Haris Koutsopoulos

About

Haris Koutsopoulos is a distinguished professor and the Associate Chair for Undergraduate Studies in the Civil and Environmental Engineering Department at Northeastern University College of Engineering. His research focuses on urban transportation networks and informatics, public transportation operations, and mobility on demand. He is actively involved in the Transit Mobility Lab, which promotes innovation in public transportation through collaborations with institutions such as MIT and various transit authorities, including the Massachusetts Bay Transportation Authority, Transport for London, and the Washington Metropolitan Area Transit Authority. Koutsopoulos has made significant contributions to the field through his work on optimizing urban transit systems, developing models to improve maintenance strategies, and leveraging machine learning for energy efficiency and operational improvements. His research has earned him numerous awards, including the 2016 Traffic Simulation Lifetime Achievement Award from the Transportation Research Board, the 2011 IEEE ITS Outstanding Application Award, and the IBM Smarter Planet Award. He holds a PhD in Transportation Systems from MIT, earned in 1986, and has been recognized for his impactful research and leadership in transportation engineering.

Research topics

• Data Mining
• Computer Science
• Artificial Intelligence
• Machine Learning
• Mathematics
• Database

Selected publications

• Dynamic Origin-Destination Prediction in Urban Rail Systems: A Multi-Resolution Spatio-Temporal Deep Learning Approach

  IEEE Transactions on Intelligent Transportation Systems · 2021 · 108 citations

  Short-term demand predictions, typically defined as less than an hour into the future, are essential for implementing dynamic control strategies and providing useful customer information in transit applications. Knowing the expected demand enables transit operators to deploy real-time control strategies in advance of the demand surge, and minimize the impact of abnormalities on the service quality and passenger experience. One of the most useful applications of demand prediction models in transi…

  DOI

• Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model

  Transportation Research Part C Emerging Technologies · 2020 · 63 citations

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• Deep learning for short-term origin–destination passenger flow prediction under partial observability in urban railway systems

  Neural Computing and Applications · 2022 · 61 citations

  Senior authorCorresponding
  DOI

• Uncertainty Quantification of Spatiotemporal Travel Demand With Probabilistic Graph Neural Networks

  IEEE Transactions on Intelligent Transportation Systems · 2024-03-06 · 37 citations

  article

  Recent studies have significantly improved the prediction accuracy of travel demand using graph neural networks. However, these studies largely ignored uncertainty that inevitably exists in travel demand prediction. To fill this gap, this study proposes a framework of probabilistic graph neural networks (Prob-GNN) to quantify the spatiotemporal uncertainty of travel demand. This Prob-GNN framework is substantiated by deterministic and probabilistic assumptions, and empirically applied to the tas…

  PublisherDOI

• A Reverse Auction-Based Individualized Incentive System for Transit Mobility Management

  IEEE Transactions on Intelligent Transportation Systems · 2024-08-26 · 6 citations

  article

  Urban rail transit systems in many cities are experiencing crowding during peak periods due to rapid population growth. Incentive-based demand management strategies aim to better utilize the available capacity by shifting peak travel to off-peak periods. Various deployments have demonstrated the crowding-reduction potential of incentives in reducing crowding but they have also shown that such strategies are inefficient with many passengers receiving the incentives but relatively few contributing…

  PublisherDOI

Frequent coauthors

• Moshe Ben‐Akiva

  64 shared

• Jinhua Zhao

  Taiyuan University of Technology

  53 shared

• Zhenliang Ma

  40 shared

• Constantinos Antoniou

  Technical University of Munich

  38 shared

• Erik Jenelius

  35 shared

• Nigel H. M. Wilson

  34 shared

• Wilco Burghout

  26 shared

• Oded Cats

  22 shared

Labs

• Transit Mobility LabPI

Awards & honors

• 2026 Distinguished Faculty Award
• 2025 Faculty Research Team Award – iSUPER Impact Engine
• August-Wihelm Scheer Visiting Professor, TUM, Technical Univ…
• 2016 Traffic Simulation Lifetime Achievement Award, Transpor…
• 2011 IEEE ITS Outstanding Application Award

Similar researchers at Northeastern University

• Nian X. Sunh-75
• Yongmin Liuh-63
• Hanumant Singhh-56