All Publications
The concept of autonomous combined transport utilizes autonomous vehicles for simultaneous passenger and goods transportation and is associated with a reduction in road traffic through bundling effects and economies of scale. Yet, its potential benefits particularly hinge on penetration and utilization rates. To date, few studies have explored user acceptance of systems that integrate passenger and freight flows. In this context, a research gap that is particularly evident pertains to the user acceptance of different concepts within autonomous combined transport. Extending the unified theory of acceptance and use of technology (UTAUT2), this paper examines the effects of six psychological constructs on the behavioral intention to use an autonomous combined transport system. Surveying 1040 respondents from Germany, two distinct combined transport concepts—scheduled and on-demand—were examined to identify key acceptance factors and operational peculiarities. Results from structural equation modelling show that the acceptance of autonomous combined transport systems depends on both, the operational concept as well as the purpose of use, with socio-demographic characteristics featuring different indirect effects per concept. Moreover, we find that performance expectancy, effort expectancy, price value, personal attitude, and trust are significant predictors of behavioral intention across both concepts, while the average order frequency of a potential user has a negative indirect impact on behavioral intention. Results show that an extended UTAUT2 model can conceptualize factors influencing autonomous combined transport acceptance, emphasizing the importance of investigating a user’s behavioral intention based on the specific operational concept.
Food supply systems are national critical infrastructures embedded in changing and uncertain environments. Hence, testing and evaluating them in their ability to meet food supply is key to reduce vulnerability to shortages. This paper presents an optimization approach to assess the resilience of nationwide food supply systems using the N-1 contingency criteria, which investigates whether the isolation of one region from the transport network destabilizes the food supply. To this end, we build a multi-regional multi-commodity large-scale model for food flow networks. Then, we implement a constraint optimization problem to find the management of food flows along the supply chain stages that minimize shortage, costs and penalties induced by the disruption for both the isolated and connected system. Lastly, resilience is quantified with established metrics. A numerical case study illustrates the proposed method, revealing which regions are critical to maintain the stability of the national food supply.
We develop an inventory control policy for perishable products considering both random demand and random lead time. We consider a B2C retail environment where excess demand is lost. The policy dynamically determines the optimal replenishment quantity under a service level constraint in every period, allowing for order-crossing, a widely disregarded characteristic in the literature. Regarding perishability, we compare the two most extreme issuing policies, first-expired-first-out (FEFO) and last-expired-first-out (LEFO), and evaluate our policy to existing inventory policies for perishables that typically ignore lead time uncertainty.
We obtain several interesting findings. First, we show that ignoring lead time uncertainty and planning based on the expected lead time significantly undershoots the target service level. Even planning with the maximum lead time, under LEFO, the achieved service level would still fall considerably below the target, which the lost-sales structure can explain. On the other hand, under FEFO, the achieved service level would overshoot the target service level, which leads to unnecessary waste. Second, a more reliable lead time can significantly reduce waste, especially under LEFO. Third, our model allows us to distinguish between past, present, and future lead time uncertainty and thus to consider partial lead time information. We show the value of lead time information on outstanding orders. Fourth, we evaluate the impact of a fast but unreliable delivery option and a slow but reliable delivery option on the retailer’s average waste and ordering process. We find that the optimal choice depends on the demand characteristics.
Shifting the food system to a more sustainable one requires changes on both sides of the supply chain, with the consumer playing a key role. Therefore, understanding the factors that positively correlate with increased organic food sales over time for an entire population can help guide policymakers, industry, and research to increase this transition further. Using a statistical approach, we developed a spatial pooled cross-sectional model to analyze factors that positively correlate with an increased demand for organic food sales over 20 years (1999–2019) for an entire region (the city-state of Hamburg, Germany), accounting for spatial effects through the spatial error model, spatially lagged X model, and spatial Durbin error model. The results indicated that voting behavior strongly correlated with increased organic food sales over time. Specifically, areas with a higher number of residents that voted for a political party with a core focus on environmental issues, the Greens and the Left Party in Germany. However, there is a stronger connection with the more “radical” Left Party than with the “mainstream” Green Party, which may provide evidence for the attitude-behavior gap, as Left Party supporters are very convinced of their attitudes (pro-environment) and behavior thus follows. By including time and space, this analysis is the first to summarize developments over time for a metropolitan population while accounting for spatial effects and identifying areas for targeted marketing that need further motivation to increase organic food sales.
