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Relevant bibliographies by topics / Continuous Time Bayesian Networks

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Academic literature on the topic 'Continuous Time Bayesian Networks'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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Journal articles on the topic "Continuous Time Bayesian Networks"

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Bhattacharjya, Debarun, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush Varshney, and Dharmashankar Subramanian. "Event-Driven Continuous Time Bayesian Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3259–66. http://dx.doi.org/10.1609/aaai.v34i04.5725.

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We introduce a novel event-driven continuous time Bayesian network (ECTBN) representation to model situations where a system's state variables could be influenced by occurrences of events of various types. In this way, the model parameters and graphical structure capture not only potential “causal” dynamics of system evolution but also the influence of event occurrences that may be interventions. We propose a greedy search procedure for structure learning based on the BIC score for a special class of ECTBNs, showing that it is asymptotically consistent and also effective for limited data. We d

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Villa, Simone, and Fabio Stella. "Learning Continuous Time Bayesian Networks in Non-stationary Domains." Journal of Artificial Intelligence Research 57 (September 20, 2016): 1–37. http://dx.doi.org/10.1613/jair.5126.

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Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node to change over continuous time. Three settings are developed for learning non-stationary continuous time Bayesian networks from data: known transition times, known number of epochs and unknown number of epochs. A score function for each setting is derived and the corresponding learning algorithm is developed. A set of numerical experiments on synthetic data is used to compare the effectiveness of non-stationary continuous time Bayesian networks to that of non-stationary dynamic Bayesian net

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Xu, J., and C. R. Shelton. "Intrusion Detection using Continuous Time Bayesian Networks." Journal of Artificial Intelligence Research 39 (December 23, 2010): 745–74. http://dx.doi.org/10.1613/jair.3050.

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Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for both systems. We use anomaly detection, which identifies patterns not conforming to a historic norm. In both types of systems, the rates of change vary dramatically over time (due to burstiness) and over components (due to service difference). To efficiently model such systems, we use continuous time Bayesian networks (CTBNs) and avoid specifying a fixed upda

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Perreault, Logan, Monica Thornton, John Sheppard, and Joseph DeBruycker. "Disjunctive interaction in continuous time Bayesian networks." International Journal of Approximate Reasoning 90 (November 2017): 253–71. http://dx.doi.org/10.1016/j.ijar.2017.07.011.

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Perreault, Logan, and John Sheppard. "Compact structures for continuous time Bayesian networks." International Journal of Approximate Reasoning 109 (June 2019): 19–41. http://dx.doi.org/10.1016/j.ijar.2019.03.005.

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Stella, F., and Y. Amer. "Continuous time Bayesian network classifiers." Journal of Biomedical Informatics 45, no. 6 (2012): 1108–19. http://dx.doi.org/10.1016/j.jbi.2012.07.002.

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Linzner, Dominik, and Heinz Koeppl. "Active learning of continuous-time Bayesian networks through interventions*." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 12 (2021): 124001. http://dx.doi.org/10.1088/1742-5468/ac3908.

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Abstract We consider the problem of learning structures and parameters of continuous-time Bayesian networks (CTBNs) from time-course data under minimal experimental resources. In practice, the cost of generating experimental data poses a bottleneck, especially in the natural and social sciences. A popular approach to overcome this is Bayesian optimal experimental design (BOED). However, BOED becomes infeasible in high-dimensional settings, as it involves integration over all possible experimental outcomes. We propose a novel criterion for experimental design based on a variational approximatio

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Sturlaugson, Liessman, and John W. Sheppard. "Uncertain and negative evidence in continuous time Bayesian networks." International Journal of Approximate Reasoning 70 (March 2016): 99–122. http://dx.doi.org/10.1016/j.ijar.2015.12.013.

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Hosoda, Shion, Tsukasa Fukunaga, and Michiaki Hamada. "Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model." Bioinformatics 37, Supplement_1 (2021): i16—i24. http://dx.doi.org/10.1093/bioinformatics/btab287.

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Abstract Motivation Accumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka–Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions. Results In this study, we developed unsupervised learning-based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying micro

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Park, Cheol Young, Kathryn Blackmond Laskey, Paulo C. G. Costa, and Shou Matsumoto. "Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks." Applied Sciences 9, no. 10 (2019): 2055. http://dx.doi.org/10.3390/app9102055.

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Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks, in which all continuous random variables have Gaussian distributions and all children of continuous random variables must be continuous. Inference in CG networks can be NP-hard even for special-case structures, such as poly-trees, where inference in discrete Bayesian networks can be perfo

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Dissertations / Theses on the topic "Continuous Time Bayesian Networks"

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Nodelman, Uri D. "Continuous time bayesian networks /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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ACERBI, ENZO. "Continuos time Bayesian networks for gene networks reconstruction." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/52709.

