Artificial Intelligence

Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets

Due to increasing concerns about environmental impact, operating costs, and energy security, public transit agencies are seeking to reduce their fuel use by employing electric vehicles (EVs). However, because of the high upfront cost of EVs, most …

Microtransit Solutions for Underserved Communities

Public transportation infrastructure is an essential component in cultivating equitable communities. However, public transit agencies have historically struggled to achieve this since they are often severely stressed in terms of resources as they have to make the trade-off between concentrating service into routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service.

Addressing Transit Accessibility Challenges due to COVID-19

The COVID-19 pandemic has not only disrupted the lives of millions but also created exigent operational and scheduling challenges for public transit agencies. Agencies are struggling to maintain transit accessibility with reduced resources, changing ridership patterns, vehicle capacity constraints due to social distancing, and reduced services due to driver unavailability.

Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning

Detection of malicious behavior is a fundamental problem in security. One of the major challenges in using detection systems in practice is in dealing with an overwhelming number of alerts that are triggered by normal behavior (the so-called false …

Detection and Mitigation of Attacks on Transportation Networks as a Multi-Stage Security Game

In recent years, state-of-the-art traffic-control devices have evolved from standalone hardware to networked smart devices. Smart traffic control enables operators to decrease traffic congestion and environmental impact by acquiring real-time traffic …

Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks

Traffic networks are one of the most critical infrastructures for any community. The increasing integration of smart and connected sensors in traffic networks provides researchers with unique opportunities to study the dynamics of this critical …

A Game-Theoretic Approach for Selecting Optimal Time-Dependent Thresholds for Anomaly Detection

Adversaries may cause significant damage to smart infrastructure using malicious attacks. To detect and mitigate these attacks before they can cause physical damage, operators can deploy anomaly detection systems (ADS), which can alarm operators to …

Database Audit Workload Prioritization via Game Theory

The quantity of personal data that is collected, stored, and subsequently processed continues to grow rapidly. Given its sensitivity, ensuring privacy protections has become a necessary component of database management. To enhance protection, a …

Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots

Learning enabled components (LECs) trained using data-driven algorithms are increasingly being used in autonomous robots commonly found in factories, hospitals, and educational laboratories. However, these LECs do not provide any safety guarantees, …

Data-Driven Energy Optimization for Multi-Modal Transit

The goal of this project is to develop a high-resolution system-level data capture and analysis framework to revolutionize the operational planning of a regional transportation authority, specifically the Chattanooga Area Regional Transportation Authority (CARTA).