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.

In recent years, innovative city logistic concepts for courier, express, and parcel (CEP) shipments have raised particular interest in contributing to a more sustainable transportation system. One solution can be the utilization of existing urban rail infrastructure. A so-called ‘cargo tram’ transports goods to an intermodal city hub, where cargo bikes cover the last leg to the receiver. Although conceptional or economic studies exist, detailed analyses of the transport-related effects, e.g., through freight demand models, still need to be carried out. Hence, in this study, we develop a methodology to integrate urban rail-based parcel transport in terms of a cargo tram into the existing agent-based freight, i.e., parcel, demand model logiTopp. Instead of a single mode choice, a two-stage selection model based on transport chains is developed, comprising a rule-based and utility-based stage. The proposed methodology is implemented in logiTopp, and the model is applied to the city of Karlsruhe, Germany, where the overall effects of rail-based parcel transport are simulated and evaluated in proof-of-concept scenarios. The analysis shows that the proposed transport chain selection model produces overall reasonable results. Moreover, they indicate that the realization of a rail-based parcel transport can reduce the overall mileage and number of trips caused by CEP shipments. However, the potential is driven by several factors, such as the number and location of city hubs, the maximum range of cargo bikes, and the overall evaluation of the cargo tram. Additional empirical investigations are necessary to validate the results from the proof-of-concept scenarios.

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.

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.

To achieve climate goals in the transport sector, many countries are trying to promote the use of public transport. However, to implement effective policies, one must understand the motivations of people who use or do not use public transport today. In this study, we examine the psychographic profiles using latent class analysis to identify the reasons why people use or do not use public transport and link these profiles to reported travel behavior. For the latent class analysis, we use selected psychological items of the German Mobility Panel (MOP), a national household travel survey, that capture attitudes toward public transport. The results highlight four classes that differ based on their psychological profiles: PT-Averse, Privacy Aware Environmentalists, Pragmatists, and PT-Lovers. The results further show that Privacy Aware Environmentalists and PT-Lovers, who have a strong personal norm, frequently use public transport and environmentally friendly transport modes. Thus, the personal norm is a driver of public transport use. The lack of privacy, which the Privacy Aware Environmentalists complain about in public transport, is not a barrier to public transport use.

Travel diaries are a state-of-the-art method to capture peoples’ travel behavior. However, traditional approaches are burdensome for respondents resulting in fatigue and attrition, whereas new methods such as GPS-tracking are costly and fraught with issues of respondents’ privacy of personal data. In this paper, we present an interactive, web-based travel diary, that improves the reporting process while minimizing efforts for survey designers. In a pilot study we show that the approach ensures to record detailed spatial trip information, but still guarantees respondents’ data privacy. The open-source design allows a cost-efficient integration in any survey engine that supports HTML and JavaScript.

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.

A mixed-method approach is used to compare data from a traditional travel diary and an approach called travel skeleton, which is used to capture typical travel behavior. 97 participants first complete the travel skeleton and then a travel diary for one week. The aim of this paper is to quantitatively and qualitatively analyze whether behavioral data from the two approaches can be used as a basis for statistical matching of individuals to generate a synthetic travel diary dataset with multidimensional information. Results show promising intrapersonal overlap between diary and skeleton with inference due to the randomness of reported diary week.

In this paper, we present a novel approach for computing personalized itineraries for individual travel plans throughout one day, considering the wide variety of mobility preferences individuals consider when making itinerary choices. We extend the Traveling Salesman Problem with Time Windows (TSP-TW) by integrating multi-criteria optimization techniques, flexible activities, park-and-ride options, and various transport modes to provide a more comprehensive representation of transportation options. We assess travelers’ mobility preferences, selecting a relevant subset for a real-world itinerary optimization scenario, and employ choice experiments to identify the importance of these preferences for individual decision-makers. The utility functions derived from these experiments are then used for itinerary optimization. We validated our method through simulations in a medium-sized German city, which demonstrated a significant improvement of 16.19% in travel utility when incorporating a utility function into itinerary optimization compared to plans based solely on travel time.