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Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and improve the models' expressiveness. Continuous time Bayesian network

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CODECASA, DANIELE. "Continuous time bayesian network classifiers." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/80691.

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Streaming data are relevant to finance, computer science, and engineering, while they are becoming increasingly important to medicine and biology. Continuous time Bayesian networks are designed for analyzing efficiently multivariate streaming data, exploiting the conditional independencies in continuous time homogeneous Markov processes. Continuous time Bayesian network classifiers are a specialization of continuous time Bayesian networks designed for multivariate streaming data classification when time duration of events matters and the class occurs in the future. Continuous time Bayesian net

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VILLA, SIMONE. "Continuous Time Bayesian Networks for Reasoning and Decision Making in Finance." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/69953.

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L'analisi dell'enorme quantità di dati finanziari, messi a disposizione dai mercati elettronici, richiede lo sviluppo di nuovi modelli e tecniche per estrarre efficacemente la conoscenza da utilizzare in un processo decisionale informato. Lo scopo della tesi concerne l'introduzione di modelli grafici probabilistici utilizzati per il ragionamento e l'attività decisionale in tale contesto. Nella prima parte della tesi viene presentato un framework che utilizza le reti Bayesiane per effettuare l'analisi e l'ottimizzazione di portafoglio in maniera olistica. In particolare, esso sfrutta, da un l

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Fan, Yu. "Continuous time Bayesian Network approximate inference and social network applications." Diss., [Riverside, Calif.] : University of California, Riverside, 2009. http://proquest.umi.com/pqdweb?index=0&did=1957308751&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268330625&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2009.<br>Includes abstract. Title from first page of PDF file (viewed March 8, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 130-133). Also issued in print.

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GATTI, ELENA. "Graphical models for continuous time inference and decision making." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19575.

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Reasoning about evolution of system in time is both an important and challenging task. We are interested in probability distributions over time of events where often observations are irregularly spaced over time. Probabilistic models have been widely used to accomplish this task but they have some limits. Indeed, Hidden Markov Models and Dynamic Bayesian Networks in general require the specification of a time granularity between consecutive observations. This requirement leads to computationally inefficient learning and inference procedures when the adopted time granularity is finer than the

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Alharbi, Randa. "Bayesian inference for continuous time Markov chains." Thesis, University of Glasgow, 2019. http://theses.gla.ac.uk/40972/.

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Continuous time Markov chains (CTMCs) are a flexible class of stochastic models that have been employed in a wide range of applications from timing of computer protocols, through analysis of reliability in engineering, to models of biochemical networks in molecular biology. These models are defined as a state system with continuous time transitions between the states. Extensive work has been historically performed to enable convenient and flexible definition, simulation, and analysis of continuous time Markov chains. This thesis considers the problem of Bayesian parameter inference on these mo

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Parton, Alison. "Bayesian inference for continuous-time step-and-turn movement models." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/20124/.

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This thesis concerns the statistical modelling of animal movement paths given observed GPS locations. With observations being in discrete time, mechanistic models of movement are often formulated as such. This popularity remains despite an inability to compare analyses through scale invariance and common problems handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow, often excused by a difficulty in interpreting the ‘instantaneous’ parameters associated with a continuous-time model. The aim here was to bolster usage by

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Tucker, Allan Brice James. "The automatic explanation of Multivariate Time Series with large time lags." Thesis, Birkbeck (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246924.

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CRISTINI, ALESSANDRO. "Continuous-time spiking neural networks: paradigm and case studies." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2014. http://hdl.handle.net/2108/202297.

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In the last decades many neuron models have been proposed in order to emulate the spiking behavior of the cortical neurons, from the simplest Integrateand- Fire to the most bio-realistic Hodgkin-Huxley model. The choice of which model have to be used depends on the trade-off between bio-plausibility and computational cost, that may be related to the specific purpose. The modeling of a continuous-time spiking neural network is the main purpose of this thesis. The “continuous-time” term refers to the fact that a spike can occur at any given time, thus in order to do exact computations wi

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Books on the topic "Continuous Time Bayesian Networks"

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C, Merrill Walter, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Neuromorphic learning of continuous-valued mappings from noise-corrupted data: Application to real-time adaptive control. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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Butz, Martin V., and Esther F. Kutter. Top-Down Predictions Determine Perceptions. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0009.

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While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key t

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Xu, Yunfei, Sarat Dass, Tapabrata Maiti, and Jongeun Choi. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks: Online Environmental Field Reconstruction in Space and Time. Springer London, Limited, 2015.

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Xu, Yunfei, Choi Jongeun, Sarat Dass, and Tapabrata Maiti. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks: Online Environmental Field Reconstruction in Space and Time. Springer International Publishing AG, 2015.

