No description available.

Most of the existing bicycle flow traffic research is limited to characterizing the longitudinal motion of bicyclists based on the assumption that there is no significant differences between the dynamics of a single-file bicycle traffic and the longitudinal motion behavior of cars. This research reparametrizes an existing car-following model to describe bicycle-following and motion behavior. Furthermore, the lack of naturalistic data has limited the validation of this model. This research aims at developing a descriptive model that is capable of capturing the inherent non-lane-based traffic behavior characteristics of bicycle traffic and provides a methodology for extracting naturalistic cycling data from video feeds for use in safety and mobility applications. In this study, The Fadhloun-Rakha (FR) bicycle-following longitudinal motion model was extended through complementing it with a lateral motion strategy; thus allowing for overtaking maneuvers and lateral bicycle movements. For the most part, the following strategy of the FR model remains valid for modeling the longitudinal motion of bicycles except for the activation conditions of the collision avoidance strategy which are modified in order to allow for overtaking when possible. The proposed methodology is innovative in that it makes use of the intersection of certain pre-defined regions around the bicycles to decide on the feasibility of angular motion along with its direction and magnitude. The resulting model is the first point-mass dynamics-based model for the description of the longitudinal and lateral behavior of bicycles in both constrained and unconstrained conditions, and it is the only existing model that is sensitive to the bicyclist physical characteristics and the bicycle and roadway surface conditions given that the used longitudinal logic was previously validated against experimental cycling data. In relation to the development of the naturalistic cycling dataset, the used videos come from a dataset collected in a previous Virginia Tech Transportation Institute study in collaboration with SPIN in which continuous video data at a non-signalized intersection on the Virginia Tech campus was recorded. The research applied computer vision and machine learning techniques to develop a comprehensive framework for the extraction of naturalistic cycling trajectories. In total, this study resulted in the collection and classification of 619 bicycle trajectories based on their type of interactions with other road users. The results confirm the success of the proposed methodology in relation to extracting the locations, speeds, and accelerations of the bicycles with a high precision level. Furthermore, preliminary insights into the acceleration and speed behavior of bicyclists around motorists are determined. / Master of Science / The behavior of bicycle traffic differs from the that of cars. Bicycle traffic flow dynamics is unconstrained in lateral motion and overtaking when compared to car traffic flow. Based on this inherent behavior, existing car-following can only model the longitudinal motion of the bicycle flow traffic and it does not describe the non-lane base traffic that characterizes bicycle traffic dynamics. Furthermore, the existing experimental controlled dataset used for validating bicycle traffic flow models does not capture the naturalistic behavior of cyclists. Therefore, this research aims to develop a descriptive model that is capable of capturing the inherent non-lane-based traffic behavior characteristics of bicycle traffic and provides a methodology for extracting a naturalistic cycling data from a video dataset for use in safety and mobility applications. In this study, the Fadhloun-Rakha (FR) bicycle-following longitudinal motion model was extended through complementing it with a lateral motion strategy; thus allowing for overtaking maneuvers and lateral bicycle movements. For the most part, the following strategy of the FR model remains valid for modeling the longitudinal motion of bicycles except for the activation conditions of the collision avoidance strategy which are modified in order to allow for overtaking when possible. The proposed methodology is innovative in that it makes use of the intersection of certain pre-defined regions around the bicycles to decide on the feasibility of angular motion along with its direction and magnitude. The resulting model is the first point-mass dynamics-based model for the description of the longitudinal and lateral behavior of bicycles in both constrained and unconstrained conditions, and it is the only existing model that is sensitive to the bicyclist physical characteristics and the bicycle and roadway surface conditions given that the used longitudinal logic was previously validated against experimental cycling data. In relation to the development of the naturalistic cycling dataset, the used videos come from a dataset collected in a previous Virginia Tech Transportation Institute study in collaboration with SPIN in which continuous video data at a non-signalized intersection on the Virginia Tech campus was recorded. The research applied computer vision and machine learning techniques to develop a comprehensive framework for the extraction of naturalistic cycling trajectories. In total, this study resulted in the collection and classification of 619 bicycle trajectories based on their type of interactions with other road users. The results confirm the success of the proposed methodology in relation to extracting the locations, speeds, and accelerations of the bicycles with a high precision level. Furthermore, preliminary insights into the acceleration and speed behavior of bicyclists around motorists are determined.

