Mobility has a vital impact on the quality of life in a city. Yet, traditional modes of car-centric transportation models generate large externalities that must be tackled by cities - such as congestion, noise and air pollution. The Free and Hanseatic City of Hamburg in Germany is striving for a mobility transition, making mobility more sustainable and environmentally friendly. The city wants to change the mobility behaviour by strengthening the means of transport that are causing less impact on the environment and climate. By 2030, the goal is to increase the share of cycling, walking and public transport to a total of 80 per cent of all routes travelled. Cycling, which is especially cost- and space-efficient, plays a crucial role here. More specifically, the share of all joumeys made by bike should be increased by 25-30 per cent within this decade. Within the framework of Hamburg's strategy of lntelligent Transport Systems (ITS), the city fosters, develops and conducts ITS-projects that focus, amongst others, on cycling. In order to increase the proportion of cycling, it is essential to promote its attractiveness. A cycling infrastructure that ensures smooth and easy cycling within the city is vital for a competitive alternative to motorised private transport. Furthermore, people enjoy cycling when they feel comfortable and safe [e.g. 3]. The ITS-project PrioBilce-HH follows this approach and addresses both topics: cycling comfort and safety. This abstract focuses on the aspect of safety.

The traffic signal affects the life of virtually everyone every day. The effectiveness of signal systems can reduce the incidence of delays, stops, fuel consumption, emission of pollutants, and accidents. The problems related to rapid growth in traffic congestion call for more effective traffic signalization using robust feedback control methodology. Online traffic-responsive signalization is based on real-time traffic conditions and selects cycle, split, phase, and offset for the intersection according to detector data. A robust traffic feedback control begins with assembling traffic demands, traffic facility supply, and feedback control law for the existing traffic operating environment. This information serves the input to the traffic control process which in turn provides an output in terms of the desired performance under varying conditions. Traffic signalization belongs to a class of hybrid systems since the differential equations model the continuous behavior of the traffic flow dynamics and finite-state machines model the discrete state changes of the controller. A complicating aspect, due to the state-space constraint that queue lengths are necessarily nonnegative, is that the continuous-time system dynamics is actually the projection of a smooth system of ordinary differential equations. This also leads to discontinuities in the boundary dynamics of a sort common in queueing problems. The project is concerned with the design of a feedback controller to minimize accumulated queue lengths in the presence of unknown inflow disturbances at an isolated intersection and a traffic network with some signalized intersections. A dynamical system has finite L₂-gain if it is dissipative in some sense. Therefore, the H<SUB>infinity</SUB>-control problem turns to designing a controller such that the resulting closed loop system is dissipative, and correspondingly there exists a storage function. The major contributions of this thesis include 1) to propose state space models for both isolated multi-phase intersections and a class of queueing networks; 2) to formulate H<SUB>infinity</SUB> problems for the control systems with persistent disturbances; 3) to present the projection dynamics aspects of the problem to account for the constraints on the state variables; 4) formally to study this problem as a hybrid system; 5) to derive traffic-actuated feedback control laws for the multi-phase intersections. Though we have mathematically presented a robust feedback solution for the traffic signalization, there still remains some distance before the physical implementation. A robust adaptive control is an interesting research area for the future traffic signalization. / Ph. D.

Crashes categorized as running red light or left turning are most likely to occur at signalized intersections and resulted in substantial severe injuries and property damages. This dissertation mainly focused on these two types of vehicle crashes and the research methodology involved several perspectives. To examine the overall characteristics of red-light running and left-turning crashes, firstly, this study applied 1999-2001 Florida traffic crash data to investigate the accident propensity of three aspects of risk factors related to traffic environments, driver characteristics, and vehicle types. A quasi-induced exposure concept and statistical techniques including classification tree model and multiple logistic regression were used to perform this analysis. Secondly, the UCF driving simulator was applied to test the effect of a proposed new pavement marking countermeasure which purpose is to reduce the red-light running rate at signalized intersections. The simulation experiment results showed that the total red-light running rate with marking is significantly lower than that without marking. Moreover, deceleration rate of stopping drivers with marking for the higher speed limit are significantly less than those without marking. These findings are encouraging and suggesting that the pavement marking may result in safety enhancement as far as right-angle and rear-end traffic crashes at signalized intersections. Thirdly, geometric models to compute sight distances of unprotected left-turns were developed for different signalized intersection configurations including a straight approach leading to a straight one, a straight approach leading to a curved one, and a curved approach leading to a curved one. The models and related analyses can be used to layout intersection design or evaluate the sight distance problem of an existing intersection configuration to ensure safe left-turn maneuvers by drivers.