Motivation and literature
In recent years, interest in intermodal rail transport has increased among academics. This is mainly triggered by the European Union's ambition to decarbonize transport. The ambitions are described in the white paper on transport. One of the main goals is to shift thirty per cent of long-distance road freight over 300 kilometers to rail or waterborne transport by 2030 and more than fifty per cent by 2050 (European Commission, 2011). Intermodal transport plays an important role in execution. Several case studies have been done on intermodal transport within a specific region (Frémont and Franc, 2010 and Arnold et al., 2004), but only one empirical study is available with a holistic overview of intermodal transport in Europe. Because the transport market is an open market with many continuous changes in transport flows, the study is not up-to-date anymore. In addition, NUTS1-level statistics are available from 2020 on intermodal rail transport (UIC, 2020). However, there is a lack of data at NUTS2 level and NUTS3 level for microanalysis. In this study, we update the study by de Langen (2017) and try to fill the gap at NUTS2 and NUTS3 levels.
Objective
The main objective of this research is to propose a market overview of all intermodal rail connections in Europe on NUTS2-level and NUTS3-level as of March 2023. The market overview and dataset can serve as a starting point to define new public statistics on multimodal transport and for research by other scholars.
Outline and structure
The first step in the market-analysis is to analyze the market structure focused on the possible roles within the intermodal transport environment. For this purpose, 10 to 15 interviews are conducted with various market players in the intermodal transport environment. Based on the structure and the interviews, market players can be identified to map the market. In the second step, it is first determined which information is used to create a database with all rail connections of intermodal transport. Subsequently, it is examined which sources are publicly available that meet the criteria. This determines which measurable characteristics there are to build a database. The sources are described and it is explained how the information can be used. The third step then explains how the data can be verified using public information from railway network operators in individual countries in Europe and using secondary sources. The results section provides a broad overview of the market structure and available connections. The results show characteristics of the entire market and a division into market segments. A geographical analysis om NUTS3 level and market shares of the identified market players by operator, rail carriers and terminal operators are provided. Subsequently, the results and limitations of the study are discussed. This leads to recommendations for the use of the data and for further research. Finally, the database with 1000+ datasets (intermodal connections), including characteristics, will be published in the appendix.
This study aims to provide a complete overview of intermodal rail connections in Germany and identify the market players involved in their operation. The lack of a comprehensive overview is attributed to the difficulty of summarizing empirical data of intermodal rail transport, combined with the many rapid changes in the dynamic open market. The study uses a dataset compiled through online research and interviews with market players. The identified market players include intermodal operators, railway carriers, terminals, and ports.
The case revolves around transport planning under difficult circumstances. The participants must schedule and coordinate the transport of a container from the South of Germany to the port of Hamburg. The transport can take place in two ways, with a total of four possible routes via direct road transport or intermodal rail transport from three different hinterland terminals. The transport routes are compared based on three criteria, (1) transport price, (2) emissions, and (3) reliability. The case contains a role play in which the participants learn the advantages and disadvantages of direct road transport compared to intermodal rail transport. For the planning process an online tool is provided. Four fixed routes are programmed into the tool. The routes consist of various transport parts with a fixed transport route, a fixed transport duration, and fixed emission values. The participants can use the tool to plan the transport by planning departure times, and planning buffers between the parts of the transport. The case is structured in such a way that all routes have advantages and disadvantages, so that it cannot be said that there is one correct answer. The case should stimulate discussion among participants.
To address climate change, transport policy tries to accelerate the electrification of vehicles. The impact of policy measures taken is difficult to predict especially in areas like commercial passenger transport where few research exists. In this study we estimate the impact of higher availability of charging infrastructure on the electrification potential of vehicles used in commercial passenger transport. For Hamburg we estimate that the electrification potential could increase by about 35%. We base this analysis on a company survey on vehicle usage patterns in commercial passenger transport and a ranked choice model to quantify the relationship between company sectors and tour patterns. This enables us to estimate the impact on electrification potential for overall Hamburg. The methodological contribution of this paper is to demonstrate a statistically viable approach to extrapolate insights from a behavioural survey to an overall region in the context of commercial passenger transport.
