The increasing volume of e-commerce returns poses significant challenges for urban transportation systems. However, little attention is paid to e-commerce returns in transportation research. This study integrates return parcel volumes into freight demand modelling using the agent-based framework logiTopp. By categorizing parcel demand (e.g. fashion and electronics) through a Multiple Discrete-Continuous Extreme Value model and linking these categories to return probabilities, we estimate category-specific parcel order and return volumes for Karlsruhe, Germany. The results indicate that roughly one in five parcels is returned, corresponding to about 27,500 return parcels per week.This framework provides a foundation for assessing the impacts of return flows on urban logistics and for evaluating operational strategies such as parcel lockers, autonomous collection systems, or integrated pickup by delivery tours. The findings highlight the need for empirical data collection on return parcel drop-off behavior to enhance the modelling framework and enable a more comprehensive assessment of its transportation impacts.

The rapid growth of e-commerce has led to increased parcel volumes, posing challenges for sustainable city logistics. This study evaluates the potential of a combined cargo and passenger tram system as an alternative to light commercial vehicle (LCV) shipments. Empirically extending the agent-based freight demand model ‘logiTopp’, transport-related effects are analyzed through scenario simulations. Results show that a cargo tram can shift LCV mileage to sustainable modes, though the overall shift exceeds saved LCV mileage. The study highlights operational challenges like missing rail access at distribution centres and emphasizes the need for policy incentives to foster the cargo tram’s potential.

Key Performance Indicators (KPIs), such as modal split or vehicle kilometres travelled (VKT), are essential for assessing mobility trends and informing policy decisions. Traditionally, such indicators rely on travel survey data, which, while comprehensive, are costly and time-consuming to collect. In recent years, alternative data sources, such as mobile phone data, floating car data, and sensor-based measurements, have become increasingly available, offering new opportunities for more frequent and spatially detailed mobility analyses.This paper examines various data sources that can be used for KPI calculation, highlighting their strengths and weaknesses. We review existing studies that have successfully combined two or more of these data sources to gain a more comprehensive understanding of travel behavior. The key focus is on methodological challenges of data fusion, including spatial and temporal resolution issues, representativeness, and consistency between datasets.Furthermore, we discuss the role of travel surveys in supporting data integration. While surveys alone cannot provide continuous travel monitoring, they offer valuable microdata on travel behavior, which can help validate and calibrate other data sources. Finally, we outline best practices for combining different mobility data sources to enhance the accuracy and reliability of transport indicators.

Ride-pooling is one of many new on-demand mobility services that have become increasingly popular in recent years. The role these novel services will play in the future is still uncertain. As of today, their share in the modal split is still low. However, in the context of traffic and environmental policy goals, these services could gain significant importance in the next years. Currently, it is often unclear who uses these services and for what purposes. Traditional household surveys are not particularly well-suited for investigating the use of these novel services and their users: the subsample size of users is small, and the number of reported trips is very low. Consequently, the results obtained are not reliable. Using the example of ride-pooling, this paper describes a mixed method study design composed of an online survey with a stated choice experiment and semi-structured interviews to understand ride-pooling use and its users. Special attention is given to the design of the stated choice experiment, which specifically targeted shared on-demand mobility services. A selection of the various results is presented to demonstrate the advantages of combining different methods when investigating new mobility services.

In ride-pooling, a fleet of vehicles is dynamically dispatched to bring travelers from A to B, trying to pool riders with similar itineraries to improve the use of resources compared to taxis or private cars. Ride-pooling is considered a core building block of future transport systems with autonomous vehicles. In this paper, we introduce Mt-KaRRi, a novel dispatcher for dynamic ride-pooling that leverages state-of-the-art shortest-path algorithms to process millions of travelers per hour. We add a simple mode choice model and use realistic travel demand in three different urban areas for extensive experiments. We find that our dispatcher scales well with a response time per request of around 1ms even for our largest instances. We show how this scalability can be used to conduct ride-pooling studies at unprecedented scale. For instance, we determine how the quality of rides and usage of vehicle resources develop for tens of thousands of vehicles and millions of travelers. We envision Mt-KaRRi as a tool for future ride-pooling simulation studies at scale.

In this study, we introduce a methodology that follows the conventional approach of generating a synthetic population (SynPop), which is subsequently enhanced and refined by geographical information using mobile phone data (MPD). The overarching goal is to enable a more specific trip distribution based on the established 4-stage model of transport demand modeling in our study area the city of Darmstadt and its surrounding rural areas in Germany. By employing a mixed exact-probabilistic data-matching approach, the methodology enhances representativeness and granularity, addressing biases and limitations of current datasets. Overall, six key attributes with a mixed approach of exact and non-exact matching were utilized for the matching process. The matching was executed iteratively using a greedy algorithm, continuing until no data points remained. A comparison of the two applied distance metrics, Nearest Neighbor matching using Propensity Scores and Mahalanobis distance each with and without a caliper, revealed, after validating the balance, that the Mahalanobis distance without a caliper achieved more reliable matching for our data. Nevertheless, the data basis must be more precisely aligned with the matching parameters in the future.

