Calibration of a microscopic traffic simulation in an urban scenario using loop detector data: a case study within the digital twin Munich

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

Using interactive space-time cube visualisation for pattern mining in bicycle trajectories and traffic-related parameters

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

Improving urban bicycle infrastructure - an exploratory study based on the effects from the COVID-19 Lockdown

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

Generating and calibrating a microscopic traffic flow simulation network of Kyoto: first insights from simulating private and public transport

Microscopic traffic flow simulations as tools for enabling detailed insights on traffic efficiency and safety gained numerous popularity among transportation researchers, planners and engineers in the first to decades of the 21st century. By implementing a test bed for simulation scenarios of complex urban transportation infrastructure it is possible to inspect specific effects of introducing small infrastructural changes related to the built environment and to the introduction of advanced traffic control strategies. The possibility of reproducing present problems or the transportation services, such as the ones of public bus services is a key motivation of this work. In this research, we reproduce the road network of the city of Kyoto for observing specific travel patterns of public buses such as the bus bunching phenomena. Therefore, a selection of currently available data sets is used for calibrating a cutout of the Kyoto road network of a relatively large extent. After introducing a method for geodata extraction and conversion, we approach the calibration by introducing virtual detectors representing present inductive loops and make use of historical traffic count records. Additionally, we introduce bus routes partially contributed by volunteer mappers (OSM project). First simulation outcomes show numerous familiar (local knowledge) flow patterns

Improving urban bicycle infrastructure-an exploratory study based on the effects from the COVID-19 Lockdown

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

Introducing data-format-dependent road network conversion techniques – lessons learned from the digital twin Munich

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

Çiler Belen's arbours

Taha Toros Arşivi, Dosya No: 85-B Harfi Muhteli

Traffic Pattern Analysis Framework with Emphasis on Floating Car Data (FCD)

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