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City logistics plays a central role in supplying and disposing goods for establishments and residents in urban areas. However, the steadily rising demand for transporting goods puts cities under pressure. Hence, municipalities strive for alternative solutions for urban freight transport, especially parcel shipments on the first and last mile. Freight demand models are suitable to evaluate the transport-related effects of such solutions. However, developing those models requires a sufficient amount of data, which, to date, especially for establishments, cannot be covered in its necessary scope and accuracy by publicly available sources. Although parcel shipments to and from establishments make up to 40 % of the overall courier, express, and parcel market, these are often neglected in existing modelling approaches. Hence, in this study, we present a data collection concept for generating highly relevant data for the microscopic modelling of urban freight, i.e., parcel transport focusing on establishments. To reflect transport demand (i.e., establishments that need to have goods shipped) and transport supply (i.e., carriers that provide a transport service), a mixed-method approach is developed comprising complementary components. On the one hand, an online establishment survey is designed aiming to reveal disaggregated transport demand data for the subsequent modelling process. The survey focuses on the delivery and shipment characteristics of goods, such as temporal and spatial demand patterns. On the other hand, expert interviews are conceptualized to identify relevant patterns of transport supply carriers such as courier, express, and parcel service providers and shall further work as secondary data for the modelling process. The approach is applied in the region of Karlsruhe, Germany. It can be shown that the survey is generally suitable for generating freight transport data on a disaggregated level and that the mixed-method approach is capable of mutually validating the data obtained. However, our approach also emphasizes the necessity to conduct an establishment survey as a personal rather than a self-reporting interview, even if the costs are higher.

Abstract Autonomous on-demand services as part of public transport are discussed to improve public transport substantially. A household survey in Karlsruhe, Germany, was conducted among inhabitants of a residential area where a combined autonomous and on-demand minibus service with automation level 4 was offered. The study investigates the residents’ appraisal of this service and reasons for using and not using it. Results indicate that people generally have a positive attitude towards it and are willing to use it in the future. Difficulties are found in travel speed, availability, and complexity of using such a new service. Favorable factors in the intention to use the service are having a mobility impairment, being open to other forms of new mobility, and not having a car in the household. In the future, to be successful, such services should improve travel times and reliability and address issues of their primary target group, such as the high complexity of accessing these services.

In recent years, machine learning techniques have been increasingly tested and applied to physically collected data to optimize the processes. In this paper, machine learning is used to check travel survey data of the German Mobility Panel (MOP). In the MOP, verified and raw data have been available for several decades, on which algorithms can learn the practices of human checking routines. By using machine learning, the algorithm is expected to learn the checking patterns from the past and thus support the data checking of new datasets. To this aim, several algorithms are applied and tested. The presented model framework supports the identification of blatant deficits in the reports at the individual and trip levels. The neural network (NN) shows the most promising results as it decreases the number of data samples checked. The checking effort can be reduced by 20.4 % at the individual trip level. This work shows that machine learning can support the data checking process in the MOP at various levels, thus leading to significant time reduction.

The use of on-demand mobility (ODM) services has increased in Chinese cities and is used by people for various purposes, such as leisure activities or commuting. The aim of this study is to identify and analyze factors that play a role in the use of ODM services for commuting of high-income earners in China. In previous studies, this group of people was identified as extremely relevant for ODM use as they can afford the services in principle. A specific focus of this study is on the influence of travel mode attitudes as well as sociodemographic characteristics. The data set used in this study was collected with the innovative travel skeleton approach based on information given by high-income individuals. The survey took place in eight different Chinese cities with 5,192 respondents. They have provided insights on their everyday travel (e.g., commuting) and attitudes towards car and public transit. To investigate the role of psychological factors behind the use frequency of ODM services, we applied a factor analysis to identify latent factors from psychological item sets used. Next, we integrated them into an ordered hybrid choice model (OHCM). The results show that people’s perceived public transit experience increase the probability to use ODM more often for commuting. We suggest a strong interrelation between public transit and such services, even among people with high incomes.

Abstract Background The car has so far played an important role for transporting goods. However, new services emerging from e-commerce may increasingly reduce its relevance as the transporting of goods might no longer be a reason for car use. As a result, e-commerce or the delivery of goods by third-parties can function as potential supplement for car-free households and support a car-free lifestyle. To assess this potential, appropriate segmentation to subgroups is needed to better understand differences in shopping behavior and the linked role of the car. Methods The presented study from Munich (Germany) provides a comprehensive approach by applying a latent class analysis. The classification revealed six distinct classes with differences in shopping behavior as well as sociodemographic and spatial characteristics. To asses underlying motivations, this approach is complemented through relating the latent classes to attitudes towards shopping and mode choice. Findings Results show that those people who frequently use their cars also have an affinity for frequent online shopping. This relationship should be considered when discussing whether e-commerce can promote a car-free lifestyle.

