• Energy Research

The energy consumption of passenger vehicles is affected by the physical properties of the environment. The ambient temperature in particular has a significant impact on the operating energy consumption. To quantify the impact of a changed climate on vehicles with different drivetrain systems, we set up a model that calculates the change in energy demand with respect to multiple global warming levels. In particular, the effect of rising temperatures on the energy consumption of battery electric vehicles and vehicles with internal combustion engines was investigated. Our results indicate that climate change will likely lead to a rise in energy consumption of vehicles with an internal combustion engine. This is mostly due to the increase in cabin climatization needs caused by the higher ambient temperatures. At a global warming level (GWL) of 4.0 °C, the calculated annual energy consumption on average is 2.1% higher than without taking the climate-change-related changes in temperature into account. Battery electric vehicles, on the other hand, are expected to have a lower overall energy consumption (up to −2.4% at 4 °C GWL) in cold and moderate climate zones. They benefit from the lower heating needs during winter caused by global warming.

This study uses accessibility measures to assess the quality of infrastructure supply (schools, parks, public transport stops in the regarded cases) and the performance of the transportation system in urban areas, based on objective, quantitative values. Within the scope of this work the methodology is applied to the city of Berlin and Mexico City as examples. Fine-grained data on the level of single households and the positions of single activity locations is used as well as a detailed road network representation and a description of public transport offers. The results show that accessibility measures are valuable input parameters for urban planning activities by showing under-supply of travel options and of important facilities on a highly disaggregated level. Furthermore, the outcomes demonstrate that the applied method is feasible to benchmark the accessibility of urban areas in regard to the Sustainable Development Goals (SDG) formulated by UNHABITAT.

About NaMIx ("Sustainable mobility index to assess the location-related mobility potential", in German: "Nachhaltige-Mobilität-Index zur Bewertung des standortbezogenen Mobilitätspotentials") is a project that aims to develop an index for sustainable mobility at locations and for neighborhoods that incorporates existing data and indices and maps different spatial levels. You can find out more about the project here: https://verkehrsforschung.dlr.de/de/projekte/namix. Most of the needed data can be retrieved from OpenStreetMap (OSM). Public transport stops can be obtained from OpenStreetMap (OSM) or General Transit Feed Specification (GTFS). Please note that due to restrictions regarding the distribution of some of the used datatypes, we had to limit the functionality in comparison to what has been shown during the final project presentation. Given the current version, the locations of buildings are retrieved from OSM instead of using the BKG address dataset. In addition, schools are retrieved from OSM as well and instead of using access by foot to elementary schools and access by bike to secondary schools NaMIx version 0.2.0 computes the access to all schools obtained from OSM by walk and by bike. Installation The current version is "NaMIx-0.2.0". It computes ten indicators almost (see above) as presented at the final project presentation. Please note that current NaMIx is realised as a Jupyter Notebook and that it is currently available in German only. To run the computation, please do the following Download a version of NaMIx : You may download a copy or fork the code at NaMIx' github page. : Besides, you may download the current release here: NaMIx-0.2.0.zip NaMIx-0.2.0.tar.gz Depack the obtained file into a folder of your choice (named from now on) Open the command line in this folder Optionally create a virtual python environment (recommended) run python -m venv venv_namix run venv_namix\Scripts\activate.bat Run pip install -r requirements.txt to install all necessary dependencies Download the 0.6.0 version of UrMoAC from UrMoAC-0.6.0.zip. Extract the contents into /demo/tools so that the jar file is located directly within this folder. Download GTFS data (e.g. from MVG). Extract the contents so that can be found in /demo/input/GTFS. Start the jupyter notebook You should have a command line open in Run python -m jupyter notebook open "namix_demo.ipynb" in the notebook The notebook processes the input data, computes the individual indicators and the joined NaMIx indicator, and stores the result in a geopackage file named "/demo/namix.gpkg". Authors NaMIx was developed and implemented by Serra Yosmaoglu, Benjamin Heldt, Daniel Krajzewicz, and Simon Nieland. ChangeLog NaMIx 0.2.0 (31.10.2023) initial version License NaMIx is licensed under the Eclipse Public License 2.0. When using it, please cite it via the DOI: https://doi.org/10.5281/zenodo.8328622 (v 0.2.0) Support If you have a usage question, please contact us via email (serra.yosmaoglu@dlr.de). Disclaimer We are not responsible for the contents of the pages we link to. The software is provided "AS IS". We cannot guarantee that the software works as you expect. References Boeing, G. (2017). OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks. Computers, Environment and Urban Systems 65, 126-139. doi:10.1016/j.compenvurbsys.2017.05.004 Krajzewicz, D., Heinrichs, D. & Cyganski, R. (2017). Intermodal Contour Accessibility Measures Computation Using the 'UrMo Accessibility Computer'. International Journal On Advances in Systems and Measurements, 10 (3&4), Seiten 111-123. IARIA. Heldt, B., Yosmaoglu, S. (2023). Neues Planungswerkzeug für Quartiere: Der Nachhaltige-Mobilität-Index. Emmett. https://emmett.io/article/der-nachhaltige-mobilitaet-index.

AbstractDigitalization coevolves with and fosters three revolutions in urban transport: sharing, electrification and automatization. This dynamic poses severe risks for social and environmental sustainability. Only strong public policies can steer digitalization towards fostering sustainability in urban transport.

The present study aims to deduce bikeability based on a collective understanding and provides a methodology to operationalize its calculation based on open data. The approach contains four steps building on each other and combines qualitative and quantitative methods. The first three steps include the definition and operationalization of the index. First, findings from the literature are condensed to determine relevant categories influencing bikeability. Second, an expert survey is conducted to estimate the importance of these categories to gain a common understanding of bikeability and merge the impacting factors. Third, the defined categories are calculated based on OpenStreetMap data and combined to a comprehensive spatial bikeability index in an automated workflow. The fourth step evaluates the proposed index using a multinomial logit mode choice model to derive the effects of bikeability on travel behavior. The expert process shows a stable interaction between the components defining bikeability, linking specific spatial characteristics of bikeability and associated components. Applied components are, in order of importance, biking facilities along main streets, street connectivity, the prevalence of neighborhood streets, green pathways and other cycle facilities, such as rental and repair facilities. The mode choice model shows a strong positive effect of a high bikeability along the route on choosing the bike as the preferred mode. This confirms that the bike friendliness on a route surrounding has a significant impact on the mode choice. Using universal open data and applying stable weighting in an automated workflow renders the approach of assessing urban bike-friendliness fully transferable and the results comparable. It, therefore, lays the foundation for various large-scale cross-sectional analyses.

Bicycle usage is significantly affected by weather conditions. Climate change is, therefore, expected to have an impact on the volume of bicycle traffic, which is an important factor in the planning and design of bicycle infrastructures. To predict bicycle traffic in a changed climate in the city of Berlin, this paper compares a traditional statistical approach to three machine learning models. For this purpose, a cross-validation procedure is developed that evaluates model performance on the basis of prediction accuracy. XGBoost showed the best performance and is used for the prediction of bicycle counts. Our results indicate that we can expect an overall annual increase in bicycle traffic of 1–4% in the city of Berlin due to the changes in local weather conditions caused by global climate change. The biggest changes are expected to occur in the winter season with increases of 11–14% due to rising temperatures and only slight increases in precipitation.