Aron Laszka is an Associate Professor in the College of Information Sciences and Technology at the Pennsylvania State University. Previously, he was an Assistant Professor at the University of Houston, a Research Assistant Professor at Vanderbilt University, and a Postdoctoral Scholar at the University of California, Berkeley.
His research interests revolve around artificial intelligence and cyber-physical systems, focusing on machine learning for decision making and optimization, with applications in smart and connected communities, critical infrastructure, and cybersecurity.
His research is funded by the National Science Foundation, the Department of Energy, the Department of Transportation, and other agencies.
Public bus transit systems provide critical transportation services for large sections of modern communities. On-time performance and maintaining the reliable quality of service is …
This project will develop and deploy a real-time, AI-driven decision support system that makes high-frequency bus service more reliable by anticipating and proactively mitigating delays, overcrowding, and service disruptions. Frequent, high-ridership bus routes are inherently unstable: when a bus falls behind, it encounters more waiting passengers and falls even further behind, until buses bunch together and leave long gaps in service, causing longer and less predictable waits, overcrowding, and missed transfers. The project focuses on WeGo Public Transit’s Route 55 Murfreesboro in Nashville, Tennessee, WeGo’s busiest route with nearly 5,000 weekday boardings, but also its least reliable one: more than 1 in 4 buses are either bunched or gapped, adding up to over 105 hours of excess waiting time for passengers every weekday.
Sequential decision-making in high-dimensional continuous action spaces, particularly in stochastic environments, faces significant computational challenges. We explore this …
This Civic Innovation Challenge (CIVIC) Stage 1 project will perform research to address transportation challenges faced by residents and workers in rural areas around industrial hubs. Rapid economic growth in these areas demands efficient public transit systems that can serve a geographically dispersed workforce with strict arrival times. However, existing solutions often struggle with these unique requirements, leading to traffic congestion, pollution, and limited access to essential services for residents. This research project will develop and deploy a novel multi-modal transit system for Blue Oval City (BOC), a new rural industrial hub in Stanton, Tennessee. The system will combine fixed-line buses with on-demand micro-transit services, addressing the challenges posed by the geographically dispersed workforce and strict arrival times.
This project reimagines public transportation for mid-sized U.S. cities through an AI-driven, multi-modal mobility framework that integrates fixed-line buses and on-demand microtransit into a single, responsive system. Led by the Chattanooga Area Regional Transportation Authority (CARTA) with technical contribution from the Pennsylvania State University, Vanderbilt University, and Cornell University, this $7 million initiative is designed to enhance infrastructure utilization, mobility access, and financial viability of public transit in Chattanooga while providing a national model for AI-enabled mobility resilience.
The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. …
New rideshare and shared mobility services have transformed urban mobility in recent years. Such services have the potential to improve efficiency and reduce costs by allowing …
While on-demand ride-sharing services have become popular in recent years, traditional on-demand transit services cannot be used by everyone, e.g., wheelchair-bound people. …
An emergency responder management (ERM) system dispatches responders, such as ambulances, when it receives requests for medical aid. ERM systems can also proactively reposition …
The increasing reliance of drivers on navigation applications has made transportation networks more susceptible to data-manipulation attacks by malicious actors. Adversaries may …