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Chu, Yiren. A digitally programmable adaptive high-frequency CMOS continuous-time filter. 1994.

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Neuromorphic learning of continuous-valued mappings from noise-corrupted data: Application to real-time adaptive control. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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Ramsay, James. Curve registration. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.9.

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This article deals with curve registration, which refers to methods for aligning prominent features in a set of curves by transforming their abscissa variables. It first illustrates the concepts of amplitude and phase variation schematically and with real data before defining the time-warping functions and their functional inverse. It then describes the decomposition of total mean squared variation into separate amplitude and phase components, along with an R2 measure of the proportion of functional variation due to phase in a sample of curves. It also considers landmark registration, novel wa

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Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes

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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Graphs on structured spaces. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0010.

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This chapter moves beyond viewing nodes as homogeneous dots set on a plane. To introduce more complicated underlying space, multiplex networks (which are defined with layers of interaction on the same underlying node set) and temporal (time-dependent) networks are discussed. It shown that despite the much more complicated underlying space, many of the techniques developed in earlier chapters can be applied. Heterogeneous nodes are introduced as an extension of the stochastic block model for community structure, then extended using methods developed in earlier chapters to more general (continuo

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Stewart, Edmund. Conclusion. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198747260.003.0008.

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Tragedy’s dissemination may be said to be, in its nature, a complex and continuous process brought about through performance and re-performance at Panhellenic gatherings. Tragedy as a genre emerged from, and was part of, a Panhellenic song culture shaped by frequent travel, competition, and exchange. By the time something that could be termed tragedy appeared at the end of the sixth century, the Greeks were already connected by a complex system of overlapping networks. Despite the prominence of particular cities, such as Athens and Sparta, the Greeks possessed no one political or cultural cent

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Book chapters on the topic "Continuous Time Bayesian Networks"

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Liu, Manxia, Fabio Stella, Arjen Hommersom, and Peter J. F. Lucas. "Representing Hypoexponential Distributions in Continuous Time Bayesian Networks." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91479-4_47.

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van der Heijden, Maarten, and Arjen Hommersom. "Causal Independence Models for Continuous Time Bayesian Networks." In Probabilistic Graphical Models. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_33.

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Cerotti, Davide, and Daniele Codetta-Raiteri. "Mean Field Analysis for Continuous Time Bayesian Networks." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91632-3_12.

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Acerbi, Enzo, and Fabio Stella. "Continuous Time Bayesian Networks for Gene Network Reconstruction: A Comparative Study on Time Course Data." In Bioinformatics Research and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08171-7_16.

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Shi, Dongyu, and Jinyuan You. "Update Rules for Parameter Estimation in Continuous Time Bayesian Network." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_17.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Probabilistic Graphical Model for Continuous Variables." In Intelligent Control and Learning Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_14.

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AbstractMost of the sampled data in complex industrial processes are sequential in time. Therefore, the traditional BN learning mechanisms have limitations on the value of probability and cannot be applied to the time series. The model established in Chap. 10.1007/978-981-16-8044-1_13 is a graphical model similar to a Bayesian network, but its parameter learning method can only handle the discrete variables. This chapter aims at the probabilistic graphical model directly for the continuous process variables, which avoids the assumption of discrete or Gaussian distributions.

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Codecasa, Daniele, and Fabio Stella. "A Classification Based Scoring Function for Continuous Time Bayesian Network Classifiers." In New Frontiers in Mining Complex Patterns. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08407-7_3.

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Scutari, Marco, and Jean-Baptiste Denis. "The Continuous Case: Gaussian Bayesian Networks." In Bayesian Networks, 2nd ed. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429347436-2.

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Scutari, Marco, and Jean-Baptiste Denis. "Time Series: Dynamic Bayesian Networks." In Bayesian Networks, 2nd ed. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429347436-4.

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Liu, Manxia, Arjen Hommersom, Maarten van der Heijden, and Peter J. F. Lucas. "Hybrid Time Bayesian Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20807-7_34.

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Conference papers on the topic "Continuous Time Bayesian Networks"

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Jain, Swati, Francois Ayello, John A. Beavers, and Narasi Sridhar. "Probabilistic Model for Stress Corrosion Cracking of Underground Pipelines Using Bayesian Networks." In CORROSION 2013. NACE International, 2013. https://doi.org/10.5006/c2013-02616.

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Abstract Stress corrosion cracking (SCC) continues to be a safety concern, mainly because it can remain undetected before a major pipeline failure occurs. SCC processes involve complex interactions between metallurgy, stress, external soil environment, and electrolyte chemistry beneath disbonded coatings. For these reasons, assessing SCC failure probability at any given location on a pipeline is difficult. In addition, the uncertainty in data makes the prediction of SCC challenging. The complex interactions that affect SCC failure probability can be modeled using Bayesian network models. The B

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Horvath, Andras. "Stable Diffusion with Continuous-time Neural Networks." In 2023 18th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA). IEEE, 2023. http://dx.doi.org/10.1109/cnna60945.2023.10652658.