Freight transport and logistics are the backbone for business and society. This becomes especially obvious in times of crisis such as the Corona crisis or the war in Ukraine. While necessary, applied research in this area is particularly difficult since data access is limited, and heterogeneity of players and problems is high. These might be the reasons why less research has been conducted compared to neighboring fields such as passenger transport. This Themed Volume has brought together multiple new contributions in the field of freight transport and logistics. The Volume is multidisciplinary with articles using different methodologies including empirical work, statistical analysis, simulation, or optimization.
Many articles originate from contributions to the 2019 European Transport Conference (ETC). After being selected they went through a thorough review process together with other papers submitted to this Themed Volume. The review process for the Themed Volume took place over the period 2020 to 2022.
The papers cover multiple domains and include almost all modes of transport. Especially remarkable are four papers in the domain of rail freight, an area in which there is a dearth of papers. A number of articles cover two of the current big topics: new technologies and sustainability. This Themed Volume also includes contributions in “classical” topics of freight transport and logistics namely transport planning, carrier and location choice.
The Themed Volume comprises 16 articles. Nine of the articles take a mcro-perspective, looking at problems and analysis at organisational level. The other seven articles take a macro-perspective, detailing transport system observations of relevance for policy decisions.
In diesem Kapitel unterscheiden wir zwei Arten von Start-ups in der Logistik: nutzerbasierte und produzientenbasierte Start-ups. Sehr wichtig für den Erfolg eines Start-ups ist, neben der Geschäftsidee selbst, auch der Zugang zu Ressourcen. Ziel des Kapitels ist es, nutzer- oder produzentenbasierte deutsche Logistik-Start-ups zu analysieren und für ihren Erfolg benötigte Ressourcen zu identifizieren. Dazu wurden Interviews mit 19 deutschen Logistik-Start-ups durchgeführt, auf deren Basis erste Hypothesen formuliert werden. Die Ergebnisse legen nahe, dass Innovation in deutschen Start-ups eher produzentenbasiert ist, beide Arten von Innovation (sowohl produzenten- als auch nutzerbasierte) in der Logistik aber erfolgreich sein können. In beiden Fällen scheint bei den erfolgreichen Start-ups jedoch eine enge Verknüpfung mit der Logistikindustrie vorzuliegen, ob als Zulieferer von komplementären Ressourcen oder als Kunde.
Discussions on sustainability in freight transportation often result in the claim that more goods should be transported via rail instead of by truck. The introduction of the CO2 emission tax from 2021 onward in Germany might positively influence the economic competitiveness of the railway sector. Complete trains (direct trains from point to point) were already rather efficient and attractive before. However, single wagon networks struggle and constantly lose market share. For mixed load cargo (shipments with one to several pallets) the single wagon network is not used at all anymore. One initiative, so-called 'Railports', aim to make the latter more efficient and attractive to the market and therefore maybe help to accelerate the modal shift. In this case students should gain insights into the complexity of investment decisions in an environment of a natural monopoly, a publicly sponsored, regulated company and the threat of regulated access to properties/infrastructure with high initial investment costs. Students should identify and discuss the possibly opposing positions of society and the investor as well as the point of view of competitors. A fictive hearing in the form of a role-play should improve the students’ negotiation skills and their argumentation skills from different perspectives.
Transparency in transport processes is becoming increasingly important for transport companies to improve internal processes and to be able to compete for customers. One important element to increase transparency is reliable, up-to-date and accurate arrival time prediction, commonly referred to as estimated time of arrival (ETA). ETAs are not easy to determine, especially for intermodal freight transports, in which freight is transported in an intermodal container, using multiple modes of transportation. This computational study describes the structure of an ETA prediction model for intermodal freight transport networks (IFTN), in which schedule-based and non-schedule-based transports are combined, based on machine learning (ML). For each leg of the intermodal freight transport, an individual ML prediction model is developed and trained using the corresponding historical transport data and external data. The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail. Consequently, the outcome of this research allows decision makers to proactively communicate disruption effects to actors along the intermodal transportation chain. These actors can then initiate measures to counteract potential critical delays at subsequent stages of transport. This approach leads to increased process efficiency for all actors in the realization of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs.
