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

• User-centric transit data analytics for inference, prediction, and optimization

  Elsevier eBooks · 2026-01-01

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  PublisherDOI

• Data-driven traffic prediction and simulation models: mobility patterns using machine learning techniques

  Elsevier eBooks · 2026-01-01

  book-chapter
  PublisherDOI

• List of contributors

  Elsevier eBooks · 2026-01-01

  book-chapter
  PublisherDOI

• Design of Revenue Service Adjustments for Urban Rail System Maintenance

  Transportation Research Record Journal of the Transportation Research Board · 2026-02-17

  articleOpen accessSenior author

  Urban railway systems require regular maintenance to uphold safe and efficient operations. System operators are sometimes forced to perform this maintenance during revenue service hours. The resulting changes in revenue service are called a “revenue service adjustment,” or RSA. Because RSAs typically feature planning horizons of months or even years, operators have an opportunity to design them in ways that minimize the level-of-service (LOS) impacts for passengers. This paper presents a framework for operators to consider LOS impacts early in the RSA planning process. First, a taxonomy of service delivery strategies is developed, where various strategies can be characterized by typical LOS impacts and operational considerations to efficiently identify options to consider. Second, a method is presented to develop optimal service plans to deliver the various strategies. Third, the distribution of LOS impacts on various groups of passengers is found for each service plan. Finally, considerations related to work planning and productivity are quantified and balanced against LOS impacts. The framework is demonstrated on a real-world case study from the Washington Metropolitan Area Transit Authority (WMATA) in which a single track needed to be removed from service. The results show that under the same resource constraints, different RSA design decisions can result in a large range of potential wait time impacts, with ratios ranging from 1.15 to 1.34 compared with normal revenue service, as well as a promising daily time period to perform work that balances a large increase in productivity with a smaller increase in LOS impacts.

  PublisherDOI

• Headway Management Strategies for High-Frequency Urban Rail Transit: Dynamic Approach to Real-Time Train Holding in Metro Systems

  Transportation Research Record Journal of the Transportation Research Board · 2026-01-27

  articleSenior author

  Urban rail transit systems frequently encounter challenges related to service reliability and passenger crowding, particularly during peak operational hours and in networks with complex service patterns. This paper presents an innovative approach to real-time train holding that addresses the unique challenges posed by systems with scheduled short-turning, where passenger loads at short-turning points can vary significantly. We developed a dual-strategy framework that combines (1) a real-time heuristic that calculates holding times using both historical data and real-time information to minimize passenger-experienced crowding, and (2) a predictive modeling approach that anticipates headway situations when full-length service trains from the terminal arrive at short-turning stations. Unlike conventional headway-equalizing strategies that overlook load variations in high-demand scenarios operating near capacity, our approach explicitly accounts for heterogeneous passenger loads across different service types to reduce denied boarding and passenger wait times. The effectiveness of our framework was evaluated using a microscopic simulation model of a high-frequency, high-demand urban rail transit system. The results demonstrate that the proposed approach reduced denied boarding incidents by 30% through improved train load balancing. The combination of predictive control with downstream holding strategies improved service quality through the proactive regulation of train dispatching at terminals, coupled with adjustments at key stations.

  PublisherDOI

• Individual Path Recommendation Under Public Transit Service Disruptions Considering Behavior Uncertainty

  Transportation Science · 2025-10-24

  article

  Public transit passengers need guidance during service disruptions. This study proposes an individual-based path recommendation (IPR) model. The model decides which paths to recommend for each passenger with the objective of minimizing system travel time and respecting passengers’ path choice preferences. We assume the recommendations could affect passengers’ path choice probabilities, but their actual choices are uncertain. This behavior uncertainty makes the problem a stochastic optimization with decision-dependent distributions. We propose a single-point approximation method to eliminate the expectation operator by introducing two new concepts: [Formula: see text]-feasibility and [Formula: see text]-concentration, which control the mean and variance of path flows in the optimization problem. The approximation yields a tractable single-stage mixed integer linear formulation, which can be solved efficiently with Benders decomposition. The approximation gap is proved to be bounded from above. Additional theoretical analysis shows that [Formula: see text]-feasibility and [Formula: see text]-concentration are strongly connected to expectation and chance constraints in a typical stochastic optimization formulation, respectively. The model is implemented in a real-world case study using data from an urban rail disruption in the Chicago Transit Authority system and a synthetic case study with varied network sizes and incident locations. In the real-world case study, results show that the proposed IPR model reduces the average travel times in the system by 6.6% compared with the status quo and by 4.2% compared with a capacity-based benchmark model. In the synthetic case study, the proposed model shows 15.0%–1.8% lower system travel time compared with the capacity-based benchmark, depending on the network sizes and demand situations. Funding: This work was supported by the Chicago Transit Authority. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0042 .

