VR, AR & gamification

Year of publication: 2022

Year of publication: 2023

In the recent years, with the growing number of vehicles on the roads in urban areas, particularly freight transport, the level of congestion and air pollution have reached a concerning level reducing the quality of life. Thus, municipalities are seeking effective solutions to address these concerns. In this regard, environmentally sensitive traffic management strategies have shown to be a promising solution to protect densely populated urban areas from high emission concentration. However, an effective strategy requires accurate estimation of spatiotemporal dispersion of emissions. This is typically achieved by employing a chain of tools contains a traffic simulator, emission model and a dispersion model. While the first two components have been frequently used to estimate traffic-related emissions, their results cannot be easily validated against empirical measurements without applying a suitable dispersion model. In this study, we propose an open-source framework for the complete toolchain to estimate emission levels in 3D urban environments taking into account terrain, buildings, and meteorological data. In order to make the framework transferable o any city around the world, we mainly rely on publicly available data and only open-source tools. More specifically, we use SUMO and its integrated emission models (HBEFA and PHEM-light) in combination with the open-source dispersion modeling tool GRAL (Graz Lagrangian Model) to achieve this goal. Subsequently, this modeling framework has been applied to a district within the city of Munich to showcase its practicality.

Year of publication: 2023

The Digital Twin Munich project (DZ-M) aims to depict complex urban environments through the use of static and dynamic components, and their semantic relationships. The project focuses on the development of a street network model and urban mobility simulation, utilizing the open source microscopic traffic flow simulation software SUMO. The transport demand is provided by the VISUM model of the city of Munich, and the data structure developed is compatible with standards such as OpenStreetMap, OpenDrive, CityGML, and GTFS. The project also includes the use of physical VRU simulators for data collection purposes, and the integration of these simulations into a 3D VR environment in Unity.

Year of publication: 2023

Digital twin is an emerging technology data from a variety of sensors to create a virtual replica of physical objects, processes, and systems. It is becoming increasingly popular as cities plan to become smarter and more efficient. Despite the growing amount of data, the main focus of urban digital twin applications has been dedicated to 3D building models that come often in the international OGC standard CityGML. For example, the German federal Agency for Cartography and Geodesy has published nation-wide 3D building models. While so far models of buildings have been a focus, 3D representations of roads according to CityGML are increasingly available due to emerging applications and data gathering methods. Thus, it is important to develop the required interfaces to make SUMO compatible with the rest of the components in an urban digital twin. In a recent research project, city of Munich conducted a data collection campaign where data from aerial images and multiple on-ground sensors were fused to extract geographical data including road geometries and lane markings. A convertor has been developed to extract the necessary attributes from the point cloud to generate SUMO-PlainXML files. Using the NETCONVERT, a SUMO network and its corresponding OpenDrive were generated, which was then used by another convertor to create a network based on the CityGML 3.0 standard. The later format allows for integrating the network objects as well as the results of the simulation of the simulation in a wider set of geographical applications such as Cesium for not only 3D visualisations, but also storing the data in 3DCityDatabase (3DCityDB) or even coupling the simulation with game engines. This is particularly important for certain applications such as modelling emission dispersion that requires buildings and vegetation or analysis of autonomous vehicles in real urban environment for an accurate modelling of multiple sensors. The entire process and the set of tools were applied on a small intersection in Munich.

Year of publication: 2023

Travel demand is an essential input for the creation of traffic models. However, estimating travel demand to accurately represent traffic behaviour usually requires the collection of extensive sets of data on traffic behaviour. Traffic counts are a comparably cost effective and reproducible source of information on travel demand. The utilisation of traffic counts to estimate demand is commonly found in the literature as the static and dynamic O-D estimation problem. A variety of approaches have been developed over recent decades to tackle this problem. Usually initial estimates of the O-D matrix are calibrated by utilising traffic counts and considering different assignment models. Other approaches for the estimation of travel demand solely based on traffic measurements can be found in the simulation software SUMO. The present work demonstrates the systematic development of a network model in SUMO in the inner city of Munich. In a sample network the estimation of travel demand through the tools flowrouter and routeSampler is tested by utilising flow measurements from induction loop detectors. The tests delivered unsatisfactory results, which is proven through observations of traffic flows in the resulting simulations as well as comparisons to historic traffic counts. The lack of sufficient detector data and the complexity of the sample network are discussed as the main reasons for the results. It is concluded that the applied tools should be tested in future studies with a more extensive dataset to perform a more comprehensive review of both tools. Therefore, we deliver specific requirements based on the network example of Munich.