Being widely adopted by the transportation and planning practitioners, the fundamental diagram (FD) is the primary tool used to relate the key macroscopic traffic variables of speed, flow, and density. We empirically analyze the relation between vehicular space-mean speeds and flows given different signal settings and postulate a parsimonious parametric function form of the traditional FD where its function parameters are explicitly modeled as a function of the signal plan factors. We validate the proposed formulation using data from signalized urban road segments in Salt Lake City, Utah, USA. The proposed formulation builds our understanding of how changes to signal settings impact the FDs, and more generally the congestion patterns, of signalized urban segments.

No description available.

The thesis is composed of two main parts. The first part deals with a microscopic traffic flow theory. Models describing the individual acceleration, deceleration and lane-changing behavior are formulated and the emerging collective traffic dynamics are investigated by means of numerical simulations. The models and simulation tools presented provide the methodical prerequisites for the second part of the thesis in which a novel concept of a traffic-adaptive control strategy for ACC systems is presented. The impact of such systems on the traffic dynamics can solely be investigated and assessed by traffic simulations. The focus is on future adaptive cruise control (ACC) systems and their potential applications in the context of vehicle-based intelligent transportation systems. In order to ensure that ACC systems are implemented in ways that improve rather than degrade traffic conditions, the thesis proposes an extension of ACC systems towards traffic-adaptive cruise control by means of implementing an actively jam-avoiding driving strategy. The newly developed traffic assistance system introduces a driving strategy layer which modifies the driver's individual settings of the ACC driving parameters depending on the local traffic situation. Whilst the conventional operational control layer of an ACC system calculates the response to the input sensor data in terms of accelerations and decelerations on a short time scale, the automated adaptation of the ACC driving parameters happens on a somewhat longer time scale of, typically, minutes. By changing only temporarily the comfortable parameter settings of the ACC system in specific traffic situations, the driving strategy is capable of improving the traffic flow efficiency whilst retaining the comfort for the driver. The traffic-adaptive modifications are specified relative to the driver settings in order to maintain the individual preferences. The proposed system requires an autonomous real-time detection of the five traffic states by each ACC-equipped vehicle. The formulated algorithm is based on the evaluation of the locally available data such as the vehicle's velocity time series and its geo-referenced position (GPS) in conjunction with a digital map. It is assumed that the digital map is complemented by information about stationary bottlenecks as most of the observed traffic flow breakdowns occur at these fixed locations. By means of a heuristic, the algorithm determines which of the five traffic states mentioned above applies best to the actual traffic situation. Optionally, inter-vehicle and infrastructure-to-car communication technologies can be used to further improve the accuracy of determining the respective traffic state by providing non-local information. By means of simulation, we found that the automatic traffic-adaptive driving strategy improves traffic stability and increases the effective road capacity. Depending on the fraction of ACC vehicles, the driving strategy "passing a bottleneck" effects a reduction of the bottleneck strength and therefore delays (or even prevents) the breakdown of traffic flow. Changing to the driving mode "leaving the traffic jam" increases the outflow from congestion resulting in reduced queue lengths in congested traffic and, consequently, a faster recovery to free flow conditions. The current travel time (as most important criterion for road users) and the cumulated travel time (as an indicator of the system performance) are used to evaluate the impact on the quality of service. While traffic congestion in the reference scenario was completely eliminated when simulating a proportion of 25% ACC vehicles, travel times were significantly reduced even with much lower penetration rates. Moreover, the cumulated travel times decreased consistently with the increase in the proportion of ACC vehicles. / In der Arbeit wird ein neues verkehrstelematisches Konzept für ein verkehrseffizientes Fahrverhalten entwickelt und als dezentrale Strategie zur Vermeidung und Auflösung von