This thesis discusses the development and application of theoretical and computational methods to study three-body processes. The main focus is on the calculation of three-body resonances and bound states. This broadly includes the study of Efimov states and resonances, three-body shape resonances, three-body Feshbach resonances, three-body pre-dissociated states in systems with a conical intersection, and the calculation of three-body recombination rate coefficients. The method was applied to a number of systems. A chapter of the thesis is dedicated to the related study of deriving correlation diagrams for three-body states before and after a three-body collision. More specifically, the thesis discusses the calculation of the H+H+H three-body recombination rate coefficient using the developed method. Additionally, we discuss a conceptually simple and effective diabatization procedure for the calculation of pre-dissociated vibrational states for a system with a conical intersection. We apply the method to H_3, where the quantum molecular dynamics are notoriously difficult and where non-adiabatic couplings are important, and a correct description of the geometric phase associated with the diabatic representation is crucial for an accurate representation of these couplings. With our approach, we were also able to calculate Efimov-type resonances. The calculations of bound states and resonances were performed by formulating the problem in hyperspherical coordinates, and obtaining three-body eigenstates and eigen-energies by applying the hyperspherical adiabatic separation and the slow variable discretization. We employed the complex absorbing potential to calculate resonance energies and lifetimes, and introduce an uniquely defined diabatization procedure to treat X_3 molecules with a conical intersection. The proposed approach is general enough to be applied to problems in nuclear, atomic, molecular and astrophysics.

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Traffic State Estimation (TSE) is a vital component in traffic control which requires an accurate viewof the current traffic situation. Since there is no full sensor coverage and the collected measurementsare inflicted with random noise, statistical estimation techniques are necessary to accomplish this.Common methods, which have been used in highway applications for several decades, are state-spacemodels in the form of Kalman Filters and Particle Filters. These methods are forms of BayesianFilters, and rely on transition models to describe the system dynamics, and observation models torelate collected measurements to the current state. Reliable estimation of traffic in urban environmentshas been considered more difficult than in highways owing to the increased complexity.This MsC thesis build upon previous research studying the use of non-parametric Gaussian Processtransition and measurement models in an extended Kalman Filter to achieve short-term TSE. To dothis, models requiring different feature sets are developed and analysed, as well as a hybrid approchcombining non-parametric and parametric models through an analytical mean function based on vehicleconservation law. The data used to train and test the models was collected in a simulated signalizedintersection constructed in SUMO.The presented results show that the proposed method has potential to performing short-term TSE inthis context. A strength in the proposed framework comes from the probabilistic nature of the GaussianProcesses, as it removes the need to manually calibrate the filter parameters of the Kalman Filter. Themean absolute error (MAE) lies between one and five vehicles for estimation of a one hour long dataseries with varying traffic demand. More importantly, the method has desirable characteristics andcaptures short-term fluctuations as well as larger scale demand changes better than a previously proposedmodel using the same underlying framework. In the cases with poorer performance, the methodprovided estimates unrelated to the system dynamics as well as large error bounds. While the causefor this was not determined, several hypotheses are presented and analysed. These results are takento imply that the combination of BF and GP models has potential for short-term TSE in a signalizedintersection, but that more work is necessary to provide reliable algorithms with known bounds. In particular,the relative ease of augmenting an available analytical model, built on conventional knowledgein traffic modelling, with a non-parametric GP is highlighted.

In this dissertation, new algorithms that address three traffic problems of major importance are developed. First automatic identification and prediction algorithms are developed to identify and predict the occurrence of traffic congestion. The identification algorithms concoct a model to identify speed thresholds by exploiting historical spatiotemporal speed matrices. We employ the speed model to define a cutoff speed separating free-flow from congested traffic. We further enhance our algorithm by utilizing weather and visibility data. To our knowledge, we are the first to include weather and visibility variables in formulating an automatic congestion identification model. We also approach the congestion prediction problem by adopting an algorithm which employs Adaptive Boosting machine learning classifiers again something novel that has not been done previously. The algorithm is promising where it resulted in a true positive rate slightly higher than 0.99 and false positive rate less than 0.001. We next address the issue of travel time modeling. We propose algorithms to model travel time using various machine learning and statistical learning techniques. We obtain travel time models by employing the historical spatiotemporal speed matrices in conjunction with our algorithms. The algorithms yield pertinent information regarding travel time reliability and prediction of travel times. Our proposed algorithms give better predictions compared to the state of practice algorithms. Finally we consider driver safety at signalized intersections and uncontrolled intersections in a connected vehicles environment. For signalized intersections, we exploit datasets collected from four controlled experiments to model the stop-run behavior of the driver at the onset of the yellow indicator for various roadway surface conditions and multiple vehicle types. We further propose a new variable (predictor) related to driver aggressiveness which we estimate by monitoring how drivers respond to yellow indications. The performance of the stop-run models shows improvements after adding the new aggressiveness predictor. The proposed models are practical and easy to implement in advanced driver assistance systems. For uncontrolled intersections, we present a game theory based algorithm that models the intersection as a chicken game to solve the conflicts between vehicles crossing the intersection. The simulation results show a 49% saving in travel time on average relative to a stop control when the vehicles obey the Nash equilibrium of the game. / Ph. D.