Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations

The rapid adoption of wireless sensor devices has made it easier to record location information of people in a variety of spaces (e.g., exhibition halls). Location information is often aggregated due to privacy and/or cost concerns. The aggregated data we use as input consist of the numbers of incoming and outgoing people at each location and at each time step. Since the aggregated data lack tracking information of individuals, determining the flow of people between locations is not straightforward. In this article, we address the problem of inferring latent people flows, that is, transition populations between locations, from just aggregated population data gathered from observed locations. Existing models assume that everyone is always in one of the observed locations at every time step; this, however, is an unrealistic assumption, because we do not always have a large enough number of sensor devices to cover the large-scale spaces targeted. To overcome this drawback, we propose a probabilistic model with flow conservation constraints that incorporate travel duration distributions between observed locations. To handle noisy settings, we adopt noisy observation models for the numbers of incoming and outgoing people, where the noise is regarded as a factor that may disturb flow conservation, e.g., people may appear in or disappear from the predefined space of interest. We develop an approximate expectation-maximization (EM) algorithm that simultaneously estimates transition populations and model parameters. Our experiments demonstrate the effectiveness of the proposed model on real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City

Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information

The movements of individuals within and among cities influence key aspects of our society, such as the objective and subjective well-being, the diffusion of innovations, the spreading of epidemics, and the quality of the environment. For this reason, there is increasing interest around the challenging problem of flow generation, which consists in generating the flows between a set of geographic locations, given the characteristics of the locations and without any information about the real flows. Existing solutions to flow generation are mainly based on mechanistic approaches, such as the gravity model and the radiation model, which suffer from underfitting and overdispersion, neglect important variables such as land use and the transportation network, and cannot describe non-linear relationships between these variables. In this paper, we propose the Multi-Feature Deep Gravity (MFDG) model as an effective solution to flow generation. On the one hand, the MFDG model exploits a large number of variables (e.g., characteristics of land use and the road network; transport, food, and health facilities) extracted from voluntary geographic information data (OpenStreetMap). On the other hand, our model exploits deep neural networks to describe complex non-linear relationships between those variables. Our experiments, conducted on commuting flows in England, show that the MFDG model achieves a significant increase in the performance (up to 250\% for highly populated areas) than mechanistic models that do not use deep neural networks, or that do not exploit geographic voluntary data. Our work presents a precise definition of the flow generation problem, which is a novel task for the deep learning community working with spatio-temporal data, and proposes a deep neural network model that significantly outperforms current state-of-the-art statistical models

Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility

Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility

Dynamics and critical behaviour of neuronal cultures grown on topographical patterns with fractal structure

Treballs Finals de Màster en Física dels Sistemes Complexos i Biofísica, Facultat de Física, Universitat de Barcelona. Curs: 2022-2023. Tutor: Jordi Soriano FraderaNeuronal cultures are an excellent experimental tool to study the collective behaviour of neuronal ensembles, providing information on the principles of synaptic functioning and propagation. However, neurons cultured on flat surfaces present limitations in terms of their functionality, as they exhibit a synchronous dynamic behaviour that differs from the much richer repertoire of activity of the brain. In order to address this limitation and help developing better in vitro tools to model the brain, here we studied the capacity to break off synchrony by modulating the spatial arrangement of neurons in the substrate they grow. For that, we designed polydimethylsiloxane (PDMS) topographical patterns with fractal geometry and used them as the substrate to grow neurons, with the goal to break the isotropy in connectivity and enrich dynamics. Neuronal activity was recorded with calcium fluorescence imaging and data analysed in the context of criticality, which was inspired by recent findings suggesting that a rich structural connectivity in the brain is behind its functioning at the edge of criticality. We observed that, first, neurons cultured on fractal patterns exhibited richer and more complex dynamics as compared to standard cultures; and, second, that an analysis of the data using the renormalisation group approach, revealed the presence of scale invariance and typical features of systems poised at criticality. Our study is a multidisciplinary endeavour that combined experimental, theoretical and data analysis aspects to validate the hypothesis of the existence of a self-organised criticality in living neuronal networks, from cultures up to the brain

Linear and nonlinear approaches to unravel dynamics and connectivity in neuronal cultures

