Journal Publications

@article{Lenfers2021b,
title = {Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg},
author = {Ulfia Annette Lenfers and Nima Ahmady-Moghaddam and Daniel Glake and Florian Ocker and Jonathan Ströbele and Thomas Clemen},
editor = {Simon Elias Bibri},
url = {https://www.mdpi.com/2073-445X/10/11/1179/htm},
doi = {10.3390/land10111179},
issn = {2073-445X},
year = {2021},
date = {2021-11-03},
journal = {Land 2021},
volume = {11},
number = {10},
abstract = {Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings. },
keywords = {adaptive behavior, agent-based model, decision support systems, multimodal travel, sohh, urban planning},
pubstate = {published},
tppubtype = {article}
}

@article{Lenfers2021,
title = {Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model},
author = {Ulfia A. Lenfers, Nima Ahmady-Moghaddam, Daniel Glake, Florian Ocker, Daniel Osterholz, Jonathan Ströbele and Thomas Clemen},
editor = {Philippe J. Giabbanelli and Arika Ligmann-Zielinska},
url = {https://doi.org/10.3390/su13137000},
doi = {10.3390/su13137000},
year = {2021},
date = {2021-06-22},
journal = {Sustainability},
volume = {13},
number = {13},
abstract = {The current trend towards living in big cities contributes to an increased demand for efficient and sustainable space and resource allocation in urban environments. This leads to enormous pressure for resource minimization in city planning. One pillar of efficient city management is a smart intermodal traffic system. Planning and organizing the various kinds of modes of transport in a complex and dynamically adaptive system such as a city is inherently challenging. By deliberately simplifying reality, models can help decision-makers shape the traffic systems of tomorrow. Meanwhile, Smart City initiatives are investing in sensors to observe and manage many kinds of urban resources, making up a part of the Internet of Things (IoT) that produces massive amounts of data relevant for urban planning and monitoring. We use these new data sources of smart cities by integrating real-time data of IoT sensors in an ongoing simulation. In this sense, the model is a digital twin of its real-world counterpart, being augmented with real-world data. To our knowledge, this is a novel instance of real-time correction during simulation of an agent-based model. The process of creating a valid mapping between model components and real-world objects posed several challenges and offered valuable insights, particularly when studying the interaction between humans and their environment. As a proof-of-concept for our implementation, we designed a showcase with bike rental stations in Hamburg-Harburg, a southern district of Hamburg, Germany. Our objective was to investigate the concept of real-time data correction in agent-based modeling, which we consider to hold great potential for improving the predictive capabilities of models. In particular, we hope that the chosen proof-of-concept informs the ongoing politically supported trends in mobility—away from individual and private transport and towards—in Hamburg.},
keywords = {agent-based model, decision support systems, IoT sensors, MARS, model development, multimodal travel, real-time data, simulation correction, smart cities, sohh, urban planning},
pubstate = {published},
tppubtype = {article}
}