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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.

Introduction: During the COVID-19 lockdown significant improvements in urban air quality were detected due to the absence of motorized vehicles. It is crucial to perpetuate such improvements to maintain and improve public health simultaneously. Therefore, this exploratory study approached bicycle infrastructure in the case of Munich (Germany) to find out which specific bicycle lanes meet the demands of its users, how such infrastructure looks like, and which characteristics are potentially important. Methods: To identify patterns of bicycle infrastructure in Munich exploratory data is collected over the timespan of three consecutive weeks in August by a bicycle rider at different times of the day. We measure position, time, velocity, pulse, level of sound, temperature and humidity. In the next step, we qualitatively identified different segments and applied a cluster analysis to quantitatively describe those segments regarding the measured factors. The data allows us to identify which bicycle lanes have a particular set of measurements, indicating a favorable construction for bike riders. Results: In the exploratory dataset, five relevant segment clusters are identified: viscous, slow, inconsistent, accelerating, and best-performance. The segments that are identified as best-performance enable bicycle riders to travel efficiently and safely at amenable distances in urban areas. They are characterized by their width, little to no interaction with motorized traffic as well as pedestrians, and effective traffic light control. Discussion: We propose two levels of discussion: (1) revolves around what kind of bicycles lanes from the case study can help to increase bicycle usage in urban areas, while simultaneously improving public health and mitigating climate change challenges and (2) discussing the possibilities, limitations and necessary improvements of this kind of exploratory methodology.

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.

Traffic contributes to a high amount of total greenhouse gas emission. This paper examines the issue whether road slope has a significant influence on carbon dioxide emission when driving a car. CO2 measurements are temporally and spatially restricted when using conservative methods, so this work makes use of extended floating car data (xFCD). An experiment is being set up for collecting data, which has been acquired for four months using an xFCD-equipped vehicle. In this time period, about 100 recordings have been acquired. Findings include a moderate positive correlation between road slope and CO2 pollution. This fact has been substantiated by a Pearson Correlation analysis. Consequently, an influence of slope on greenhouse gas emissions appears to be present for a certain local area., Adjunct Proceedings of the 14th International Conference on Location Based Services

