Kai Nagel

The largest on-demand ridepooling (DRT; demand-responsive transport) service in a single European city has officially been part of Hamburg’s public transit system since 2023. Policy makers, practitioners, and planners aim to provide a holistic mobility offering and by doing so, reduce the dependency on private car usage. Against this background, an agent-based simulation is presented and deployed to investigate how various pricing schemes influence the ridership of DRT service, with a particular focus on connections to/from traditional public transit (PT). This involves a novel process that addresses a common problem of service overcrowding while simulating fixed-size DRT fleets with a low mode share in agent-based transport models. The results suggest that a DRT discount for PT season ticket holders significantly increased overall ridership, whereas the number of intermodal trips remained constant. Similar results were observed for PT-quality-dependent pricing schemes, in which DRT was surcharged if parallel PT connections were relatively good, or discounted if they were relatively poor or nonexistent. In contrast, a direct discount for intermodal trips increased the share and the absolute number of intermodal trips, which tended to replace direct DRT trips. Most importantly, the results indicated a tradeoff between operator revenue (or the need for subsidies) and the share of intermodal trips.

Under a 2021 amendment to German law, the KelRide project became the first public on-demand service operating electric autonomous vehicles (AVs) without fixed routes on public roads. This paper addresses two notable gaps in the literature by (1) conducting an ex post evaluation of demand predictions for a non-infrastructure (Mobility-on-Demand (MoD)) project and (2) using real-world data to analyze how demand responds to key Autonomous Mobility-on-Demand (AMoD) system parameters in a rural context. Earlier simulation-based demand forecasts are compared to observed booking data, and the recalibrated model is used to investigate the sensitivity of passenger numbers to vehicle speed, fleet size, service area, operating hours, and idle vehicle positioning. Results show that increasing vehicle speed leads to a superlinear rise in passenger numbers—especially at small fleet sizes—while demand saturates at large fleet sizes. A linear increase in demand is observed with expanding service areas, provided fleet size is sufficient. Extending operating hours from 9 a.m.–4 p.m. to full-day service increases demand by a factor of two to four. Passengers numbers also vary notably depending on the positioning of idle vehicles. Consistent with empirical findings, the analysis underscores that raising AV speed is essential for ensuring the long-term viability of autonomous mobility services.

Background: The COVID-19 pandemic disrupted social life and forced people to reconsider how, where, and with whom to spend time. These decisions are deeply personal and their intricacies are still poorly understood. Methods: To understand how people make such decisions, we conducted an online survey in summer 2023, collecting self-reported absolute contact numbers for four time points: 2019, 03/2020, summer 2021, and 01/2023. We analyzed the resulting contact data, focusing on the quantification of heterogeneities in reductions. Results: Analysis of the survey data revealed that the COVID-19 pandemic triggered substantial reductions in both the work and the leisure context. Mean reductions gradually decreased as time progressed, but by 01/2023 contact numbers remained below pre-pandemic levels. We found contact behavior to demonstrate heterogeneity in three different aspects. First, the distribution of contact reductions followed a bimodal pattern, with a distinct peak at either extreme: A large fraction of the survey participants initially strongly reduced their contacts, a smaller group maintained nearly normal contact levels, and the remainder of participants reduced their contacts intermediately. Consistent with the decrease of mean contact reductions, the relative sizes of these behavioral groups shifted over time, with participants relaxing their reductions incrementally. Second, we found risk perception to be an indicator for the strength of contact reductions: Risk-averse participants reduced their leisure contacts significantly more than risk-tolerant participants, resulting in a trend of both fewer and later COVID-19 infections. Neither age, gender, nor having a COVID-19-relevant comorbidity significantly influenced self-reported contact reductions. Third, the survey results provide evidence that social homophily persisted during the COVID-19 pandemic, revealing a correlation between participants’ and their closest contacts’ number of contacts during the COVID-19 pandemic. Risk-averse participants hereby especially preferred to maintain contact with equally careful individuals. Conclusions: Our study emphasizes the time-dependency and heterogeneity of contact reductions. On the one hand, our findings can easily be integrated into epidemiological models, improving their accuracy and predictive power. On the other hand, the results may guide the design of effective public health interventions, and help to predict and understand their effectiveness.

Striving for better simulation results, transport planners want to simulate larger domains with increased levels of detail. Achieving fast execution times for these complex traffic simulations requires the parallel computing power of modern hardware. This paper presents an architectural update to the MATSim traffic simulation framework, introducing a prototype that adapts the existing traffic flow model to a distributed parallel algorithm. The prototype is capable of scaling across multiple compute nodes, utilizing the parallel computing power of modern hardware. Benchmarking reveals a 119-fold improvement in execution speed over the current implementation, and a 43 times speedup when compared to single-core performance. The prototype can simulate 24 h of large-scale traffic in just 3.5 s. Based on these results, we advocate for integrating a distributed simulation approach into MATSim and outline steps for further optimizing the prototype for large-scale applications.

This study presents a framework for integrating traffic simulation with high-resolution air pollution modeling to design adaptive traffic management policies aimed at reducing urban air pollution. Building on prior work that establishes the coupling of the MATSim traffic model with the PALM-4U urban climate model, this second part focuses on implementing a feedback loop to inform traffic management decisions based on simulated air pollution concentration levels. The research explores how traffic volumes and atmospheric conditions, such as boundary layer dynamics, influence air quality throughout the day. In an artificial case study of Berlin, a time-based toll is introduced, aimed at mitigating concentration peaks in the morning hours. The toll scheme is tested in two simulation scenarios and evaluated regarding the effectiveness of reducing air pollution levels, particularly NO₂ during the morning hours. The case study results serve to illustrate the framework’s capabilities and highlight the potential of integrating traffic and environmental models for adaptive policy design. The presented approach provides a model for responsive urban traffic management, effectively aligning transportation policies with environmental goals to improve air quality in urban settings.

This study conducts a detailed analysis of population mobility during the COVID-19 pandemic, utilizing a unique approach that contrasts two types of mobile phone data: GPS-based and cell-based. The primary objective is to evaluate the effects of governmental restrictions on a variety of activities including school attendance, work, shopping, and leisure. We compare both data sets by using a set of defined criteria, including anticipated activity reductions during full and partial closures, as well as the timing of activity changes in response to policy implementations. Our research reveals that while cell-based data lacks the precision to differentiate between various out-of-home activities effectively, GPS-based data, especially when integrated with OpenStreetMap, proves significantly more adept at identifying and categorizing specific activity types. The GPS-based data shows, for example, that school activities fell by more than 80% while work activities were only reduced by around 50%.