Verkehrsstaus auf Richtungsfahrbahnen vorgestellt. Die operative Umsetzung erfolgt durch ein ACC-System, das um eine, auf Informationen über die lokale Verkehrssituation basierende, automatisierte Fahrstrategie erweitert wird. Die Herausforderung bei einem Eingriff in das individuelle Fahrverhalten besteht - unter Berücksichtigung von Sicherheits-, Akzeptanz- und rechtlichen Aspekten - im Ausgleich der Gegensätze Fahrkomfort und Verkehrseffizienz. Während sich ein komfortables Fahren durch große Abstände bei geringen Fahrzeugbeschleunigungen auszeichnet, erfordert ein verkehrsoptimierendes Verhalten kleinere Abstände und eine schnellere Anpassung an Geschwindigkeitsänderungen der umgebenden Fahrzeuge. Als allgemeiner Lösungsansatz wird eine verkehrsadaptive Fahrstrategie vorgeschlagen, die ein ACC-System mittels Anpassung der das Fahrverhalten charakterisierenden Parameter umsetzt. Die Wahl der Parameter erfolgt in Abhängigkeit von der lokalen Verkehrssituation, die auf der Basis der im Fahrzeug zur Verfügung stehenden Informationen automatisch detektiert wird. Durch die Unterscheidung verschiedener Verkehrssituationen wird ein temporärer Wechsel in ein verkehrseffizientes Fahrregime (zum Beispiel beim Herausfahren aus einem Stau) ermöglicht. Machbarkeit und Wirkungspotenzial der verkehrsadaptiven Fahrstrategie werden im Rahmen eines mikroskopischen Modellierungsansatzes simuliert und hinsichtlich der kollektiven Verkehrsdynamik, insbesondere der Stauentstehung und Stauauflösung, auf mehrspurigen Richtungsfahrbahnen bewertet. Die durchgeführte Modellbildung, insbesondere die Formulierung eines komplexen Modells des menschlichen Fahrverhaltens, ermöglicht eine detaillierte Analyse der im Verkehr relevanten kollektiven Stabilität und einer von der Stabilität abhängigen stochastischen Streckenkapazität. Ein tieferes Verständnis der Stauentstehung und -ausbildung wird durch das allgemeine Konzept der Engstelle erreicht. Dieses findet auch bei der Entwicklung der Strategie für ein stauvermeidendes Fahrverhalten Anwendung. In der Arbeit wird die stauvermeidende und stauauflösende Wirkung eines individuellen, verkehrsadaptiven Fahrverhaltens bereits für geringe Ausstattungsgrade nachgewiesen. Vor dem Hintergrund einer zu erwartenden Verbreitung von ACC-Systemen ergibt sich damit eine vielversprechende Option für die Steigerung der Verkehrsleistung durch ein teilautomatisiertes Fahren. Der entwickelte Ansatz einer verkehrsadaptiven Fahrstrategie ist unabhängig vom ACC-System. Er erweitert dessen Funktionalität im Hinblick auf zukünftige, informationsbasierte Fahrerassistenzsysteme um eine neue fahrstrategische Dimension. Die lokale Interpretation der Verkehrssituation kann neben einer verkehrsadaptiven ACC-Regelung auch der Entwicklung zukünftiger Fahrerinformationssysteme dienen.

The thesis is composed of two main parts. The first part deals with a microscopic traffic flow theory. Models describing the individual acceleration, deceleration and lane-changing behavior are formulated and the emerging collective traffic dynamics are investigated by means of numerical simulations. The models and simulation tools presented provide the methodical prerequisites for the second part of the thesis in which a novel concept of a traffic-adaptive control strategy for ACC systems is presented. The impact of such systems on the traffic dynamics can solely be investigated and assessed by traffic simulations. The focus is on future adaptive cruise control (ACC) systems and their potential applications in the context of vehicle-based intelligent transportation systems. In order to ensure that ACC systems are implemented in ways that improve rather than degrade traffic conditions, the thesis proposes an extension of ACC systems towards traffic-adaptive cruise control by means of implementing an actively jam-avoiding driving strategy. The newly developed traffic assistance system introduces a driving strategy layer which modifies the driver's individual settings of the ACC driving parameters depending on the local traffic situation. Whilst the conventional operational control layer of an ACC system calculates the response to the input sensor data in terms of accelerations and decelerations on a short time scale, the automated adaptation of the ACC driving parameters happens on a somewhat longer time scale of, typically, minutes. By changing only temporarily the comfortable parameter settings of the ACC system in specific traffic situations, the driving strategy is capable of improving the traffic flow efficiency whilst retaining the comfort for the driver. The traffic-adaptive modifications are specified relative to the driver settings in order to maintain the individual preferences. The proposed system requires an autonomous real-time detection of the five traffic states by