[eng] In the present thesis, we propose to explore neuronal circuits at the mesoscale, an approach in which one monitors small populations of few thousand neurons and concentrates in the emergence of collective behavior. In our case, we carried out such an exploration both experimentally and numerically, and by adopting an analysis perspective centered on time series analysis and dynamical systems. Experimentally, we used neuronal cultures and prepared more than 200 of them, which were monitored using fluorescence calcium imaging. By adjusting the experimental conditions, we could set two basic arrangements of neurons, namely homogeneous and aggregated. In the experiments, we carried out two major explorations, namely development and disintegration. In the former we investigated changes in network behavior as it matured; in the latter we applied a drug that reduced neuronal interconnectivity. All the subsequent analyses and modeling along the thesis are based on these experimental data. Numerically, the thesis comprised two aspects. The first one was oriented towards a simulation of neuronal connectivity and dynamics. The second one was oriented towards the development of linear and nonlinear analysis tools to unravel dynamic and connectivity aspects of the measured experimental networks. For the first aspect, we developed a sophisticated software package to simulate single neuronal dynamics using a quadratic integrate–and–fire model with adaptation and depression. This model was plug into a synthetic graph in which the nodes of the network are neurons, and the edges connections. The graph was created using spatial embedding and realistic biology. We carried out hundreds of simulations in which we tuned the density of neurons, their spatial arrangement and the characteristics of the fluorescence signal. As a key result, we observed that homogeneous networks required a substantial number of neurons to fire and exhibit collective dynamics, and that the presence of aggregation significantly reduced the number of required neurons. For the second aspect, data analysis, we analyzed experiments and simulations to tackle three major aspects: network dynamics reconstruction using linear descriptions, dynamics reconstruction using nonlinear descriptors, and the assessment of neuronal connectivity from solely activity data. For the linear study, we analyzed all experiments using the power spectrum density (PSD), and observed that it was sufficiently good to describe the development of the network or its disintegration. PSD also allowed us to distinguish between healthy and unhealthy networks, and revealed dynamical heterogeneities across the network. For the nonlinear study, we used techniques in the context of recurrence plots. We first characterized the embedding dimension m and the time delay δ for each experiment, built the respective recurrence plots, and extracted key information of the dynamics of the system through different descriptors. Experimental results were contrasted with numerical simulations. After analyzing about 400 time series, we concluded that the degree of dynamical complexity in neuronal cultures changes both during development and disintegration. We also observed that the healthier the culture, the higher its dynamic complexity. Finally, for the reconstruction study, we first used numerical simulations to determine the best measure of ‘statistical interdependence’ among any two neurons, and took Generalized Transfer Entropy. We then analyzed the experimental data. We concluded that young cultures have a weak connectivity that increases along maturation. Aggregation increases average connectivity, and more interesting, also the assortativity, i.e. the tendency of highly connected nodes to connect with other highly connected node. In turn, this assortativity may delineates important aspects of the dynamics of the network. Overall, the results show that spatial arrangement and neuronal dynamics are able to shape a very rich repertoire of dynamical states of varying complexity.[cat] L’habilitat dels teixits neuronals de processar i transmetre informació de forma eficient depèn de les propietats dinàmiques intrínseques de les neurones i de la connectivitat entre elles. La present tesi proposa explorar diferents tècniques experimentals i de simulació per analitzar la dinàmica i connectivitat de xarxes neuronals corticals de rata embrionària. Experimentalment, la gravació de l’activitat espontània d’una població de neurones en cultiu, mitjançant una càmera ràpida i tècniques de fluorescència, possibilita el seguiment de forma controlada de l’activitat individual de cada neurona, així com la modificació de la seva connectivitat. En conjunt, aquestes eines permeten estudiar el comportament col.lectiu emergent de la població neuronal. Amb l’objectiu de simular els patrons observats en el laboratori, hem implementat un model mètric aleatori de creixement neuronal per simular la xarxa física de connexions entre neurones, i un model quadràtic d’integració i dispar amb adaptació i depressió per modelar l’ampli espectre de dinàmiques neuronals amb un cost computacional reduït. Hem caracteritzat la dinàmica global i individual de les neurones i l’hem correlacionat amb la seva estructura subjacent mitjançant tècniques lineals i no–lineals de series temporals. L’anàlisi espectral ens ha possibilitat la descripció del desenvolupament i els canvis en connectivitat en els cultius, així com la diferenciació entre cultius sans dels patològics. La reconstrucció de la dinàmica subjacent mitjançant mètodes d’incrustació i l’ús de gràfics de recurrència ens ha permès detectar diferents transicions dinàmiques amb el corresponent guany o pèrdua de la complexitat i riquesa dinàmica del cultiu durant els diferents estudis experimentals. Finalment, a fi de reconstruir la connectivitat interna hem testejat, mitjançant simulacions, diferents quantificadors per mesurar la dependència estadística entre neurona i neurona, seleccionant finalment el mètode de transferència d’entropia gereralitzada. Seguidament, hem procedit a caracteritzar les xarxes amb diferents paràmetres. Malgrat presentar certs tres de xarxes tipus ‘petit món’, els nostres cultius mostren una distribució de grau ‘exponencial’ o ‘esbiaixada’ per, respectivament, cultius joves i madurs. Addicionalment, hem observat que les xarxes homogènies presenten la propietat de disassortativitat, mentre que xarxes amb un creixent nivell d’agregació espaial presenten assortativitat. Aquesta propietat impacta fortament en la transmissió, resistència i sincronització de la xarxa