In this paper we present the findings of the 12th ISCTSC conference workshop on commercial trips patterns and demand for goods from firms and households. With discussions structured on 3 key dimensions (subjects of observation, indicators and factors of change), this workshop highlighted the complexity of observational methods combining household and company behaviors. Given this first conclusion, participants identified 2 methodological challenges for commercial trips observation: discussing the pertinence of traditional surveys regarding these forms of mobility and involving respondents in surveys on this complex and sensitive topic. Elements were presented and proposed in the perspective of limiting respondents burden specifically for company related surveys and tailoring survey methods specifically for fast changing behaviors such as e-commerce. An additional challenge identified by the participants was also the need for a better connection between freight and passenger reflexions.

Growing e-commerce activities cause an equally rapid rise in demand for courier, express, and parcel (CEP) transportation, resulting in increased emissions and substantial strain on the road infrastructure, especially in urban areas. Therefore, transportation planners investigate the potential of alternative concepts for parcel and freight transport. Commonly freight demand models are used, however, current models for CEP-based transportation typically focus on private parcel demand. In this paper, we extend the integrated agent-based model logiTopp of last-mile parcel delivery and private travel demand by introducing company agents and, consequently, first-mile deliveries. We present a concept for modeling (the number of) relations between companies and carriers, as these relations highly influence the resulting travel demand in the survey area. Along with implementation techniques, we provide a greedy algorithm to estimate these carrier relations, which takes carrier market shares, customer parcel demand, and carrier capacity into account. All developed model components are solely based on open-source data.

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.

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.

In order to reduce national and global greenhouse gas (GHG) emissions, many countries worldwide have committed themselves to a more sustainable development of their transport sector. Promoting the use of electrical vehicles (EVs) rather than combustion engine cars is one political strategy to achieve a reduction in GHG emissions. To implement targeted and effective promotion measures governments can refer to market diffusion models for EVs. However, in our study we identify that in existing models the consideration of environmental measures is underrepresented. Hence, this paper addresses this gap in current market diffusion models for EVs by particular focusing on environmental effects as additional influencing factors of the market diffusion. Results are drawn for the German car market with a market diffusion simulation until 2050 applying the market diffusion model ALADIN considering the introduction of distinct CO2 tax trajectories. The results are analyzed based on scenarios, where (i) no CO2 tax, (ii) the current governmental plan for a CO2 tax, and (iii) a considerable high CO2 tax is applied. Additional insights when incrementally increasing the CO2 tax are provided. The scenario analysis shows that the market diffusion is highly dependent on the evolution of external factors. A CO2 tax considerably higher than the current governmental plan by 2030 (such as 150€/t, based on its monetary value by 2020) is required to have a meaningful impact on the market diffusion of EVs. Moreover, applying a considerable high CO2 tax leads to a slower growth of BEV and PHEV from 2040 onwards that is compensated by a growth in FCEV vehicles.

Recently, a substantial increase in parcel volumes has been observable, primarily shipped by courier, express, and parcel service providers (CEPSPs). Especially in urban areas, existing space conflicts are intensified, while emissions are steadily rising due to the higher need of parcel transportation. From a regional planning perspective, understanding the net effect of increased parcel volumes is essential for transportation planning and policies, for which freight demand models are commonly used. Existing models primarily focus on parcel deliveries to private customers, although parcel shipments to and from companies considerably contribute to the overall parcel volumes. Hence, this study aims to develop an agent-based model that explicitly represents the in- and outgoing parcel volumes of companies in urban areas delivered by CEPSPs. An approach based on Open Data and self-conducted expert interviews with CEPSPs is developed. First, OpenStreetMap data is used to geographically represent companies with the corresponding sector assignment within a study area in Karlsruhe, Germany. Second, a concept for modeling the weekly in- and outgoing parcel volume for each company in the study area is developed using literature-based data. The approach is integrated into the existing agent-based framework logiTopp considering all relevant CEPSPs of the respective area. The application shows that modeling CEP-based transportation volumes of companies based on Open Data is possible though restrictions apply to the granularity of the used data. However, potential is seen in generating a well-funded empirical database of companies’ in- and outgoing parcel demand structures to improve the model further.

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.