Martin Kagerbauer

This study investigates how cycling speeds vary across infrastructure types using open data from Hamburg, Germany, collected between 2022 and 2024. By integrating bicycle network data, tracking app-based cycling speeds, land use, and topographic information, key determinants of cycling speed are identified through a gamma regression model. Results show that infrastructure type, surface conditions, and surrounding land use significantly affect speed. Dedicated cycling infrastructure promotes faster and more consistent speeds, especially on bituminous surfaces. Also, longer segments increase speeds. Urban or industrial areas tend to reduce speeds, while fields and forests lead to faster cycling speeds.

Ride-pooling (RP) services, in which passengers with similar destinations share a ride, offer considerable potential for enhancing urban mobility by bridging gaps in public transportation (PT) networks and providing a convenient alternative to private car use. For the effective design and operation of such services, a detailed understanding of user preferences and usage patterns is essential. This study investigates differences in RP preferences and usage between day and night (with nighttime defined as 10:00 p.m. to 5:00 a.m.), drawing on both a stated choice experiment (SCE) and revealed preference data collected in Mannheim, Germany. The focus lies on the local RP service fips, which is integrated into the PT system. The SCE, conducted in 2024 with 566 participants, was analyzed using a nested logit model. The analysis of the SCE reveals that nighttime preferences for RP are characterized by reduced sensitivity to travel time and cost, creating an opportunity for RP operators to optimize stop network designs during nighttime hours by increasing pooling rates. In addition, it indicates a greater likelihood of private car usage at night, especially among women, likely due to safety concerns and limited PT availability. The analysis of revealed preference data provides a complementary perspective. It shows that the RP nighttime service primarily attracts younger users, while many respondents report not being active on weekend nights. However, the combination of low public awareness and limited service availability, evidenced by rejected booking requests, suggests that existing demand is not being fully captured. This implies that low usage is not merely the result of low demand, but also of structural barriers on both the supply and information side. Overcoming these barriers through targeted information campaigns and expansion of nighttime service capacity could substantially enhance sustainable urban travel options during nighttime.

Individual travel behavior, such as mode choice, is determined to a distinct degree by the respective portfolio of available mobility tools, such as the number of cars, public transit pass ownership, or a carsharing membership. However, the choice of different mobility tools is interdependent, and individuals weigh alternatives against each other. This process of parallel trade-offs is currently not reflected in typically used sequential logit models of agent-based travel demand models. This study fills this research gap by applying discrete choice and neural network models on a synthetic population to model multiple mobility tool ownership simultaneously. Using data from a national household travel survey, both model types approximated the given target distributions of mobility tools more accurately than the sequence of three corresponding logit models. Owing to its greater flexibility, the tested shallow and deep neural network exhibited higher predictive accuracy than simultaneous discrete choice models. The results indicated that neural networks with only one hidden layer were more robust and easier to formulate and interpret than deep networks with three hidden layers. Finally, the flat neural network was applied to a different synthetic population resulting in equally accurate results.

To make cities more sustainable and livable and to achieve climate targets in transportation, cities around the globe must undergo sustainable transformations. However, disparities in initial conditions pose challenges when trying to implement these sustainable changes. Identifying these differences aids in the comprehension of future developments. In this study, we establish an international comparison by decoding the mobility-related characteristics of cities and determining urban archetypes. Using publicly accessible data, we analyze and classify 96 cities in different countries. Therefore, we utilize principal component analysis to simplify the data. The emerging components serve as input for segmentation. This approach yields nine unique urban archetypes, ranging from Well-Functioning and Ancient Hybrid Cities in Europe to Paratransit and Traffic-Saturated Cities in the southern hemisphere. Our results show that there is a significant advantage to using a multidimensional segmentation basis, which we identify in an extensive literature review. The result is a finer segmentation, which is especially clear for European cities that demonstrate four different clusters. We discuss that the effect of future restrictions on private car usage will vary widely between the urban archetypes.

Autonomous busses and on-demand (OD) services have the potential to improve the public transport system. However, research on potential traffic impacts is still ongoing, mainly because of a lack of existing use cases of autonomous driving as part of public transport. The availability of revealed preference data for mode choice decisions is thus very limited. Therefore, we conducted a stated choice experiment to assess mode choice preferences with regard to use cases as the main mode of transport and as the solution for the first and last mile. We also distinguished between OD and schedule-based (sched.) services. The target population of the survey is the population of Baden-Württemberg, a state in southwestern Germany. The responses of 1,434 people were analyzed using a nested logit approach. On this basis, we established exemplary utility functions and descriptively derived recommendations for efficient forms of deploying autonomous busses in addition to already existing well-developed public transport systems. It was found that, under the given conditions, public transport pass owners without a car in their household would be the most interested in using autonomous busses. Car owners without a smartphone see less benefit. It was also shown that the recruiting method of the respondents is crucial. Those reached via social media were significantly more positive than those contacted via an online panel. Further evaluations show that autonomous busses are rated similarly to existing public transport and consequently have particularly high potential on medium distances, especially if their deployment leads to shorter access routes.

Durch den Wegfall der Fahrtätigkeit in vollautonomen Fahrzeugen ergeben sich neue Möglichkeiten die Fahrzeit zu nutzen. In der vorliegenden Studie wird auf Basis einer Stated-Preference-Befragung untersucht, welchen Tätigkeiten sich Personen in autonomen Fahrzeugen widmen würden und wie sich diese von der heutigen Zeitnutzung im Öffentlichen Verkehr unterschiedet. Die Ergebnisse lassen eine Vielzahl von Aktivitäten, insbesondere jedoch in den Bereichen Kommunikation und Freizeit, erwarten. Im Fahrzeug zu Arbeiten wird bei Vollzeit-Erwerbstätigen im Mittel zu 9,1% der Fahrzeit erwartet, bei Teilzeit-Beschäftigen zu 6,7%. Aufgrund dieser geringen Zeitanteile ist davon auszugehen, dass sich durch autonome Fahrzeuge insgesamt leichte Veränderungen bei den Aktivitäts- und Wegemustern zeigen werden.

Als Reaktion auf das steigende Paketaufkommen werden neue, nachhaltige Konzepte der City-Logistik gesucht. Eine Lösung kann die Nutzung der bestehenden städtischen Schieneninfrastruktur über Cargo Trams sein. Um die verkehrlichen Wirkungen eines derartigen Konzepts quantifizieren zu können, wird in dieser Arbeit ein Güterverkehrsmodell für den Pakettransport, mit Fokus auf gewerbliche Paketnachfrage, vorgestellt. Die Ergebnisse zeigen, dass durch eine Cargo Tram grundsätzlich positive Effekte auf den Verkehr zu erwarten sind. Das Potenzial hängt jedoch stark von verschiedenen Faktoren wie der Anzahl und Lage der City Hubs ab.

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.