Spatio-temporal traffic anomaly detection for urban networks

Urban road networks are often affected by disruptions such as accidents and roadworks, giving rise to congestion and delays, which can, in turn, create a wide range of negative impacts to the economy, environment, safety and security. Accurate detection of the onset of traffic anomalies, specifically Recurrent Congestion (RC) and Nonrecurrent Congestion (NRC) in the traffic networks, is an important ITS function to facilitate proactive intervention measures to reduce the level of severity of congestion. A substantial body of literature is dedicated to models with varying levels of complexity that attempt to identify such anomalies. Given the complexity of the problem, however, very less effort is dedicated to the development of methods that attempt to detect traffic anomalies using spatio-temporal features. Driven both by the recent advances in deep learning techniques and the development of Traffic Incident Management Systems (TIMS), the aim of this research is to develop novel traffic anomaly detection models that can incorporate both spatial and temporal traffic information to detect traffic anomalies at a network level. This thesis first reviews the state of the art in traffic anomaly detection techniques, including the existing methods and emerging machine learning and deep learning methods, before identifying the gaps in the current understanding of traffic anomaly and its detection. One of the problems in terms of adapting the deep learning models to traffic anomaly detection is the translation of time series traffic data from multiple locations to the format necessary for the deep learning model to learn the spatial and temporal features effectively. To address this challenging problem and build a systematic traffic anomaly detection method at a network level, this thesis proposes a methodological framework consisting of (a) the translation layer (which is designed to translate the time series traffic data from multiple locations over the road network into a desired format with spatial and temporal features), (b) detection methods and (c) localisation. This methodological framework is subsequently tested for early RC detection and NRC detection. Three translation layers including connectivity matrix, geographical grid translation and spatial temporal translation are presented and evaluated for both RC and NRC detection. The early RC detection approach is a deep learning based method that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The NRC detection, on the other hand, involves only the application of the CNN. The performance of the proposed approach is compared against other conventional congestion detection methods, using a comprehensive evaluation framework that includes metrics such as detection rates and false positive rates, and the sensitivity analysis of time windows as well as prediction horizons. The conventional congestion detection methods used for the comparison include Multilayer Perceptron, Random Forest and Gradient Boost Classifier, all of which are commonly used in the literature. Real-world traffic data from the City of Bath are used for the comparative analysis of RC, while traffic data in conjunction with incident data extracted from Central London are used for NRC detection. The results show that while the connectivity matrix may be capable of extracting features of a small network, the increased sparsity in the matrix in a large network reduces its effectiveness in feature learning compared to geographical grid translation. The results also indicate that the proposed deep learning method demonstrates superior detection accuracy compared to alternative methods and that it can detect recurrent congestion as early as one hour ahead with acceptable accuracy. The proposed method is capable of being implemented within a real-world ITS system making use of traffic sensor data, thereby providing a practically useful tool for road network managers to manage traffic proactively. In addition, the results demonstrate that a deep learning-based approach may improve the accuracy of incident detection and locate traffic anomalies precisely, especially in a large urban network. Finally, the framework is further tested for robustness in terms of network topology, sensor faults and missing data. The robustness analysis demonstrates that the proposed traffic anomaly detection approaches are transferable to different sizes of road networks, and that they are robust in the presence of sensor faults and missing data.Open Acces