Affiliated Researchers: Transportation Networks

Affiliates are Penn Staters who have a connection to or an interest in the work of the Institute of Energy and the Environment.

Displaying 10 search result(s) for Transportation Networks.

Vikash Gayah


Professor, Civil and Environmental Engineering
Top research keywords: Macroscopic Fundamental Diagram, Traffic Signal, Safety Performance, Urban Network, Traffic Network

Mohamad Darayi


Associate Professor of Systems Engineering, Engineering Division (Great Valley)
Top research keywords: Network Components, Vulnerability, Disruptions, Multi-industry, Freight Transportation

Ilgin Guler


Associate Professor, Civil and Environmental Engineering
Top research keywords: Connected Vehicles, Traffic Signal, Signalized Intersection, Pre-signal, Control Algorithm

Aron Laszka


Assistant Professor, College of Information Sciences and Technology
Top research keywords: Blockchain, Attackers, Attacker, Smart Contract, Adversary

Mallika Bose


Professor, Landscape Architecture
Top research keywords: India, Transport, United States, Low Income, Focus Group

Jose Ventura


Professor, Harold and Inge Marcus Department of Industrial and Manufacturing Engineering
Top research keywords: Supplier Selection, Programming Model, Refuelling Station, Transportation Cost, Price-dependent Demand

Manzhu Yu


Associate Professor, Geography
Top research keywords: Dust Storm, Particular Matter 2.5, Machine Learning, Cloud Computing, Deep Learning

Anirudh Subramanyam


Assistant Professor, Harold and Inge Marcus Department of Industrial and Manufacturing Engineering
Top research keywords: Vehicle Routing, Optimization Problem, Realization, Routing Problem, Two-stage Robust Optimization

Xianbiao Hu


Associate Professor, Civil and Environmental Engineering
Top research keywords: Connected Vehicles, Autonomous Vehicles, Case Study, Multi Agent Systems, Global Positioning System

Kostas Papakonstantinou


Associate Professor, Civil and Environmental Engineering
Top research keywords: Partially Observable Markov Decision Process, Reinforcement Learning, Deep Reinforcement Learning (deep RL), Lifecycle, Markov Decision Process