Related papers: Primary User Traffic Classification in Dynamic Spe…
Accurate roadway travel-time prediction is foundational to transportation systems analysis, yet widespread reliance on either data-intensive congestion models or overly na\"ive heuristics limits scalability and practical adoption in…
One primary focus of next generation wireless communication networks is the millimeterwave (mmWave) spectrum, typically considered in the 30 GHz to 300 GHz frequency range. Despite their promise of high data rates, mmWaves suffer from…
Following [21, 23], the present work investigates a new relative entropy-regularized algorithm for solving the optimal transport on a graph problem within the randomized shortest paths formalism. More precisely, a unit flow is injected into…
The analysis of GPS trajectories is a well-studied problem in Urban Computing and has been used to track people. Analyzing people mobility and identifying the transportation mode used by them is essential for cities that want to reduce…
Traffic flow forecasting is challenging due to the intricate spatio-temporal correlations in traffic flow data. Existing Transformer-based methods usually treat traffic flow forecasting as multivariate time series (MTS) forecasting.…
Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes in ISPs to firewall and malware detection in clients. Classical machine learning algorithms and deep learning models…
Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffic flows and short-term prediction of congestion occurrence due to rush-hour or…
We present a novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning. We predict both the low-level information (future trajectories) as well as the high-level…
Accurate mobile traffic forecast is important for efficient network planning and operations. However, existing traffic forecasting models have high complexity, making the forecasting process slow and costly. In this paper, we analyze some…
This paper studies optimal bandwidth and power allocation in a cognitive radio network where multiple secondary users (SUs) share the licensed spectrum of a primary user (PU) under fading channels using the frequency division multiple…
We describe and validate a novel data-driven approach to the real time detection and classification of traffic anomalies based on the identification of atypical fluctuations in the relationship between density and flow. For aggregated data…
With the global energy transition and the rapid penetration of electric vehicles (EVs), the widening travel cost gap between EVs and gasoline vehicles (GVs) increasingly affects commuters' route choices and may reshape urban congestion…
Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, traditional measurements based on mean first passage time are not…
Over the past decade the rate of care unit (CU) use in the United States has been increasing. With an aging population and ever-growing demand for medical care, effective management of patients' transitions among different care facilities…
This paper considers joint active user detection (AUD) and channel estimation (CE) for massive connectivity scenarios with sporadic traffic. The state-of-art method under a Bayesian framework to perform joint AUD and CE in such scenarios is…
An approach for mapping an electric vehicle EV driver s travel time constraints and risk taking behavior to real time routing in a probabilistic time-dependent or stochastic network is proposed in this paper. The proposed approach is based…
Many functionals of interest in statistics and machine learning can be written as minimizers of expected loss functions. Such functionals are called $M$-estimands, and can be estimated by $M$-estimators -- minimizers of empirical average…
Rapid urbanization places increasing stress on already burdened transportation systems, resulting in delays and poor levels of service. Billions of spatiotemporal call detail records (CDRs) collected from mobile devices create new…
With the development of information technology, requirements for data flow have become diverse. When multi-type data flow (MDF) is used, games, videos, calls, etc. are all requirements. There may be a constant switch between these…
Massive machine type communication (mMTC) has been identified as an important use case in Beyond 5G networks and future massive Internet of Things (IoT). However, for the massive multiple access in mMTC, there is a serious access preamble…