Abstract Purpose Ridesourcing services have become popular recently and play a crucial role in Mobility as a Service (MaaS) offers. With their increasing importance, the need arises to integrate them into travel demand models to investigate transport system-related effects. As strong interdependencies between different people’s choices exist, microscopic and agent-based model approaches are especially suitable for their simulation. Method This paper presents the integration of shared and non-shared ridesourcing services (i.e., ride-hailing and ride-pooling) into the agent-based travel demand model mobiTopp. We include a simple vehicle allocation and fleet control component and extend the mode choice by the ridesourcing service. Thus, ridesourcing is integrated into the decision-making processes on an agent’s level, based on the system’s specific current performance, considering current waiting times and detours, among other data. Results and Discussion In this paper, we analyze the results concerning provider-related figures such as the number of bookings, trip times, and occupation rates, as well as effects on other travel modes. We performed simulation runs in an exemplary scenario with several variations with up to 1600 vehicles for the city of Stuttgart, Germany. This extension for mobiTopp provides insights into interdependencies between ridesourcing services and other travel modes and may help design and regulate ridesourcing services.

New mobility concepts such as shared, autonomous, electric vehicle (SAEV) fleets raise questions to the vehicles’ technical design. Compared to privately owned human driven cars, SAEVs are expected to exhibit different load profiles that entail the need for newly dimensioned powertrain and battery components. Since vehicle architecture is very sensitive to operating characteristics, detailed SAEV driving cycles are crucial for requirement engineering. As real world measurements reach their limit with new mobility concepts, this contribution seeks to evaluate three different traffic simulation approaches in their ability to model detailed SAEV driving profiles. (i) The mesoscopic traffic simulation framework MATSim is analyzed as it is predestined for large-scale fleet simulation and allows the tracking of individual vehicles. (ii) To improve driving dynamics, MATSim’s simplified velocity profiles are enhanced with real-world driving cycles. (iii) A sequential tool-coupling of MATSim with the microscopic traffic simulation tool SUMO is pursued. All three approaches are compared and evaluated by means of a comprehensive test case study. The simulation results are compared in terms of driving dynamics and energy related key performance indicators (KPI) and then benchmarked against real driving cycles. The sequential tool-coupling approach shows the greatest potential to generate reliable SAEV driving profiles.

During wayfinding pedestrians do not always choose the shortest available route. Instead, route choices are guided by several well-known wayfinding strategies or heuristics. These heuristics minimize cognitive effort and usually lead to satisfactory route choices. Our previous study evaluated the costs of four well-known pedestrian wayfinding heuristics and their variation across nine network morphologies. It was observed that the variation in the cost of these wayfinding heuristics increased with an increase in the irregularity of the network, indicating that people may opt for more diverse heuristics while walking through relatively regular networks, and may prefer specific heuristics in the relatively irregular ones. The study presented here aims to investigate this claim by comparing simulated routes with observed pedestrian trajectories in Beijing and Melbourne, two cities at opposite ends of the regularity spectrum. We found that the values of mean route length and mean Network Hausdorff Distance for walking trips made in Melbourne were consistently lesser than the corresponding values obtained in Beijing. Also, across both the cities, we found that while there was minimal variation in the popularity of heuristics overall, in cases where different heuristics produced dissimilar routes, the shortest leg first strategy and the least angle strategy were more popular.

Being a vast external influence, the COVID-19 pandemic causes major changes in travel behavior on the individual level. This exceptional situation and the political measures such as the lockdown lead to decreased overall travel demand and shifts in mode choice. To understand these effects, the analysis of car usage in private households offers explanatory insights. To this aim, this study provides a differentiation of car usage before and during the COVID -19 pandemic in spring 2020. Furthermore, insights into structural changes of car usage in private households based on German Mobility Panel data are presented. The results show that during the COVID -19 pandemic car usage was affected, and overall mileage decreased. Especially newer cars with large cubic capacity show a large decrease in mileage by 39 % between 2019 and 2020. In addition, we see that the type of car usage (business/private) and the household characteristics are related to different magnitudes of change in car usage. Overall, it becomes evident that examining identical households and cars before and during the pandemic allows for a deeper understanding of the impacts of the COVID -19 pandemic.

