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Current Research Projects

Agent-based simulation of passenger behavior to optimize the delay management in rail transport (ASimOV)


				Agent-based simulation of passenger behavior to optimize the delay management in rail transport (since 2016): Delay management of railway operations decides how to react to delays of individual trains. One of the dispatching decisions to be made is to determine whether a connecting train should wait for a delayed feeder train or if it is better to depart on time. Similarly, priority decisions have to be taken in case several trains compete for the same track section concurrently.

A major challenge in this project lies in connecting two heterogeneous simulation models within the context of an optimization task: the macroscopic simulation of trains in the infrastructure network and the microscopic simulation of passengers in the railway station to form a scalable and efficient overall simulation system.

Agent-based modeling and simulation (ABMS) is a computational model for simulating autonomous (cognitive) agents and the interactions among them in a shared environment. The ABMS will be used to analyze the effect of passenger behavior on train delays and the dispatching in general. Different influencing factors, such as goals and preferences of the passengers, their behavior during changeovers between trains, and the degree of information will be modeled and studied. This will enable us to test existing dispatching strategies and to develop new ones by using the simulation. Finally, the simulation methodology will be extended to an iterative hybrid process which runs the simulation and the optimization steps alternately. This process makes it possible to use information from the simulation within the optimization and vice versa. In this way we intend to provide optimized solutions which are also performing well in simulated realistic scenarios.


Explainable AI Methods for Human-Centric RIDEshaRing (EC-RIDER)


				Explainable AI Methods for Human-Centric RIDEshaRing (since 2019): This project is supported by the Volkswagen Foundation. The consortium comprises five internationally renowned universitary working groups from Germany and Israel, covering the core scientific areas required for achieving the project goals.

With EC-RIDER, we propose such a human-centric AI-enabled approach towards sustainable ridesharing, e.g. when finding the best assignment of passengers to drivers/vehicles. By taking user goals, needs, preferences, constraints, decision patterns and behavior into account more accurately, we hope to create future shared mobility services and underlying dynamic management mechanisms (e.g. pricing and incentives), that are not only efficient, but also acceptable and attractive to city dwellers.

Such a human-centric approach to shared mobility services based on AI methods needs to address a number of unsolved research challenges:

    1. Understand and model human motivation and behavior (user satisfaction, trust, choice of mobility mode) in ridesharing applications while maintaining user privacy.
    2. Develop and evaluate novel business and operations models including pricing and incentive schemes, taking the models of RC1 into account, in order to achieve a satisfactory degree of social wel-fare.
    3. Develop and evaluate innovative algorithmic AI methods based on the models provided by RC1 and RC2 to find fair, socially sustainable, and efficient traffic assignments.
    4. Develop new methodologies for successfully creating and applying the AI methods of RC3. The critical success factor we will address is explainability of AI methods and algorithms to the human user.

SocialCars


				SocialCars (since 2014):The overall objective of the SocialCars Research Training Group RTG is to research new methods and applications of decentralized, cooperative traffic management, that are enabled by new technological trends and developments such as Car-to-X communication. SocialCars focuses on the interplay of centralized management in the sense of classical traffic control, and decentralized management in the sense of the local goals of individual traffic participants. In order to comprehensively study this interplay while considering both the requirements of traffic participants and the constraints of the urban environment, we propose six fields of research, in which we investigate novel and interdisciplinary research questions. In these fields of research, we study problems related to behavioural aspects of traffic participants, societal objectives, technical and algorithmic foundations of communication, interaction, and dynamic geo-information, as well as models and methods of cooperative, (de)centralized traffic management. We research solutions to these problems that will enable us to realistically describe dynamic cooperative traffic systems, and to evolve and optimize such systems in accordance with societal objectives. The subject-specific competences of the participating professors provide excellent coverage of the six fields of research, from traffic management over dynamic geo-information systems and communication technology to modeling distributed decision-making, coordination, and cooperation. The RTG will be embedded in the Niedersächsisches Forschungszentrum Fahrzeugtechnik (NFF), thus the main working space of the students will be in the new NFF Research Building at TU Braunschweig where they are exposed to an inspiring environment with a wide range of academic and industrial competences from renowned colleagues working in the areas of mobility, traffic, and automotive engineering. At the same time, the students will also have a workspace at their home institutes, which will serve to foster their embedding in their own discipline. This substantially expands the qualification opportunities in the strategic research focus Mobility and Traffic of the NTH.