Increasing car ownership over the last decades has led to a significant portion of public space in cities being allocated to cars. As a consequence, there is a strong competition for the use of public space, especially in dense urban neighborhoods with few private parking spaces. While many residents and experts advocate for a reduction of car dominance to make place for climate-friendly mobility, improve the quality of life, and mitigate the effects of climate change, car owners often specifically oppose the repurposing of on-street parking spaces, as they fear an aggravation of the already high parking pressure. To identify measures that could increase the acceptance of a reduction in on-street parking spaces, we applied an empirical three-stage mixed-method approach as accompanying research for a participatory mobility transition project in Munich’s Dreimühlen quarter. The approach consisted of a longitudinal parking study, a travel behavior survey and a qualitative group discussion. Our results highlight the value of combining quantitative, qualitative, and participatory methods for identifying potentials and appropriate measures for the mobility transition in urban neighborhoods. Furthermore, we found that integrating our approach into a mobility transition project can enhance public acceptance of planned transformations.

With the advancement of artificial intelligence (AI), machines are gaining unprecedented autonomous capabilities. This progress presents a significant challenge in how to seamlessly integrate humans, machines, organizations, and the environment into meaningful socio-technical systems. This process is called Human System Integration (HSI) and a prime example is vehicle automation in the transportation sector. In this sector AI enables a spectrum of automation levels, culminating in highly and fully automated systems. In the center of this integration challenge lies the concept of control. Traditional control theory, which focuses on a single entity’s command and execution, is no longer sufficient to address this growing complexity. The rise of automated systems makes new control paradigms necessary. Shared control, where humans and machines collaboratively operate the vehicle, and traded control, where authority is passed back and forth; both provide more dynamic and flexible solutions. The synthesis of these approaches, cooperative control, represents a new frontier for integrating people with intelligent technologies. As these paradigms become increasingly relevant, they demand novel methodologies that extend beyond traditional engineering and human-in-the-loop experiments. To bridge the gap from initial theoretical concepts to practical design patterns and implementations, a deeper and more systematic investigation into the human-system relationship is required, moving toward a holistic understanding of human adaptation, trust, and collaboration with vehicle automation. Challenges for human adaptation introduced by the rapid evolution of intelligent technologies cannot be neglected, as failures in human-system coordination can have direct implications for public safety and societal acceptance. This gives rise to the crucial concept of Human Systems Migration (HSM), which is now scientifically defined and investigated. It describes the dynamic process of humans and technologies moving together through new system configurations. In this context, the DFG-funded research group MiRoVA (Migration of Road Vehicle Automation) was established to investigate and exemplify Human Systems Migration in the domain of automated vehicle systems.In this paper, we present Human Systems Migration as key paradigm for understanding the integration of people and intelligent technologies. We illustrate these paradigms with the MiRoVA project, where we explore migration paths through different automation levels and analyze the resulting processes of adaptation and collaboration. We view the migration challenge on various levels, including a technological, game theoretical, as well as micro and macro perspective. These considerations not only address the integration of people and automation, but also extend to broader concerns such as social development, autonomy, safety and sustainability. Our interdisciplinary approach provides a foundation for bridging theoretical models with design patterns and practical implementations, addressing critical questions of trust, safety, and societal acceptance of vehicle automation.

Segmentation is widely applied in quantitative travel behavior research to categorize heterogeneous populations into more homogeneous subgroups to create a more nuanced understanding of travel behavior and facilitate targeted policy and planning. However, without proper validation, identified segments may reflect artifacts of the data or the statistical method used rather than meaningful mobility patterns, limiting their practical use. Respondent validation, which assesses whether individuals recognize themselves in the segments they were assigned to, has received little attention in quantitative travel behavior research. This paper introduces a quantitative online self-assessment survey as a method to pursue respondent validation of segmentation results. Using a case study in Munich, Germany, we evaluate whether respondents can comprehend, differentiate, and identify with segment profiles, how well their self-assessments align with statistical assignments and membership probabilities, and what reasons they provide for mismatches. The results indicate that the profiles of segments created as part of our method were generally perceived as appealing, comprehensible, and distinguishable, though only a minority of respondents fully identified with a single segment. Exact matches of self-assessments and statistical assignments to segments were rare, but self-assessments correlated positively with membership probabilities, suggesting that probabilistic segmentations may be better suited for quantitative respondent validation than deterministic assignments. To reveal potentials for refinement of the segments and their descriptions, our method lastly enabled respondents to attribute discrepancies between self-assessments and statistical assignments to specific segment characteristics. Factors specific to our case study limit the generalizability of our findings. Overall though, the findings suggest that our method can provide systematic insights into respondents’ evaluations of segmentation results and can thus contribute to the validation of segmentation results, especially strengthening their credibility, transparency, and practical relevance.