In this paper, we present an approach to segment people based on a visualization of the longitudinal week activity data from the German Mobility Panel. In order to perform segmentations, different clustering methods are commonly used. Most of the approaches require comprehensive prior knowledge about the input data, e.g., condensing information to cluster-forming variables. As this may influence the method itself, we used images with a high degree of freedom. These images show week activity schedules of people, including all trips and activities with their purposes, modes as well as their duration or their temporal position within the week. Thus, we answer the question whether using only this type of image data as input will produce reasonable clustering results as well. For the clustering, we extracted the images from an existing tool, processed them for the method and finally used them again to select the final cluster solution based on the visual impression of cluster assignments. Our results are meaningful as we identified seven activity patterns (clusters) using this visual validation. The approach is confirmed by the data-based analysis of the cluster solution showing also interpretable key figures for all patterns. Thus, we show an approach taking into account many aspects of travel behavior as an input to clustering, while ensuring the interpretability of solutions. Usually, key figures from the data are used for validation, but this practice may obscure some aspects of the longitudinal data, which are visible when looking on the images as validation.

Transport systems are affected by fundamental technical and social dynamics. For planners and researchers, travel demand models are an important tool to gain insights into possible effects of these dynamics and to find appropriate ways of dealing with them. However, most state-of-the-art travel demand models underestimate social aspects of travel choices, which we consider essential for understanding stability and variability of travel behavior. Based on a qualitative interview study, the paper presents an interdisciplinary approach to consider social aspects for modeling shopping destination choice. Starting point for our considerations is that people are social beings, moving around to build and maintain relationships, and that these relationships only unfold in relation to overall sociotechnical structures. The interview study provides evidence for relationships between stores and customers. Relationships can be distinguished in two dimensions. First, in terms of the nature of a relationship: having a relationship either with the owner of a specific store or towards specific brands. Second, in terms of the meaning: if the relationships is perceived meaningfull or obligatory. Findings are represented in a first modeling approach. The results show that the more seldom relationships to owners of a store can be modeled easily, while the more typical relationships to brands require further research.

Travel behavior can be determined by its spatial context. If there are many shops and restaurants in close proximity, various activities can be done by walking or cycling, and a car is not needed. It is also more difficult (e.g., parking space, traffic jams) to use a car in high-density areas. Overall, travel behavior and dependencies on travel behavior are influenced by urbanity. These relationships have so far only been examined very selectively (e.g., at city level) and not in international comparison. In this study we define an Urbanity Index (UI) at zip code level, which considers factors influencing mobility, international comparability, reproducibility as well as practical application and the development of a scalable methodology. In order to describe urbanity, data were collected regarding spatial structure, population, land use, and public transport. We developed the UI using a supervised machine learning technique which divides zip codes into four area types: (1) super-urban, (2) urban, (3) suburban/small town, (4) rural. To train the model, the perception from experts in known zip codes concerning urbanity and mobility was set as ground truth. With the UI, it is possible to compare countries (Germany and France) with a uniform definition and comparable datasets.

To counteract climate change, electric vehicles are replacing vehicles with internal combustion engine on the automotive market. Therefore, electric vehicles must be accepted and used like conventional vehicles. This study aims to investigate to which extent electric vehicles are already being used like conventional vehicles. To do this, we present a supervised method where we combine usage data from conventional vehicles (from car use model based on survey data) and electric vehicles (from sensor data) in Germany and California. Based on conventional vehicles, eight car usage profiles were defined by hierarchical clustering in a previous study. Using a softmax regression, we estimate for each electric vehicle a probability of assignment for every car usage profile. Comparison of conventional and electric vehicles with a high probability reveals that electric vehicles are used similar for long-distance travel (>100 km) and different for short-distance travel (

The German government seeks to increase the number of electric vehicles (EV) in the German car fleet to one million by 2020. Since some characteristics of EVs differ from conventional cars, there is an increasing need to assess the various impacts of a growing EV fleet. In this work, we have focused on possible effects related to the field of transport. We identified three important aspects and evaluated them over a period of one week using the microscopic travel demand model mobiTopp. First, we modelled the potential EV user groups of the near future by developing an EV user model; this model considers both interest in EVs and suitability for EV usage. Second, we simulated the travel behaviour of EV users; we used an EV usage model to consider the restrictions of EVs in choice decisions and also compared the usage behaviour of EV and conventional cars users. Third, we analysed the power consumption of the simulated EVs and evaluated the load peaks based on the simulated travel patterns. Our results indicate that a growing EV fleet implies a more heterogeneous distribution of EVs among car owners. They also indicate that the trip chain length of battery electric vehicles (BEVs) is much lower than that of extended range electric vehicles (EREVs) and conventional cars on average.