Transportation Operations Research

Many modern transportation systems rely on efficient allocation of resources across large networks with complex spatial and temporal considerations. This already challenging task is often further complicated by the presence multiple interacting decision-makers and stakeholders. Our research aims to develop methods for analysis, forecasting and effective decision-making in these complex systems. We combine tools from optimization, game theory, and machine learning with increasingly available data on transportation operations and decisions. Our current projects tackle problems in aviation, urban transit and ride sharing, maritme transportation, and high-speed rail. Click on the headings below for details of selected projects in various application areas.

Air Transportation

Airline Competition and Coordination: Airlines make pricing and service decisions in a competitive environment. Fare, service frequency and route planning choices of different airlines across the air transportation network are interrelated, and so have significant implications for the performance of the air transportation system as a whole. In addition, recent years have seen several major airline mergers, the formation of alliances, and allegations of ticket price collusion between major carriers. We use game-theoretic and statistical methods to model airline behavior, with a view toward informing forecasting, planning, and policy making. Recent and ongoing work analyzes the effects of airline mergers, develops methods for efficient and equitable air traffic congestion mitigation in airports, and uses game theoretic methods to model resource allocation incentives in airline networks.

Airline Scheduling and Delay Modeling: Airlines must tackle multiple large, interrelated assignment and scheduling problems on a daily basis: aircraft must be routed between different airports, flights must be scheduled, and crew must be assigned using efficient algorithms that are robust to disruption and uncertainty and that minimize delays. We evaulate and develop a variety of optimization-based approaches to these challenges, combining state-of-the-art optimization algorithms and data-driven modeling. Past and ongoing work evaluates approaches and develops models for aircraft routing and models crew-related delay propagation in airline networks.

Urban Transit and Ride-Sharing

Urban Transit Forecasting and Routing: Our group has ongoing collaborations with transit companies currently deploying systems for efficient transit operations in Ithaca, NY. We work on both delay forecasting models and dynamic routing algorithms in order to improve passenger experiences and gain insight into the the dynamics of large-scale transit operations.

Maritime Transportation and Resource Mangagement

Fisheries Observer Assigment and Routing: Human observers monitor fishing vessel activity in order to ensure compliance with international fishing regulations and monitor the sustainability of fishery resources. Working with the World Wildlife Fund (WWF), we develop optimization-based approaches for efficiently assigning hundreds of observers to sequences of fishing vessel trips, minimizing deployment costs while conforming to complex constraints on scheduling.

High-Speed Rail

Multi-modal Scheduling and Competition: The growth of high-speed rail, particularly in Europe and Asia, has resulted in competition for passengers between airlines, rail, and other trasportation modes. In recent and on going work, our group has uses optimization-based, statistical, and agent-based models to model the competitive interactions between rail and other modes of travel (e.g., airlines). We have an ongoing collaboration with researchers at Beijing Jiaotong University using these approaches in the context of China's rapidly growing high-speed rail system.