Our research team developed a microscopic traffic simulator of the LAX airport using machine learning, optimization and commercial traffic simulation software. This simulator is currently used by the LAX traffic agency in their decision-making process. 
Our team proposed OECC, an algorithm for Anomaly and Out-of-Distribution Detection using Deep Neural networks. OECC achieves state-of-the-art (SOTA) results in both image and text classification tasks. 
Published in Transportation Research Part C: Emerging Technologies, 2021
Abstract: Traffic congestion constitutes a major problem in urban areas. Trucks contribute to congestion and have a negative impact on the environment due to their size, slower dynamics and higher fuel consumption. The individual routing decisions made by truck drivers do not lead to system optimum operations and contribute to traffic imbalances especially in places where the volume of trucks is relatively high. In this paper, we design a coordination mechanism for truck drivers that uses pricing-and-routing schemes that can help alleviate traffic congestion in a general transportation network. We consider the user heterogeneity in Value-Of-Time (VOT) by adopting a multi-class model with stochastic Origin–Destination (OD) demands for the truck drivers. The main characteristic of the mechanism is that the coordinator asks the truck drivers to declare their desired OD pair and pick their individual VOT from a set of N available options, and guarantees that the resulting pricing-and-routing scheme is Pareto-improving, i.e. every truck driver will be better-off compared to the User Equilibrium (UE) and that every truck driver will have an incentive to truthfully declare his/her VOT, while leading to a revenue-neutral (budget balanced) on average mechanism. This approach enables us to design personalized (VOT-based) pricing-and-routing schemes. We show that the Optimum Pricing Scheme (OPS) can be calculated by solving a nonconvex optimization problem. To improve computational efficiency, we propose an Approximately Optimum Pricing Scheme (AOPS) and prove that it satisfies the aforementioned properties. Both pricing-and-routing schemes are compared to the Congestion Pricing with Uniform Revenue Refunding (CPURR) scheme through extensive simulation experiments where it is shown that OPS and AOPS achieve a much lower expected total travel time and expected total monetary cost for the users compared to the CPURR scheme, without negatively affecting the rest of the network. These results demonstrate the efficiency of personalized (VOT-based) pricing-and-routing schemes.
Recommended citation: A-A. Papadopoulos, I. Kordonis, M. Dessouky, P. Ioannou. (2021). "Personalized Pareto-Improving Pricing-and-Routing Schemes for Near-Optimum Freight Routing: An Alternative Approach to Congestion Pricing." Transportation Research Part C: Emerging Technologies. vol. 125. https://www.sciencedirect.com/science/article/abs/pii/S0968090X2100036X?via%3Dihub
Published in Neurocomputing, 2021
Abstract: Deep neural networks have achieved great success in classification tasks during the last years. However, one major problem to the path towards artificial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classification algorithms assume that all classes are known prior to the training stage. In this work, we propose a methodology for training a neural network that allows it to efficiently detect out-of-distribution (OOD) examples without compromising much of its classification accuracy on the test examples from known classes. We propose a novel loss function that gives rise to a novel method, Outlier Exposure with Confidence Control (OECC), which achieves superior results in OOD detection with OE both on image and text classification tasks without requiring access to OOD samples. Additionally, we experimentally show that the combination of OECC with state-of-the-art post-training OOD detection methods, like the Mahalanobis Detector (MD) and the Gramian Matrices (GM) methods, further improves their performance in the OOD detection task, demonstrating the potential of combining training and post-training methods for OOD detection.
Recommended citation: A-A. Papadopoulos, M.R. Rajati, N. Shaikh, J. Wang. (2021). "Outlier Exposure with Confidence Control for Out-of-Distribution Detection." Neurocomputing. 441, 138-150. https://www.sciencedirect.com/science/article/abs/pii/S0925231221002393?via%3Dihub
Published in IEEE Transactions on Intelligent Transportation Systems, 2021
Abstract: Traffic simulators can capture the complex dynamics of road networks thus are widely used to understand the behavior of the traffic system and predict the effects of transportation technologies, events and new policy changes. In order to build a traffic simulation model that can accurately capture the real traffic conditions, the Origin-Destination (OD) matrix is needed to be fed into the simulator as an input, which is however generally difficult to acquire. In this paper, we propose a neural network model for estimating the OD matrix that excites the microscopic simulation model for complex traffic networks to reproduce real world traffic flows using only the flow rate information of a small subset of links in the traffic network, which is relatively easy to collect. An optimization method is used to generate the training dataset of the neural network. Combining the OD matrix estimation model, the training dataset generation method, and the microscopic traffic simulator, an integrated traffic simulation-prediction system is developed to predict the behavior of the transportation system. We test the proposed system on the road network of the central terminal area (CTA) of the Los Angeles International Airport (LAX), which demonstrates that the integrated traffic simulation-prediction system can reproduce the real world traffic flow with low relative root mean square errors especially for high volume links and can be used to simulate the behavior of real world scenarios.
Recommended citation: Y. Zhang, A-A. Papadopoulos, P. Chen, F. Alasiri, T. Yuan, J. Zhou, P. A. Ioannou. (2021). "Integrated Traffic Simulation-Prediction System using Neural Networks with Application to the Los Angeles International Airport Road Network." IEEE Transactions on Inteligent Transportation Systems. (under review). https://arxiv.org/abs/2008.01902
Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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