To achieve a modal shift away from private car use, public transportation must become a more attractive alternative, particularly with regard to reliability. While travel time and cost have been extensively studied as key determinants of mode choice, the impact of travel time reliability has received increasing attention only in recent years. However, quantitative surveys addressing this issue remain scarce, and most existing evidence is based on stated choice studies with highly heterogeneous results. One reason for this inconsistency is the complexity of representing reliability, as it can only be described through a distribution. Many forms of representation appear to overwhelm respondents. This study therefore seeks to bridge this gap by conducting a stated choice experiment with an improved visualization of reliability, presenting travel time and five equally likely delay times as bar charts and incorporating realistic choice situations that account for both trip purpose and transported luggage.The survey, conducted with a net sample of 859 respondents, reveals that travel time reliability is a decisive factor influencing mode choice. Delays in public transportation are particularly disfavored, with the mean delay rated 3.7 times more negatively than travel time itself. This valuation significantly exceeds the ratios applied in existing cost-benefit analyses, such as those used in German transport infrastructure planning. These findings suggest that the perceived importance of reliability is substantially higher than previously assumed. For on-demand services, the study indicates that displaying slightly longer expected travel times with early arrivals is preferable to promising shorter times that result in delays. These insights underscore the need for policymakers and transport planners to prioritize reliability improvements in public transportation planning and investments.

Freeway Control Systems (FCS) play a vital role in enhancing road safety and traffic efficiency by dynamically managing traffic through variable speed limits, overtaking restrictions, and warning messages. An accurate representation of FCS behavior in traffic simulations is essential for realistic modeling. However, the manual implementation of FCS logic in simulations is time-consuming and requires high customization. This study proposes a data-driven approach to automatically reconstruct FCS control algorithms from historical traffic and display data. The models achieved prediction accuracies of at least 87%, effectively capturing key behaviors such as congestion-related speed reductions. Among the architectures evaluated, the baseline Convolutional neural network offered the best balance of performance and computational efficiency. At the same time, more complex models showed promise for further accuracy gains with continued development. These findings demonstrate the feasibility and potential benefits of integrating FCS models based on neural networks into traffic simulations.

Multimodal travel behavior plays a pivotal role in sustainable transport infrastructure design. Unlocking its potential requires a deeper understanding of the underlying mechanisms governing everyday travel. In this study, we investigate the association between activity variability and multimodal travel behavior in Germany using data from the German Mobility Panel. Through descriptive analyses and regression modeling, we explore the activity-related characteristics and contextual factors influencing the adoption of multimodal travel among employees. Our findings reveal that using multiple transport modes positively correlates with engaging in diverse activities. Notably, leisure and shopping activities exhibit a powerful influence on multimodal travel behavior. Moreover, complex travel needs, as indicated by high variations in distances traveled and a more significant number of linked trips, act as additional drivers of multimodal behavior. Furthermore, our results suggest that multimodal travel behavior is more prominent during the transition from weekdays to weekends. These findings contribute to understanding multimodal travel patterns and can inform the development of strategies to promote sustainable and efficient transport systems.

Monitoring travel behavior is essential for policy and planning. Specifically, understanding transport user groups is crucial for designing an effective transport system and determining travel distances, especially since this information is essential for climate protection measures. This study examines the determinants of passenger kilometers traveled (PKT) using Hierarchical Age-Period-Cohort (HAPC) models, based on the Mobility in Germany survey from 2002, 2008, and 2017. The results reveal a non-linear relationship between age and PKT, with PKT increasing to a peak and then declining thereafter. Gender and economic disparities persist, with men and higher-income individuals traveling greater distances. Urban-rural differences have a significant impact, with urban residents relying more heavily on bicycles and public transportation. Disaggregated analyses by transport mode indicate generational shifts towards bicycle and public transport among cohorts, while car use remains relatively stable across generations. The findings highlight the significance of socio-demographic and contextual factors in shaping mobility, indicating that transport policy should consider generational preferences and structural inequalities to foster equitable and sustainable travel patterns.

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.