each ACC-equipped vehicle. The formulated algorithm is based on the evaluation of the locally available data such as the vehicle's velocity time series and its geo-referenced position (GPS) in conjunction with a digital map. It is assumed that the digital map is complemented by information about stationary bottlenecks as most of the observed traffic flow breakdowns occur at these fixed locations. By means of a heuristic, the algorithm determines which of the five traffic states mentioned above applies best to the actual traffic situation. Optionally, inter-vehicle and infrastructure-to-car communication technologies can be used to further improve the accuracy of determining the respective traffic state by providing non-local information. By means of simulation, we found that the automatic traffic-adaptive driving strategy improves traffic stability and increases the effective road capacity. Depending on the fraction of ACC vehicles, the driving strategy "passing a bottleneck" effects a reduction of the bottleneck strength and therefore delays (or even prevents) the breakdown of traffic flow. Changing to the driving mode "leaving the traffic jam" increases the outflow from congestion resulting in reduced queue lengths in congested traffic and, consequently, a faster recovery to free flow conditions. The current travel time (as most important criterion for road users) and the cumulated travel time (as an indicator of the system performance) are used to evaluate the impact on the quality of service. While traffic congestion in the reference scenario was completely eliminated when simulating a proportion of 25% ACC vehicles, travel times were significantly reduced even with much lower penetration rates. Moreover, the cumulated travel times decreased consistently with the increase in the proportion of ACC vehicles. / In der Arbeit wird ein neues verkehrstelematisches Konzept für ein verkehrseffizientes Fahrverhalten entwickelt und als dezentrale Strategie zur Vermeidung und Auflösung von Verkehrsstaus auf Richtungsfahrbahnen vorgestellt. Die operative Umsetzung erfolgt durch ein ACC-System, das um eine, auf Informationen über die lokale Verkehrssituation basierende, automatisierte Fahrstrategie erweitert wird. Die Herausforderung bei einem Eingriff in das individuelle Fahrverhalten besteht - unter Berücksichtigung von Sicherheits-, Akzeptanz- und rechtlichen Aspekten - im Ausgleich der Gegensätze Fahrkomfort und Verkehrseffizienz. Während sich ein komfortables Fahren durch große Abstände bei geringen Fahrzeugbeschleunigungen auszeichnet, erfordert ein verkehrsoptimierendes Verhalten kleinere Abstände und eine schnellere Anpassung an Geschwindigkeitsänderungen der umgebenden Fahrzeuge. Als allgemeiner Lösungsansatz wird eine verkehrsadaptive Fahrstrategie vorgeschlagen, die ein ACC-System mittels Anpassung der das Fahrverhalten charakterisierenden Parameter umsetzt. Die Wahl der Parameter erfolgt in Abhängigkeit von der lokalen Verkehrssituation, die auf der Basis der im Fahrzeug zur Verfügung stehenden Informationen automatisch detektiert wird. Durch die Unterscheidung verschiedener Verkehrssituationen wird ein temporärer Wechsel in ein verkehrseffizientes Fahrregime (zum Beispiel beim Herausfahren aus einem Stau) ermöglicht. Machbarkeit und Wirkungspotenzial der verkehrsadaptiven Fahrstrategie werden im Rahmen eines mikroskopischen Modellierungsansatzes simuliert und hinsichtlich der kollektiven Verkehrsdynamik, insbesondere der Stauentstehung und Stauauflösung, auf mehrspurigen Richtungsfahrbahnen bewertet. Die durchgeführte Modellbildung, insbesondere die Formulierung eines komplexen Modells des menschlichen Fahrverhaltens, ermöglicht eine detaillierte Analyse der im Verkehr relevanten kollektiven Stabilität und einer von der Stabilität abhängigen stochastischen Streckenkapazität. Ein tieferes Verständnis der Stauentstehung und -ausbildung wird durch das allgemeine Konzept der Engstelle erreicht. Dieses findet auch bei der Entwicklung der Strategie für ein stauvermeidendes Fahrverhalten Anwendung. In der Arbeit wird die stauvermeidende und stauauflösende Wirkung eines individuellen, verkehrsadaptiven Fahrverhaltens bereits für geringe Ausstattungsgrade nachgewiesen. Vor dem Hintergrund einer zu erwartenden Verbreitung von ACC-Systemen ergibt sich damit eine vielversprechende Option für die Steigerung der Verkehrsleistung durch ein teilautomatisiertes Fahren. Der entwickelte Ansatz einer verkehrsadaptiven Fahrstrategie ist unabhängig vom ACC-System. Er erweitert dessen Funktionalität im Hinblick auf zukünftige, informationsbasierte Fahrerassistenzsysteme um eine neue fahrstrategische Dimension. Die lokale Interpretation der Verkehrssituation kann neben einer verkehrsadaptiven ACC-Regelung auch der Entwicklung zukünftiger Fahrerinformationssysteme dienen.