Search Results for author:
Found 20 papers, 5 papers with code
Forecasting and Mitigating Disruptions in Public Bus Transit Services
Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies.
Decision Making in Non-Stationary Environments with Policy-Augmented Search
In this paper, we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment.
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks
The increasing reliance of drivers on navigation applications has made transportation networks more susceptible to data-manipulation attacks by malicious actors.
Task Grouping for Automated Multi-Task Machine Learning via Task Affinity Prediction
We identify inherent task features and STL characteristics that can help us to predict whether a group of tasks should be learned together using MTL or if they should be learned independently using STL.
Artificial Intelligence for Smart Transportation
Second, we provide an overview of how AI can aid decision-making with a focus on transportation.
Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows
In smaller problem instances, the baseline approach is as competitive as our framework.
Principled Data-Driven Decision Support for Cyber-Forensic Investigations
A recent study demonstrated that data-driven decision support, based on a dataset of prior incidents, can provide state-of-the-art prioritization.
Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit
Vehicle routing problems (VRPs) can be divided into two major categories: offline VRPs, which consider a given set of trip requests to be served, and online VRPs, which consider requests as they arrive in real-time.
An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services
Accounting for stochastic requests while optimizing a non-myopic utility function is computationally challenging; indeed, the action space for such a problem is intractably large in practice.
Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire
We model and solve the proactive control problem as a Markov decision process and introduce an integrated testbed for spatio-temporal wildfire propagation and proactive power-system operation.
Transit-Gym: A Simulation and Evaluation Engine for Analysis of Bus Transit Systems
Public-transit systems face a number of operational challenges: (a) changing ridership patterns requiring optimization of fixed line services, (b) optimizing vehicle-to-trip assignments to reduce maintenance and operation codes, and (c) ensuring equitable and fair coverage to areas with low ridership.
Efficient Data Management for Intelligent Urban Mobility Systems
Modern intelligent urban mobility applications are underpinned by large-scale, multivariate, spatiotemporal data streams.
Computers and Society
Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets
In this paper, we present a novel framework for the data-driven prediction of route-level energy use for mixed-vehicle transit fleets, which we evaluate using data collected from the bus fleet of CARTA, the public transit authority of Chattanooga, TN.
Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service
Our results show that the proposed algorithms are scalable and can reduce energy use and, hence, environmental impact and operational costs.
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense
In this paper, we propose a multi-agent partially-observable Markov Decision Process model of MTD and formulate a two-player general-sum game between the adversary and the defender.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning
We then use these in a double-oracle framework to obtain an approximate equilibrium of the game, which in turn yields a robust stochastic policy for the defender.
Hey Google, What Exactly Do Your Security Patches Tell Us? A Large-Scale Empirical Study on Android Patched Vulnerabilities
In this paper, we perform a comprehensive study of 2, 470 patched Android vulnerabilities that we collect from different data sources such as Android security bulletins, CVEDetails, Qualcomm Code Aurora, AOSP Git repository, and Linux Patchwork.
Cryptography and Security
Get Your Workload in Order: Game Theoretic Prioritization of Database Auditing
For enhancing the privacy protections of databases, where the increasing amount of detailed personal data is stored and processed, multiple mechanisms have been developed, such as audit logging and alert triggers, which notify administrators about suspicious activities; however, the two main limitations in common are: 1) the volume of such alerts is often substantially greater than the capabilities of resource-constrained organizations, and 2) strategic attackers may disguise their actions or carefully choosing which records they touch, making incompetent the statistical detection models.
Designing Secure Ethereum Smart Contracts: A Finite State Machine Based Approach
The adoption of blockchain-based distributed computation platforms is growing fast.
Cryptography and Security Formal Languages and Automata Theory Software Engineering
Optimal Detection of Faulty Traffic Sensors Used in Route Planning
Erroneous data can adversely affect applications such as route planning, and can cause increased travel time.