Vehicle traffic in urban environments consists of a variation of traffic phenomena. Defining and measuring these traffic phenomena is challenging, since traffic sensors can still not observe the traffic situation of one city entirely over a period of time. One possibility to get general overviews is analyzing data coming from tracked vehicle movements. In the best cases, tracked vehicles are numerous and part of vehicle fleets that represent a big proportion of traffic participants in the investigation area. Traffic data in the form of movement trajectories is producible via the Floating Car Data (FCD) technology, which uses mobile devices that allow positioning and recording on-board information in every tracked vehicle. In case of operating taxis, these devices are part of already installed dispatcher systems and are able to produce Floating Taxi Data (FTD). One type of applications with FCD and FTD consists of inferring traffic situations with numerous different computational techniques. This thesis introduces a traffic pattern analysis framework for FCD with the emphasis on detecting specific vehicle traffic patterns. The extracted patterns should define urban traffic congestion as the detectable traffic phenomenon, which is the focus of this work. In general, tracking numerous moving entities participating in traffic is part of a large body of ongoing research. By reviewing traditional traffic data acquisition techniques from different domains, this work aims to provide a connection to various research disciplines connected with research on moving objects. Those fields are coming from physics, computer science, GIScience and geography to mention a few. In contrast to traffic phenomena on highways, which are well studied, this work focus on urban traffic in highly populated cities with dense transportation infrastructure. By selecting, modifying, and applying various methodological aspects, this work shows the establishment of a traffic pattern analysis framework that allows extracting typical periodical and unusual traffic patterns for each day of the week. Traffic congestion can be seen as a daily event, since it has starting and end points, that occurs on specific rush hours of the day, but as well as traffic anomalies that are caused by different events in the urban environment. The distinguishing between different types of traffic congestion events is challenging, especially when relying on classified movement patterns from FCD, which is only a fraction of all traffic participants. The first step is to clarify the various terminologies and to associate them with respective formalizations of each appearance, as the terms road capacity and traffic bottlenecks. Additionally, there are different aspects of traffic congestion detection, which includes reasoning on FCD representations, preprocessing and analytical possibilities. The last mentioned include map matching on road segments and density-based clustering of vehicle movement. Preceding steps of the framework consist of adjusted preprocessing of the data. The following six framework techniques aim to reveal specific traffic patterns from the preprocessed FCD by different forms of representing urban traffic congestion events. The underlying computational methods of the framework enable the possibility to apply various computations as a sequence that reveal an increasing number of details on urban traffic congestion events. The results of the framework computations include mainly three different products that are subsequently inferable: congestion polygons, congestion propagation polylines (CPP) and bundles of associated road segments. The affected road segments result from previous matching between road segments and congestion polygons, or congestion propagation polylines. The evaluation of the framework outcomes consists of visual analysis methods. A test FTD set from taxis in Shanghai from 2007 serves for the framework evaluation. The results show selected parts of the urban investigation area influenced by recurrent and non-recurrent traffic congestion, which conclude to expected travel time variations during rush hours. Afterwards, the test results serve for extensive discussions on the usefulness and reasonability of the framework methods. A concluding outlook outlines ideas on future work, which mainly consists of proposed methodical extensions and finding suitable applications for the traffic pattern analysis framework. Der Fahrzeugverkehr in städtischen Umgebungen besteht aus einer Variation von Verkehrsphänomenen. Die Definition und Messung dieser Verkehrsphänomene ist eine Herausforderung, denn Verkehrssensoren können die Verkehrssituation einer Stadt immer noch nicht gänzlich über einen längeren Zeitraum beobachten. Eine Möglichkeit, allgemeine Übersichten zu erhalten, besteht aus der Analyse von beobachteten Fahrzeugbewegungen. Im besten Falle sind die beobachteten Fahrzeuge zahlreich und Teil von Fahrzeugflotten, die einen großen Anteil der Verkehrsteilnehmer im Untersuchungsgebiet ausmachen. Verkehrsdaten in Form von Bewegungstrajektorien sind produzierbar über die Technologie Floating Car Data (FCD), die über mobile Geräte Positionierung und Aufzeichnung von fahrzeugeigenen Informationen in jedem beobachteten Fahrzeug ermöglichen. Im Falle von operativen Taxis sind diese Geräte Teil von bereits installierten Dispatchersystemen und können Floating Taxi Data (FTD) produzieren. Eine Art von Anwendung mit FCD und FTD besteht darin, Verkehrssituationen mit zahlreichen verschiedenen rechnerischen Methoden abzuleiten. Diese Arbeit stellt ein Verkehrsmusteranalyse-Framework für FCD vor mit dem Schwerpunkt auf der Erkennung spezifischer Fahrzeugverkehrsmuster. Die extrahierten Muster sollten städtische Verkehrsstaus als das nachweisbare Verkehrsphänomen definieren, das im Mittelpunkt dieser Arbeit steht. Im Allgemeinen ist die Beobachtung zahlreicher bewegter Objekte, die am Verkehr teilnehmen, Teil einer großen laufenden Forschungsaktivität. Durch die Bewertung traditioneller Verkehrsdatenerfassungstechniken aus verschiedenen Forschungsgebieten soll mit dieser Arbeit eine Verbindung zu verschiedenen Forschungsdisziplinen hergestellt werden, die sich mit der Forschung von sich bewegten Objekten beschäftigen. Diese Disziplinen kommen aus Physik, Informatik, GIScience und Geographie, um nur einige zu nennen. Im Gegensatz zu Verkehrsphänomenen auf Autobahnen, die schon gut erforscht sind, konzentriert sich diese Arbeit auf den städtischen Verkehr in hoch bevölkerten Städten mit dichter Verkehrsinfrastruktur. Durch die Auswahl, Adaption und Anwendung verschiedener methodischer Aspekte zeigt diese Arbeit die Etablierung eines Verkehrsmusteranalyse-Frameworks, das es ermöglicht, typische periodische und ungewöhnliche Verkehrsmuster für jeden Tag der Woche zu extrahieren. Verkehrsstau kann als tägliches Ereignis gesehen werden, da es Anfangs- und Endpunkte hat und zu bestimmten Stoßzeiten des Tages auftaucht. Verkehrsstaus können auch Verkehrsanomalien sein, die durch verschiedene Ereignisse in der städtischen Umgebung verursacht werden. Die Unterscheidung zwischen verschiedenen Arten von Verkehrsstauereignissen ist besonders dann schwierig, wenn man sich auf klassifizierte Bewegungsmuster von FCD stützt, die nur einen Bruchteil aller Verkehrsteilnehmer ausmachen. Der erste Schritt besteht darin, die verschiedenen Terminologien zu klären und sie mit den jeweiligen Formalisierungen jedes Erscheinungsbildes zu verknüpfen, wie beispielsweise die Begriffe Straßenkapazität und Verkehrsengpässe. Darüber hinaus gibt es verschiedene Aspekte der Verkehrsstauerkennung, die Darstellung, Vorverarbeitung und analytische Möglichkeiten von FCD beinhalten. Letztgenannte beziehen sich auch auf Map-Matching mit Straßensegmenten und das dichtebasierte Clustering von Fahrzeugbewegungen. Die vorangehenden Schritte des Frameworks bestehen aus einer angepassten Vorverarbeitung der Daten. Die folgenden sechs Methoden des Frameworks zielen darauf ab, spezifische Verkehrsmuster aus den vorverarbeiteten FCD aufzudecken durch unterschiedliche Darstellungsformen von städtischen Verkehrsstauereignissen. Die zugrunde liegenden Berechnungsmethoden des Frameworks ermöglichen es verschiedene Berechnungen als Sequenz anzuwenden, die eine ansteigende Anzahl von Details über städtische Verkehrsstauereignisse aufdecken kann. Die Ergebnisse der Framework-Berechnungen umfassen vor allem drei verschiedene Produkte, die nacheinander ableitbar sind: Staupolygone, Stauausbreitungspolylinien (CPP) und Bündel von zugehörigen Straßensegmenten. Die betroffenen Straßensegmente resultieren aus einem vorherigen Matching zwischen Straßensegmenten und Staupolygonen oder Stauausbreitungspolylinien. Die Evaluierung der Ergebnisse der Framework-Anwendung besteht aus visuellen Analysemethoden. Ein Test-FTD-Satz von Taxis in Shanghai aus dem Jahre 2007 dient für die Evaluierung des Frameworks. Die Ergebnisse zeigen ausgewählte Teile des städtischen Untersuchungsgebietes, die durch wiederkehrende und nicht wiederkehrende Verkehrsstaus beeinflusst werden, die auf die zu den erwarteten Fahrzeitschwankungen während der Stoßzeiten rückschließen. Danach dienen die Testergebnisse für umfangreiche Diskussionen über den Nutzen und die Bedeutung der Framework-Methoden. Ein abschließender Ausblick skizziert Ideen für künftige Arbeiten, die vor allem aus methodischen Erweiterungen und geeignete Anwendungen für das Verkehrsmusteranalyse-Framework bestehen.