Freight Transport Modeling in Emerging Countries examines freight transport models developed in emerging countries including Turkey, South Africa, India, Chile, and more. It provides a toolbox of successful freight transport model applications, alternative data collection methods, and evaluation techniques for the development of future policies. The book offers solutions for issues related to the urban, national, and international transportation of goods and examines new advances in freight transport models and data collection techniques and their applications in emerging countries.
Emerging countries have unique transport-related policies, regulatory structures, logistics systems, and long-term uncertainties that hinder their economic development. This book tackles these issues by examining decision-making models for locating logistics sites such as ports and distribution centers, modeling urban freight movements in megacities and port cities, using existing datasets to get information when data is not available, implementing policies related to the national and international movements of goods, and more.
Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in the area adjacent to their home location. This paper aims to fill this gap by introducing a gravity-based approach to model food-flows from supermarkets to consumers and demonstrating how models of consumer shopping behavior can be used to improve computational methodologies to infer the source of an outbreak of foodborne disease. To demonstrate our approach, we develop and calibrate a gravity model of German retail shopping behavior at the postal-code level. Modeling results show that on average about 70 percent of all groceries are sourced from non-home zip codes. The value of considering shopping behavior in computational approaches for inferring the source of an outbreak is illustrated through an application example to identify a retail brand source of an outbreak. We demonstrate a significant increase in the accuracy of a network-theoretic source estimator for the outbreak source when the gravity model is included in the food supply network compared with the baseline case when contaminated individuals are assumed to shop only in their home location. Our approach illustrates how gravity models can enrich computational inference models for identifying the source (retail brand, food item, location) of an outbreak of foodborne disease. More broadly, results show how gravity models can contribute to computational approaches to model consumer shopping interactions relating to retail food environments, nutrition, and public health.
Im SMECS-Projekt wurde durch Anwendung von KI-Verfahren des Maschinellen Lernens ein IT-System entwickelt, welches dynamisch die Ankunftszeit (ETA) von Containertransporten im intermodalen Vorlauf der maritimen Transportkette prognostiziert. Das System erkennt proaktiv Konflikte bei der Einhaltung des geplanten Transportverlaufes vom Warenversender bis zum Seehafen und befähigt die Anwender zu einer zielgerichteten und effizienten Durchführung geeigneter Störungsmaßnahmen durch die Vorgabe von akteursspezifischen Handlungsempfehlungen.
This paper presents a calibrated dynamic multi-scale multi-regional input–output (MSMRIO) model of the German food supply system based on real data. The model comprises 51 commodity groups from farm to fork differentiating three different temperature ranges as well as living animals. Spatially, it works on an aggregate level of 402 regions within Germany as well as its 50 most important trading nations. It determines the commodity flows and the additionally needed transport capacity in case of disruptions. Showing how changes in production, inventories, sourcing, and consumption affect commodity flows, the model uncovers vulnerabilities and makes risk evaluation possible.
Food is an important resource in disaster management, and food stock levels hold significance for disaster mitigation research and practice. The presence or absence of food stocks is a vulnerability indicator of a region. A large part of overall food stock, before a disaster strikes, is held by private companies (retailers, wholesalers and food producers). However, there is little-to-no information on the food stock levels of commercial companies, and no approach exists to derive such information. We develop an approximation model based on essential inventory management principles and available data sources to estimate aggregated food stock levels in supply networks. The model is applied in a case example that features dairy product stock levels in the German state of Saxonia. The resulting overall stock levels are normalised, and their usability is showcased in a simple vulnerability analysis. Disaster managers are provided with a model that can be used estimate otherwise unavailable data and facilitates investigations into the regional resilience of an area. The limitations of our study are based on the aggregated nature of the supply network structure and data usage (i.e. in the model, we do not consider any seasonality or trend effects).