  PublisherDOI

• Flex-Route Transit for Smart Cities: A Reinforcement Learning Approach to Balance Ridership and Performance

  Smart Cities · 2025-09-16

  articleOpen access

  A major challenge for modern transit systems relying on traditional fixed-route designs is providing broad accessibility to users. Flex-route transit can enhance accessibility in low-density areas, since it combines the directness of fixed-route transit with the coverage of on-demand mobility. Although deviating for optional pickups can increase ridership and transit accessibility, it also deteriorates the service performance for fixed-route riders. To balance this inherent trade-off, this paper proposes a reinforcement learning approach for deviation decisions. The proposed model is used in a case study of a proposed flex-route service in the city of Boston. The performance on competing objectives is evaluated for reward configurations that adapt to peak and off-peak scenarios. The analysis shows a significant improvement of our method compared to a heuristic derived from industry practice as a baseline. To evaluate robustness, we assess performance across scenarios with varying demand compositions (fixed and requested riders). The results show that the method achieves greater improvements than the baseline in scenarios with increased request ridership, i.e., where decision-making is more complex. Our approach improves service performance under dynamic demand conditions and varying priorities, offering a valuable tool for smart cities to operate flex-route services.

  PublisherOA PDFDOI

• Estimating City-wide Operating Mode Distribution of Light-Duty Vehicles: A Neural Network-based Approach

  ArXiv.org · 2025-03-28

  preprintOpen access

  Driving cycles are a set of driving conditions and are crucial for the existing emission estimation model to evaluate vehicle performance, fuel efficiency, and emissions, by matching them with average speed to calculate the operating modes, such as braking, idling, and cruising. While existing emission estimation models, such as the Motor Vehicle Emission Simulator (MOVES), are powerful tools, their reliance on predefined driving cycles can be limiting, as these cycles often do not accurately represent regional driving conditions, making the models less effective for city-wide analyses. To solve this problem, this paper proposes a modular neural network (NN)-based framework to estimate operating mode distributions bypassing the driving cycle development phase, utilizing macroscopic variables such as speed, flow, and link infrastructure attributes. The proposed method is validated using a well-calibrated microsimulation model of Brookline MA, the United States. The results indicate that the proposed framework outperforms the operating mode distribution calculated by MOVES based on default driving cycles, providing a closer match to the actual operating mode distribution derived from trajectory data. Specifically, the proposed model achieves an average RMSE of 0.04 in predicting operating mode distribution, compared to 0.08 for MOVES. The average error in emission estimation across pollutants is 8.57% for the proposed method, lower than the 32.86% error for MOVES. In particular, for the estimation of CO2, the proposed method has an error of just 4%, compared to 35% for MOVES. The proposed model can be utilized for real-time emissions monitoring by providing rapid and accurate emissions estimates with easily accessible inputs.

  PublisherOA PDFDOI

• Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach

  ArXiv.org · 2025-10-03

  preprintOpen accessSenior author

  Efficient real-time dispatching in urban metro systems is essential for ensuring service reliability, maximizing resource utilization, and improving passenger satisfaction. This study presents a novel deep learning framework centered on a Convolutional Long Short-Term Memory (ConvLSTM) model designed to predict the complex spatiotemporal propagation of train headways across an entire metro line. By directly incorporating planned terminal headways as a critical input alongside historical headway data, the proposed model accurately forecasts future headway dynamics, effectively capturing both their temporal evolution and spatial dependencies across all stations. This capability empowers dispatchers to evaluate the impact of various terminal headway control decisions without resorting to computationally intensive simulations. We introduce a flexible methodology to simulate diverse dispatcher strategies, ranging from maintaining even headways to implementing custom patterns derived from observed terminal departures. In contrast to existing research primarily focused on passenger load predictioning or atypical disruption scenarios, our approach emphasizes proactive operational control. Evaluated on a large-scale dataset from an urban metro line, the proposed ConvLSTM model demonstrates promising headway predictions, offering actionable insights for real-time decision-making. This framework provides rail operators with a powerful, computationally efficient tool to optimize dispatching strategies, thereby significantly improving service consistency and passenger satisfaction.

  PublisherOA PDFDOI

• A Bunch of Gaps: Factors Behind Service Reliability in Chicago’s High-Frequency Transit Network

  Smart Cities · 2025-08-28

  articleOpen access

  Frequent transit services in urban areas have the potential to increase their accessibility to transit-dependent riders and reduce congestion by attracting new ridership through a modal shift. However, bus services operating in mixed traffic face operational challenges that reduce reliability and hinder their attractiveness. The sources of unreliability can range from local-level conditions, like the road infrastructure, to higher-level decisions, like the service plan. For the effective planning of improvement strategies, both scales of analysis must be considered. This paper uses a novel modeling framework to understand reliability by analyzing the route and segment factors separately. The Chicago Transit Authority (CTA) bus network is used as a case study for the analysis. The data reflect the operational, demand, and urban conditions of 50 high-frequency bus routes. At the route level, we use the coefficient of headway variation as the dependent variable and diverse route characteristics as explanatory variables. The results indicate that the most significant contributors to the variability of headways are variability in schedules and dispatching at terminals. It is also found that driver experience impacts reliability and that east–west routes are more unreliable than north–south routes. At the segment level, we use data from trips involved in bunching and gaps. As the dependent variable, a novel measure is formulated to capture how quickly bunching or gaps are formed. The bunching and gap events are treated as separate regression models. Findings suggest that link and dwell time variability are the most significant contributors to gap and bunching formation. In terms of infrastructure, bus lane segments reduce gap formations, and left turns increase bunching and gap formations. The insights presented can inform improvements in service and transit infrastructure planning to improve transit level of service (LOS) and support the future of sustainable, smart cities.

  PublisherOA PDFDOI

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