About the lab
Our interdisciplinary team researches transport at all levels. We investigate global and national trends of mobility, analyse travel demand of entire metropolitan regions and cities and determine the resulting effects on individual freeways and junctions.
Featured projects (2)
Up-to-date information about travel demand and personal travel behavior is important for transportation policy decisions and planning. On the basis of such information the transportation infrastructure can be designed and preserved in order to meet the needs of the population - today and in the future. Since 1994 the German Mobility Panel (MOP) surveys such essential information on an annual basis - for example information about travel mode use, travel purposes or travel time of the German population. For that purpose households are interviewed about their every day travel behavior and their car usage. With this information the survey participants substantially contribute to a sustainable development of the transportation infrastructure. Please visit the project website www.mobilitaetspanel.de for further information.
mobiTopp is an agent-based travel demand modelling framework designed in a modular fashion, so that exchange of individual modules is easy. Source code concerning mobiTopp is published on https://github.com/mobitopp
Featured research (16)
This paper considers which work-related trip patterns are included in household travel surveys and which in commercial travel surveys and if there are certain patterns that are distinctly underrepresented in either one. The study is structured as a comparison between data from a household travel survey and data from a commercial travel survey. Both surveys were conducted in Germany and within close temporal proximity. We applied cluster analysis to identify differences in the data and identify work-related travel patterns. The results show that work-related travel patterns are quite complex. Although some patterns are covered in both surveys, mobile workers’ travel patterns in particular are not represented well in the household travel survey. Furthermore, our analysis shows that not all commercial trips are generated by motorized vehicles and a considerable share of work-related trips are undertaken using public transport or active modes of transport that are not covered by the commercial travel survey. The results indicate that researchers and transport planners creating travel demand models need to pay more attention to work-related travel behavior and acknowledge that depending on the area of study, traditional household travel surveys may not provide a complete sample of the population; however, simply adding data on commercial trips from commercial travel demand models to data from household travel surveys does not provide a complete picture of work-related travel either.
The COVID-19 pandemic has forced employers and employees to re-evaluate their attitudes toward telecommuting. This induced a change in the sheer number of people who have started to work from home (WFH). While previous studies highlight differences between telecommuters based on their level of telecommuting experience, these effects have not been studied in detail. This may limit the evaluation of implications for post-pandemic times and the transferability of models and predictions based on data collected during the COVID-19 pandemic. This study expands on previous findings by comparing the characteristics and behavior of those who have started to telecommute during the pandemic and those who had already telecommuted before. Furthermore, this study addresses the uncertainty that exists about whether the findings of studies conducted before the pandemic—for example about sociodemographic characteristics of telecommuters—still hold true, or if the pandemic induced a shift in telecommuters’ profiles. Telecommuters show differences when considering their previous experience in WFH. The results of this study suggest that the transition induced by the pandemic was more drastic for new telecommuters compared with experienced telecommuters. The COVID-19 pandemic had an effect on how household configurations are considered in the choice to WFH. With decreased access to child care resulting from school closings, people with children in the household were more likely to choose to telecommute during the pandemic. Also, while people living alone are generally less likely to choose to WFH, this effect was reduced as a result of the pandemic.
The objective of this study is to investigate the role of spatial characteristics on car ownership and availability respectively in agent-based travel demand models and its affection on the model's results. Based on Open Data we generate an automated workflow to evaluate spatial characteristics such as land use, points of interest, private vehicle network, and public transport quality. We estimate two multinomial logit models for car ownership: one considering socio-demographic characteristics only, and one considering both, socio-demographic and spatial characteristics. The models' results are spatially evaluated and compared with statistical data. Moreover, we analyze the sensitivity of public transport quality measures on car ownership. The application of both car ownership models in the agent-based travel demand model mobiTopp to the city of Hamburg, Germany shows that integrating spatial characteristics significantly improves the model's goodness of fit as well as its overall prediction power. Moreover, the application demonstrates that a detailed consideration of spatial characteristics in car ownership models contributes to a more realistic spatial distribution of cars. Furthermore, the study shows that i.e., public transport quality measures in car ownership models are relevant to reflect secondary mode choice effects (i.e., different mode choice sets due to change in car stock) in travel demand models.
Disruptions in public transport operations occur every day. Thus, providing a reliable system is a challenge for operations and planning. This paper gives insights into the dynamics and processes of operations control centers in public transport to reveal potentials for further improvement in reliability. Therefore, directors were interviewed, dispatchers observed, and operations documentation was studied. It has become obvious that the process of dispatching has four different types of call signals (assault, accident, missing replacement, and wish-to-talk) corresponding to different kinds of incidents. The drivers use those call signals to contact the operations control center and initialize different procedures of communication between the dispatchers, drivers, and other involved parties. As the communication is mostly conducted via phone or radio, several improvements are possible, such as training in communications and increased use of information technology in operations. In planning tools, the handling of incidents is marginally supported. As all kinds of incidents can affect the service, they should be represented in planning tools to design more reliable public transport systems. However, they do not need to be represented in full detail. Verbal communication could mostly be reduced to single decisions. Accidents, for example, influence the operation by delayed vehicles and blocked ways. The findings of this work allow a better understanding of operations control centers and reveal their potentials for improvement.
In Karlsruhe wurde 2021 mit dem EVA-Shuttle ein autonomer Kleinbus angeboten, welcher technologische Fort-schritte im Vergleich zu vergleichbaren Projekten vorwies. Im Rahmen einer Haushaltsbefragung zeigte sich, dass EinwohnerInnen dem Angebot aufgeschlossen gegenüberstanden. Die NutzerInnen weisen ein multimodaleres Verkehrsverhalten als Nicht-NutzerInnen auf. Schwierigkeiten zeigten sich in der Reisegeschwindigkeit, Verfügbar-keit und der Komplexität der Nutzung. Personen können sich die Nutzung auch in Zukunft vorstellen.