities of homogeneous urban population segments. The outputs from the 2nd chapter are not only important and interesting on their own, but they also flow into the final third part of the work. The final 3rd chapter defines tour-based travel demand model development steps and showcases their practical application to the real-world scenario. Demand model quality assessment efforts and results are presented and discussed together with necessary explanations for significant deviations from reality. The resulting model is applied to investigate the performance of Siaurine Street in Vilnius, which is to be built in the coming years. At the very end of 3rd chapter a compre-hensive urban travel demand modelling framework is formulated and serves as a best practice guide. General conclusions summarises the whole study. These are followed by an extensive list of references that were mentioned or relied upon to some extent in the work. Finally, separate lists of scientific publications and conference presen-tations conclude the thesis. Overall, there have been five scientific articles published on the topic of the thesis. Four articles were published in scientific journals that are referenced in Clarivate Analytics Web of Science database, and one article was published in a scientific journal that is referenced in other databases

Behavioral Approach to Estimation of Smart Card Holders Socio-Demographic Characteristics in a Public Transportation System

RÉSUMÉ Les systèmes de collecte automatisée des titres de transport sont utilisés dans de nombreuses villes, le titre de transport est le plus souvent stocké sur une carte à puce (CAP). Ils génèrent quotidiennement d’importants volumes de données liées à la mobilité des individus. Il devient très intéressant de disposer de méthodes pour pouvoir utiliser ces données car elles présentent le triple avantage d’être exhaustives, longitudinales et directement liées au réseau de transport en commun. En effet, tous les passagers doivent valider leur embarquement (à l’exception des fraudeurs qui s’octroient s’affranchissent de ce devoir), ces données sont récoltées tous les jours de l’année pour l’intégralité du réseau et elles sont liées à un véhicule et une ligne de bus. Il y a de nombreuses applications développées à ce jour: reconstitution des chaînes de déplacements, étude des typologies de déplacements sur le réseaux, étude de la loyauté des usagers, validation des enquêtes de déplacements, identification des maxima de charges sur chaque ligne, étude de l’adéquation de l’offre à la demande, etc.----------ABSTRACT Automated Fare Collection (AFC) systems such as smart cards are being used in many different cities and countries. The AFC systems leverage large volume of data related to person’s mobility and it becomes very interesting to develop methods to use these data to complement other data sources. They present four main advantages, they are longitudinal, they concern every public transit user (within the limitation of the penetration rate of the smart card and the policy around shared smart card ownership), they are passively leveraged and they are directly related to the public transit structure. There are already many applications such as processing the trip chain, study of public transit users loyalty and behaviour, validation of travel surveys etc

Passengers’ choices in multimodal public transport systems : A study of revealed behaviour and measurement methods

The concept of individual choice is a fundamental aspect when explaining and anticipating behavioural interactions with, and responses to, static and dynamic travel conditions in public transport (PT) systems. However, the empirical rounding of existing models used for forecasting travel demand, which itself is a result of a multitude of individual choices, is often insufficient in terms of detail and accuracy. This thesis explores three aspects, or themes, of PT trips – waiting times, general door-to-door path preferences, with a special emphasis on access and egress trip legs, and service reliability – in order to increase knowledge about how PT passengers interact with PT systems. Using detailed spatiotemporal empirical data from a dedicated survey app and PT fare card transactions, possible cross-sectional relationships between travel conditions and waiting times are analysed, where degrees of mental effort are gauged by an information acquisition proxy. Preferences for complete door-todoorpaths are examined by estimation of full path choice models. Finally, longitudinal effects of changing service reliability are analysed using a biennial panel data approach. The constituent studies conclude that there are otherexplanatory factors than headway that explain waiting times on first boarding stops of PT trips and that possession of knowledge of exact departure times reduces mean waiting times. However, this information factor is not evidentin full path choice, where time and effort-related preferences dominate with a consistent individual preference factor. Finally, a statistically significant on-average adaption to changing service reliability is individual-specific andnon-symmetrical depending on the direction of reliability change, where a relatively large portion of the affected individuals do not appear to respond to small-scale perturbations of reliability while others do, all other thingsbeing equal