The advanced CAD model of a cargo bike

This article describe a way to create generative model at the example of cargo bike model, which is very simple object which can be used to present all important rules applied during crating generative models. Great attention was paid to the issue of model parametrization which is elementary thing in all modelling. Besides these aspects, it is also shown how to transform parametric model into generative model using programming languages. In the last part of article was included tests of correct working of model which focused also to the right position cyclist on the bike and shows how model of cargo bike could change its sizes thanks to correctly created generative model.

Movement Patterns of Electric Cargo Bike Commuters – First Insights from Field Experiments and Trajectory Analyses

In addition to and parallel to the SERVUS project, the requirements for a new, agile, urban cargo bicycle with electric drive are identified through a targeted, scientific application potential analysis in the project "E-Cargo Bike - Accompanying Research" described here. These findings are then implemented in the SERVUS project in an agile development and production process. Several prototypes are built in the process. The new e-cargo bicycles should be able to be used both privately and commercially. They will be presented to users for the first time at events (IAA 2021) and rated by them. In addition, the longer-term allocation of the bikes to selected users enables a cross-comparison with existing e-cargo bicycle models. Finally, the substitution potential of journeys in motorized individual transport is estimated and projected using the example of Munich. A key for gathering further information on the abilities of specific electric cargo bike types is tracking experiments with various sensor setups of which the first attempts are presented in this research. Additionally, and by enabling a certain spatial accuracy of gathered GNSS data, we are able to match selected maneuvers of these vehicle types to selected types of the Munich transportation infrastructure. Besides trajectory shapes of maneuvers, we are able to incorporate travel time and delay variations depending on investigation areas and times of the day at selected urban locations for every inspected maneuver (at for examples differing intersection geometries with and without traffic light signals). Another aspect is the presence of bicycle infrastructure and its relation to the option of using mixed traffic modes.

Towards Sustainable Liveable City: Management Operations of Shared Autonomous Cargo-Bike Fleets

Finding a sustainable mobility solution for the future is one of the most competitive challenges in the logistics and mobility sector at present. Policymakers, researchers, and companies are working intensively to provide novel options that are environmentally friendly and sustainable. While autonomous car-sharing services have been introduced as a very promising solution, an innovative alternative is arising: the use of self-driving bikes. Shared autonomous cargo-bike fleets are likely to increase the livability and sustainability of the city, as the use of cargo-bikes in an on-demand mobility service can replace the use of cars for short-distance trips and enhance connectivity to public transportation. However, more research is needed to develop this new concept. In this paper, we investigate different rebalancing strategies for an on-demand, shared-use, self-driving cargo-bikes service (OSABS). We simulate a case study of the system in the inner city of Magdeburg using AnyLogic. The simulation model allows us to evaluate the impact of rebalancing on service level, idle mileage, and energy consumption. We conclude that the best proactive rebalancing strategy for our case study is to relocate bikes only between neighboring regions. We also acknowledge the importance of bike relocation to improve service efficiency and reduce fleet size.

Localization of Relevant Urban Micro-Consolidation Centers for Last-Mile Cargo Bike Delivery Based on Real Demand Data and City Characteristics

The use of cargo cycles for last-mile parcel distribution requires urban micro-consolidation centers (UMC). We develop an approach to localize suitable locations for UMCs with the consideration of three criteria: demand, land use, and type of road. The analysis considers metric levels (demand), linguistic levels (land use), and cardinal levels (type of road). The land-use category is divided into commercial, residential, mixed commercial and residential, and others. The type of road category is divided into bicycle road, pedestrian zone, oneway road, and traffic-calmed road. The approach is a hybrid multi-criteria analysis combining an Analytical Hierarchical Process (AHP) and PROMETHEE methods. We apply the approach to the city center of Stuttgart in Germany, using real demand data provided by a large logistics service provider. We compared different scenarios weighting the criteria differently with DART software. The different weight allocation results in different numbers of required UMCs and slightly different locations. This research was able to develop, implement, and successfully apply the proposed approach. In subsequent steps, stakeholders such as logistics companies and cities should be involved at all levels of this approach to validate the selected criteria and depict the “weight” of each criterion.

Adaptive model predictive stabilization of an electric cargo bike using a cargo load moment of inertia estimator

This paper addresses the problem of stabilizing an electric cargo bike. For most control objectives, it suffices to consider a cargo bike as a two-wheeler. However, in addition to the challenges posed to the control of traditional two-wheelers, electric cargo bikes also have the issue of the cargo load, which can significantly influence the driving behaviour. Hence, detection and estimation of the mass, position and inertial properties of the cargo load become important. Here, a Kalman filter based algorithm which estimates these parameters online is presented. For the estimation, measurements of the force exerted by the load are recorded using force sensors installed under the load. Along with these, roll angle and roll acceleration are also measured. The estimated values are then used by an adaptive model predictive controller (MPC) to adjust the model-parameters and stabilize a cargo bike while following a set trajectory.

Cycle Logistics Projects in Europe: Intertwining Bike-Related Success Factors and Region-Specific Public Policies with Economic Results

The aim of this paper is to investigate whether and which specific, distinctive characteristics of European cycle logistics projects and the corresponding supporting policies have an impact on their economic performances in terms of profit and profitability. First, we identify project success factors by geographic area and project-specific characteristics; then, we statistically test possible dependence relationships with supporting policies and economic results. Finally, we provide a value-based identification of those characteristics and policies which more commonly lead to better economic results. This way, our work may serve as a basis for the prioritization and contextualization of those project functionalities and public policies to be implemented in a European context. We found that cycle logistics projects in Europe achieve high profit and profitability levels, and the current policies are generally working well and supporting them. We also found that profit and profitability vary across the bike model utilized: mixing cargo bikes and tricycles generates the highest profit and profitability, whilst a trailer–tricycle–cargo bike mix paves the way for high volumes and market shares.

Impact Assessment Model for the Implementation of Cargo Bike Transshipment Points in Urban Districts

Mitigating climate change and improving urban livability is prompting cities to improve sustainability of urban transportation and logistics. Cargo bikes, in combination with urban transshipment points, are gaining momentum as a green last mile alternative. Although a wide body of research proves their viability in dense urban areas, knowledge about planning urban transshipment points is very limited. This also entails the siting of such facilities and the assessment of effects on emissions. This study therefore presents a first quantitative scenario-based model that assesses the impacts on a district. It examines different strategies for siting urban transshipment points in a single district and its effect on traffic, the carbon footprint, and air quality to give strategic insights where to create candidate locations for such facilities. Our result contributes to knowledge of planning urban transshipment facilities and assessing the impact of different configurations. The findings demonstrated that the use of cargo bikes to make courier, express, and parcel (CEP) deliveries in urban districts could reduce greenhouse gas (GHG), particulate matter (PM10), and nitrogen oxides (NOx) emissions significantly. However, the choice of vehicles completing inbound and outbound processes and the strategies for siting urban transshipment points display widely differing and even conflicting potential to reduce emissions.