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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.

This work focuses on the visualisation of bicycle trajectories at an urban junction in Munich, Germany, using a Space-Time cube (STC) approach. The trajectories are obtained from a traffic observation using computer-vision-based approaches and pre-processed for analysis. A GUI implementation in MATLAB is introduced for evaluating the usefulness of the STC technique for transport planning and engineering purposes, with a focus on evaluating traffic safety. The visual patterns are evaluated by experts based on the quality of five interactive components of the implemented STC GUI., Zenodo, ISBN:https://zenodo.org/communities/gisruk2023

The Digital Twin Munich project (DZ-M) aims to depict complex urban environments through theuse of static and dynamic components, and their semantic relationships. The project focuses on thedevelopment of a street network model and urban mobility simulation, utilizing the open sourcemicroscopic traffic flow simulation software SUMO. The transport demand is provided by theVISUM model of the city of Munich, and the data structure developed is compatible with standardssuch as OpenStreetMap, OpenDrive, CityGML, and GTFS. The project also includes the use ofphysical VRU simulators for data collection purposes, and the integration of these simulations into a3D VR environment in Unity.

This paper deals with an approach to visualize origin destination data by counting the number of overlapping buffers created around the origin destination links. This procedure is applied to data from the MVG Rad bike sharing system in Munich, Germany. The results highlight the most strongly frequented bike sharing journeys, as well as the most attractive origins and destinations within the area of observation.

The transformation towards sustainable urban traffic strongly includes bicycle traffic. Besides conven- tional bicycles, (electric) cargo bicycles are discovered to have a great potential both for private and commercial us- age in transportation. Despite many similarities to conventional bicycles, cargo bicycles differ especially in weight, size, and load configuration. Driving simulators for many road user groups such as cars and even conventional bicycles are intensively applied in urban planning and transportation research. Cargo bicycle riders are often omit- ted as traffic participants in these considerations. In this paper, we describe the setup of an electric cargo bicycle simulator with all hard- and software components used. Additionally, we give insights into first results.

In order to facilitate safe interactions between automated vehicles (AVs) and vulnerable road users (VRUs) such as bicyclists, we present a communication application for mobile devices that allows an AV or its passenger and a bicyclist to interact in certain traffic scenarios. At the intersection, the AV or its passenger can change the existing right-of-way rules to prioritise the ego-vehicle or the bicyclist. In a coupled driving simulator in which these two road users can interact, 16 proofof- concept experiments are conducted. It is found that the perceived safety at conflict points can be increased through the use of the application. An investigation of the user data provides insights into the AV passengers’ decision types and duration in the scenarios studied. Moreover, the simulation results are used to revise and further develop the application concept.

Investigating traffic volume is essential in order to be able to determine the impact of current and future traffic on road networks. The present study examines the Annual Average Daily Traffic (AADT) and the Heavy Commercial Annual Average Daily Traffic (HCAADT) in the metropolitan area of Minneapolis-St. Paul in 1996 and 2016. In particular, the traffic volume data is visualized both two- and three-dimensionally using various visualisation techniques. Comparisons based on these 2D and 3D visualisations strongly indicate an overall increase in traffic volume from 1996 to 2016. Differences between the increase of AADT and HCAADT data as well as spatially differing patterns could also be identified. The paper documents initial findings on visualizing traffic volumes in a twoand three-dimensional way.

A traffic control strategy is developed based on stop-time delay of traffic usersto assign coordination between bicycles and motorvehicles by a fair distribution of stop-time delay in a complex urban area. To reach this goal, we implement a hierarchical coordination process for finding the optimal coordination interval in which the delay and number of stops of the whole network is minimized. Here, the coordination interval refers to the intervals in second requiredtoimplement the coordination and to evaluate the performance of the network. Therefore, different interval lengths are definedby integer multiples of cycle time and the coordination performance is evaluated for different traffic usersand the networkas well. SUMO, an open source microscopic traffic simulation software, is used for the simulation studies. The coordination performance is measured using the average delay and the number of stops. The investigation area is situated in the center of Munich and contains nine signalized intersections with separated bicycle lanes in the main direction. Traffic was added to the simulation network based on observed traffic volumes and turning ratios. The optimum coordination interval is achieved by minimizing the average delay and number of stops of the entire network while at the same time providing a balanced stop-time delay between different types of traffic users. The analysis of the results in the major direction indicates that by using the new algorithm in traffic control strategy, there would bea significant reduction of delay and number of stops for bicyclistswhile at the same time,the delay of the entire network is not higherthan the one of the current states.

Urban environments often involve the interaction of numerous vulnerable road users both with each other and with motorized road users. Many factors influence the ability of pedestrians and cyclists to efficiently move through such environments, and in order to study these, we use a bicycle simulator to conduct studies with test subjects, evaluating novel traffic control strategies on existing transport infrastructure depicted in Virtual Reality (VR) environments. Two studies are conducted and their quantitative (trajectories of the test subjects) and qualitative (questionnaire responses) results are analyzed. We discuss the first insights into and usefulness of conducting bicycle simulator studies, both with and without simulated road users, particularly as it relates to the approval and planning phases of infrastructure elements and traffic control strategies., {"references":["Abadi M. G., Hurwitz D. S., Sheth M., McCormack E. and Goodchild A. Factors impacting bicyclist lateral position and velocity in proximity to commercial vehicle loading zones: Application of a bicycling simulator Accident Analysis & Prevention, 2019, 125, pp. 29-39.","Kaths H., Keler A., Kaths J. and Busch F. Analyzing the behavior of bicyclists using a bicycle simulator with a coupled SUMO and DYNA4 simulated environment EPiC Series in Computing, 2019, 62, pp. 199-205.","Kwon, D.-S., Yang G.-H., Lee C.-W., Shin J.-C., Park Y., Jung B., Lee D. Y., Lee K., Han S.-H. and Yoo B.-H. KAIST interactive bicycle simulator Proceedings 2001 ICRA, IEEE International Conference on Robotics and Automation, 2001, South Korea, pp. 2313-2318.","Schramka, F., Arisona S., Joos M. and Erath A. Development of a Virtual Reality Cycling Simulator Journal of Computers, 2018, 13, pp. 603-616."]}

Cycling in urban areas has complex patterns. Depending on the qualitative conditions and the expansion of the respective bicycle infrastructure, bicycle traffic flows can develop differently. Additionally, mixed traffic participation, interaction frequencies with other road users and the spatial layout of parking facilities influence the properties of cycling trips. These factors have temporal and spatial variations and may emerge in certain periodicities. We investigate only one on these factors, namely permanent bicycle traffic count data at sparse locations and introduce an experimental methodology for delivering a first proof of concept of comparing bicycle flow in two different urban investigation areas, NYC and Munich. As the former is more complex and populated we introduce a preprocessing procedure (as an optional part of the methodology) for identifying hotspot of frequent bicycle usage via OD pairs from NYC CitiBike bike sharing service. Subsequently we extract OSM road segments for creating microscopic traffic flow simulation networks, which are then respectively calibrated based on the bicycle traffic count information at the permanent locations in both cities. First simulation results address our first research question: Is the state or quality of the present bicycle infrastructure inferable only from permanent bicycle traffic counts? The second research question addresses the possibility to infer event information, such as social or weather events from data of permanent bicycle traffic count locations. First results are discussed based on their usefulness for further studies. Bicycle traffic efficiency is compared on general level with first experiences with the two calibrated simulation networks and the detection of events with a comparison of both traffic count data sets and additional weather data.