Floating Car Data (FCD) of taxis provides useful traffic information. Its handling needs effective analysis steps, including Map Matching (MM) onto road segments. Another challenge is to compute realistic FCD-based travel times in complex urban road networks with different elevation levels. We propose a method for inferring travel time variations in intersections of these networks. Based on intersecting recorded taxi trajectories in Euclidean space, we assign average travel time differences. Additionally, the same technique allows distinguishing between elevated and non-elevated road intersections in complex urban environment. In the end we test and evaluate the method with taxi FCD from Shanghai.

Recent services for car navigation include selection s for computing the shortest, fastest or most economic route between origin and destination. Our idea is to compute the least complex route, which might be challenging, since there is no consistent definit ion of complexity. We focus on the complexity of ro ad intersections, which is experienced by vehicle drivers with the turning motion itself in selected crossroads. Therefore, we define weights for different turning possibilities. In a case study in Le Havre, we comput e the least complex path on the underly ing road network. First results show differing routes comparing to shortest path based on Dijkstra’s Algorithm.

People in growing urban areas are more and more influenced by emissions coming from numerous vehicles and factories. In this paper we inspect the concentration of particulate matter (PM2.5) visually over time. This information stems from a data set of air quality measurements from 36 static sensors in Beijing over one year (from 8.02.2013 till 8.02.2014). One possibility for creating an overview for 36 positions with varying PM2.5 measurements in time is the use of interpolation techniques. In our approach, we generate surfaces of PM2.5 concentration using inverse distance weighting (IDW). The resulting surfaces represent interpolated PM2.5 values, based on averaged PM2.5 information (e.g. average of one day). We create simple interactive visualizations using points as surface representations. Each surface point within the 3D visual analysis display exhibits its PM2.5 value by differing coloration and z-value (height component). The interactivity consists of using selection circles for stacked 3D displays of interpolated PM2.5 surfaces for different times (time series). The aim of this visual information analysis is the possible detection of periodical hotspots of high PM2.5 concentrations, which might be useful for people with respiratory diseases. For the detection of dynamic PM2.5 hotspot variations, we introduce thresholds for querying only the highest PM2.5 values of the surfaces. Afterwards, these points are aggregated into convex hulls (polygons), with the idea of comparing the size and shape of the PM2.5 hotspots in each created surface. The change of position and size of these polygons over time may be an indicator for air quality changes within an urban environment. Considering the above, this may be a starting point for the conception of a personalized routing solution for pedestrians or vehicle drivers with respiratory diseases, who want to avoid these hotspots of high PM2.5 concentrations.