  1. Topic "Modeling Interaction among Different Types of Road Users in Shared Spaces":

    The main motivation of this project is modeling the strategic interaction (conflict handling) between traffic participants in shared space environment. In shared spaces, different transport modes are coexisted such as pedestrians, cyclists, motorized vehicles and public transport. In addition, the movement of the traffic participants here conducts based on the social protocol (e.g. courtesy behavior) and informal rules (e.g. colored floors). Participants are interacting more frequent to negotiate the priority over the integrated space. The interaction between different user modes in shared spaces is the result of a complex human decision-making process. The goal of this project is to develop a hybrid model for imitating the above behavior. Here, a hybrid model means which can handle both automatic user reactions as well as the complicated decision making.

    In any simple situation, the reactive interaction among participants can be modeled by a physical approach such as social force model of Helbing and Molnar, which modeled the interaction among pedestrians. The social force model also extended for other traffic modes. However, to model the complex human decision process, other approaches like algorithmic game and decision theory are particularly promising.

    In this thesis, the reactive (operational level) interaction among road users will be modeled by a generalized social force model. In addition, the complex conflict among users will be modeled with a game theoretic approach. The validation of the model is planned by using existing real-world trajectories from a shared space environment available in SocialCars from an earlier project. Finally, study cross-relationships between parameters of the game-theoretic interaction model and aspects of shared space design with concern about safety issues and the effectiveness, which can influence the interaction behavior of traffic participants as informal rules, is intended to perform.

  2. Topic "Online Coordination of Same Day Delivery with Mixed Autonomous Fleets":

    Same day delivery of goods purchased via the Internet is becoming widely available. The foreseeable availability of autonomous transport vehicles (SAE level 4 or higher) creates opportunities but also new planning and control challenges for city logistics operators – deliveries can be made (under certain preconditions) by autonomous vehicles (“Pods”). To carry multiple deliveries of a single customer by different operators in a customer-friendly and sustainable way, coordination among city logistics operators is required.

    This dissertation shall investigate coordination problems at the operations level. We assume that Pods that serve the same customers shall form dynamic convoys, agreeing on order and time of visiting individual customers. Since operators are self-interested and competing, they have differing preferences over solution alternatives. In this work, we shall study the applicability of methods of algorithmic decision theory (and, in particular, computational social choice theory) to this coordination problem and research voting mechanisms to find solutions that are acceptable to all parties and sustainable from the perspective of urban planning. The work shall also investigate how voting mechanisms can be integrated with traditional optimization approaches. Validation of the results will be carried out through simulation using an agent-based simulation environment.
Also see https://socialcars.github.io for additional documentation of research.


CONNECT


				CONNECT (since 2013):Product Data Management (PDM) is an essential subarea within Product Life Cycle Management (PLM) and therefore of significant importance for industry. The increasing global collaboration in the field of product development along with the rising global competition requires new concepts and technologies to support the processes of the Product Data Management appropriately. Besides the safe, efficient and scalable management of product data, the seamless integration of trustworthy collaboration partners is one of the main focuses. In CONNECT, MEClab supports Volkswagen in the development and testing of innovative concepts to realize and optimize processes and technologies for the implementation of a global Product Data Management Systems (K-PDM).


eCULT/eCULT+


				eCULT/eCULT+ (since 2011): eCULT/eCULT+ (eCompetence and Utilities for Learners and Teachers) is a collaborative project in Lower Saxony, funded by the Federal Ministry of Education and Research. Eleven universities and two other institutions are taking part in this project. The goal is to improve the quality of teaching and learning at universities. Therefor the project participants exchange local experiences to enhance existing eLearning approaches and to develop new eLearning approaches together. The project focuses on eAssessment, teaching and learning organisation, and video-based teaching and learning. The MEC research group is active in the area of eAssessment. In this area we focus on decentralised systems to support programming and modeling exercises at universities.



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