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Found 37 papers, 11 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we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment.
Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
However, existing approaches for decision-making in NSMDPs have two major shortcomings: first, they assume that the updated environmental dynamics at the current time are known (although future dynamics can change); and second, planning is largely pessimistic, i. e., the agent acts ``safely'' to account for the non-stationary evolution of the environment.
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.
On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data
The ability to accurately predict public transit ridership demand benefits passengers and transit agencies.
ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA
A key part of such tests is adversarial testing, in which the goal is to find scenarios that lead to failures of the given system.
Generative Anomaly Detection for Time Series Datasets
To this end, we propose a principled and comprehensive framework consisting of a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies.
ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria
More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in under-developed countries with low vaccination uptake.
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.
Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems
Our samplers of RNS and GBO sampled a higher percentage of high-risk scenes of 83% and 92%, compared to 56%, 66% and 71% of the grid, random and Halton samplers, respectively.
Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search
We find that PC-MCTS can achieve higher cumulative rewards than the policy in isolation under several environmental shifts while converging in significantly fewer iterations than pure MCTS.
Designing Decision Support Systems for Emergency Response: Challenges and Opportunities
Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities.
Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services
First, we show how crowdsourced reports, ground-truth historical data, and other relevant determinants such as traffic and weather can be used together in a Convolutional Neural Network (CNN) architecture for early detection of emergency incidents.
Efficient Out-of-Distribution Detection Using Latent Space of $β$-VAE for Cyber-Physical Systems
In this paper, we study this problem as a multi-labeled time series OOD detection problem over images, where the OOD is defined both sequentially across short time windows (change points) as well as across the training data distribution.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
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.
Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems
These statistical models are then used for proactive stationing which allocates first responders across the spatial area in order to reduce overall response time.
Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems
In this paper, we show how the deep-RBF network can be used for detecting anomalies in CPS regression tasks such as continuous steering predictions.
ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems
To help with this process, we provide a scenario modeling procedure that can use the prior distributions of the scenes and threat conditions to generate the data required for estimating the conditional relationships.
Robotics
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
Hierarchical Planning for Resource Allocation in Emergency Response Systems
We use the emergency response as a case study and show how a large resource allocation problem can be split into smaller problems.
Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion
Crowdsourcing platforms such as Waze provides an opportunity to develop a rapid, `proactive' approach to collect data about incidents through crowd-generated observational reports.
Ranked #1 on
Traffic Accident Detection
on custom
Designing Emergency Response Pipelines : Lessons and Challenges
Emergency response to incidents such as accidents, crimes, and fires is a major problem faced by communities.
A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management
In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems.
Workflow Automation for Cyber Physical System Development Processes
Development of Cyber Physical Systems (CPSs) requires close interaction between developers with expertise in many domains to achieve ever-increasing demands for improved performance, reduced cost, and more system autonomy.
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.
Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of $β$-VAE
We use the fact that compact latent space generated by an appropriately selected $\beta$-VAE will encode the information about these factors in a few latent variables, and that can be used for computationally inexpensive detection.
On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities
This is not a trivial planning problem --- a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem.
Data-Driven Optimization of Public Transit Schedule
To address these, this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems.
An Online Decision-Theoretic Pipeline for Responder Dispatch
We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch.
Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems
This architecture integrates the system to be assured with a safe controller and provides a decision logic to switch between the decisions of these controllers.
Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks
In this paper we work with components of backend data servers such as hard disks, that are subject to degradation.
DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion
We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network.
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.