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Kumar, Amit, Nidhi Soni, Sulaymonov Dilshod Abduraximovich, Hassan M. Al-Jawahry, S. Jayasree, and Gnanajeyaraman Rajaram. "Time Management Recommendations with Bayesian Neural Networks: An Intelligent Assistant Approach." In 2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS). IEEE, 2024. http://dx.doi.org/10.1109/iicccs61609.2024.10763590.

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Blessy, Y. M., Raveendra N. Amarnath, D. Antony Joseph Rajan, R. Dillibai., S. T. Aarthy., and T. R. GaneshBabu. "Adaptive Real-Time Vehicle Usage Patterns with Cloud-Enabled Bayesian Networks." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961864.

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Yao, Honglei, Donglan Liu, Yingxian Chang, Xin Liu, and Chaofan Tang. "Time Series Anomaly Detection Based on Normalized Flow and Bayesian Networks." In 2024 IEEE 9th International Conference on Data Science in Cyberspace (DSC). IEEE, 2024. https://doi.org/10.1109/dsc63484.2024.00077.

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Gallagher, John C., and Eric T. Matson. "Analog Dopplegangers: Twinning with Deep Continuous-Time Recurrent Neural Networks." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651456.

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Villa, Simone, and Fabio Stella. "Learning Continuous Time Bayesian Networks in Non-stationary Domains." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/804.

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Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node in a continuous time Bayesian network to change over time. Structural learning of nonstationary continuous time Bayesian networks is developed under different knowledge settings. A macroeconomic dataset is used to assess the effectiveness of learning non-stationary continuous time Bayesian networks from real-world data.

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Größl, Martin. "Modeling dependable systems with continuous time Bayesian networks." In SAC 2015: Symposium on Applied Computing. ACM, 2015. http://dx.doi.org/10.1145/2695664.2695729.

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Schupbach, Jordan, Elliott Pryor, Kyle Webster, and John Sheppard. "Combining Dynamic Bayesian Networks and Continuous Time Bayesian Networks for Diagnostic and Prognostic Modeling." In 2022 IEEE AUTOTESTCON. IEEE, 2022. http://dx.doi.org/10.1109/autotestcon47462.2022.9984758.

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Perreault, Logan, Monica Thornton, Shane Strasser, and John W. Sheppard. "Deriving prognostic continuous time Bayesian networks from D-matrices." In 2015 IEEE AUTOTESTCON. IEEE, 2015. http://dx.doi.org/10.1109/autest.2015.7356482.

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Reports on the topic "Continuous Time Bayesian Networks"

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Roberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard, and Jameson Shannon. Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/44483.

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This study explored the use of artificial neural networks to predict UHPC compressive strengths given thermal history and key mix components. The model developed herein employs Bayesian variational inference using Monte Carlo dropout to convey prediction uncertainty using 735 datapoints on seven UHPC mixtures collected using a variety of techniques. Datapoints contained a measured compressive strength along with three curing inputs (specimen maturity, maximum temperature experienced during curing, time of maximum temperature) and five mixture inputs to distinguish each UHPC mixture (cement typ

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Shanahan, Erin, Rob Daley, Lydia Druin, Kristin Legg, and Sonya Daw. Status of whitebark pine in the Greater Yellowstone Ecosystem: A step-trend analysis with comparisons from 2004 to 2023. National Park Service, 2025. https://doi.org/10.36967/2313989.

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Whitebark pine (Pinus albicaulis) is a high-elevation conifer of the northern Rocky Mountains and the Pacific Northwest. This slow-growing, long-lived conifer influences critical ecosystem functions in subalpine environments, including snow capture, landscape stability, soil amelioration, and overall forest health and resilience. Moreover, its nutritious seeds feed the federally threatened grizzly bear (Ursus arctos), Clark’s nutcracker (Nucifraga columbiana), red squirrel (Tamiasciurus hudsonicus), and other species. However, whitebark pine is declining in high-elevation forests due to severa

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Pulugurtha, Srinivas S., Abimbola Ogungbire, and Chirag Akbari. Modeling and Evaluating Alternatives to Enhance Access to an Airport and Meet Future Expansion Needs. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2120.

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The continued growth of air travel calls for the incessant construction effort at many airports and their surroundings. Thus, there is a need to determine how airports can better manage existing infrastructure to accommodate this growth. This study, therefore, focuses on (1) investigating how changes in transportation infrastructure have affected travel time reliability (TTR) of the surrounding road network within the airport vicinity over time, and, (2) exploring selected unconventional intersection designs and proposing new inbound/outbound access routes from the nearby major roads to the ai

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