The e-commerce sector’s rapid expansion has led to an increase in delivery activities both within and across cities, fuelling the growth of the courier, express, and parcel (CEP) services. CEP service providers are crucial for the distribution of goods across all types of cities, especially for last-mile delivery. However, CEP service providers need innovative approaches for their last-mile distribution in small- and medium-sized cities to reduce transport costs and negative environmental impacts. For this reason, this paper analyses the quantitative impacts of logistics measures of CEP service providers for last-mile delivery in small- and medium-sized cities, especially the resulting transport costs and environmental impacts, in the framework of a case study for the investigation area of the Aachen city region. A simulation-based analysis was conducted using the agent-based transport simulation MATSim and the linked route optimisation Jsprit. The results revealed that electric trucks are not cost-effective as a stand-alone logistics measure for last-mile delivery in small- and medium-sized cities. However, combining electric trucks with other sustainable logistics measures, such as parcel shops and parcel lockers, results in a viable logistics measure for last-mile delivery. It is possible to reduce total transport costs by at least 5.4% and CO₂ emissions by at least 61.1%. Hence, CEP service providers should replace diesel trucks with a mix of sustainable logistics measures for last-mile delivery in small- and medium-sized cities to achieve better operational efficiency and lesser environmental impacts.

Intermodal freight transport (IFT) requires a large number of optimisation measures to ensure its attractiveness. This involves numerous control decisions on different time scales, making integrated optimisation with traditional methods almost unfeasible. Recently, a new trend in optimisation science has emerged: the application of Deep Learning (DL) to combinatorial problems. Neural combinatorial optimisation (NCO) enables real-time decision-making under uncertainties by considering rich context information—a crucial factor for seamless synchronisation, optimisation, and, consequently, for the competitiveness of IFT. The objective of this study is twofold. First, we systematically analyse and identify the key actors, operations, and optimisation problems in IFT and categorise them into six major classes. Second, we collect and structure the key methodological components of the NCO framework, including DL models, training algorithms, design strategies, and review the current State of the Art with a focus on NCO and hybrid DL models. Through this synthesis, we integrate the latest research efforts from three closely related fields: optimisation, transport planning, and NCO. Finally, we critically discuss and outline methodological design patterns and derive potential opportunities and obstacles for learning-based frameworks for integrated optimisation problems. Together, these efforts aim to enable a better integration of advanced DL techniques into transport logistics. We hope that this will help researchers and practitioners in related fields to expand their intuition and foster the development of intelligent decision-making systems and algorithms for tomorrow’s transport systems.

Online shopping has gained popularity in many countries over the past two decades. As a result, the question of whether it reduces the overall transportation volume from fewer personal shopping trips or increases it because of more freight transportation, especially in the courier, express, and parcel segment, is a hot topic in transportation research. Yet few studies have applied a holistic approach to distinguishing online-shopping behavior with regard to different product categories, the distances traveled to purchase different products on physical shopping trips, the means of transportation used on these trips, and the freight transportation caused by online shopping. In addition, the outbreak of the COVID-19 pandemic in spring 2020 led to a dramatic increase in online shopping in many countries, with people trying to avoid physical shopping trips for health reasons. Against this background, this article attempts to clarify the net effects of online shopping on transportation volume both before and during the pandemic. For this purpose, two online surveys were conducted to capture online-shopping behavior and its effects on transportation before (2018) and during the pandemic (2021). The investigation area was Germany. The analysis showed that the pandemic generated more online shopping and more personal shopping trips. However, the overall mileage resulting from personal shopping trips, freight transportation, and pick-up and drop-off trips caused by online shopping remained relatively constant, as people traveled shorter distances during the pandemic.