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MORO. --- Mobiliteit --- Wegvervoer.

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Transport. Traffic --- Academic collection --- Traffic flow --- Transportation --- Transportation and state --- Research --- Traffic engineering --- Belgium --- Traffic congestion

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Intelligent Transportation Systems are a promising set of technological advancements, whose main objective is that of enabling road users and policymakers alike to better exploit the available transportation infrastructure at its best possible level of service. The reduced costs of collecting, storing and processing information are enabling the development of richer mathematical models, which in turn can be exploited to better understand and anticipate the behaviour of transportation networks. From the perspective of Dynamic Traffic Management policymakers, this is highly desirable: being able to correctly anticipate the behaviour of users travelling in a network has been historically recognized as a strict prerequisite to achieve system optimal performances. However, a gap can be found in the state-of-the-art between what’s theoretically achievable and what’s practically feasible. Existing anticipatory traffic control policies are defined in a fully centralized framework, where a single, global entity is responsible for determining the network-wide optimal control law. This is undesirable from two perspectives: on the one hand, no centralization exists in the real world where multiple (hierarchical and/or equivalent) controllers interact; on the other hand, the centralized problem is computationally complex. In the “control track” of this work, the aim is that of determining under which conditions such a centralized problem can be decomposed into smaller, distinct sub-problems. To achieve this goal, the problem is analysed both analytically from a time static point of view and empirically from a time dynamic perspective. Once the nature of the controller-to-controller interactions is established, controller-wise decomposition policies are developed, together with the respective conditions for optimality. Decomposed anticipatory control techniques are therefore introduced in form of algorithms, tested and validated through in-silico experiments. The developed methodologies are proven to be highly competitive with respect to fully centralized anticipatory control solutions, as well as very beneficial compared to standard, non-anticipatory techniques. The accuracy of transportation models, upon which the aforementioned dynamic traffic management policies are based, depend directly on the amount and quality of information that is retrieved from the underlying network. When dealing with anticipatory traffic control policies, this becomes a very stringent assumption, as information on the whole transportation network is needed to achieve system optimal behaviour. Installing and maintaining a set of sensors on a network to achieve network-wide information is though very expensive, therefore the problem of determining the minimum amount of sensors to be located on a network so to obtain this information has been defined and studied in literature. Full observability solutions to the Network Sensor Location Problem have been defined, however, the amount of sensors needed to obtain full information is still beyond what is economically viable. In the “sensing track” of this work, the main focus is thus on the problem of characterizing partial observability solutions, that is, solutions in which only a subset of all sensors needed to retrieve full information from the underlying network is available. A partial observability metric capable of capturing the indirect and partially known relationships between the observed and unobserved variables characterizing the network is developed, and exploited in simple heuristic algorithms to obtain the set and locations of sensors that maximizes the amount of partially recovered information. The implications of locating sensors according to this metric are studied in Origin/Destination matrix estimation procedures. Furthermore, the relationship between the route set enumeration technique utilized to obtain full observability solutions and the resulting quality and amount of information embedded therein is assessed empirically. The overall results show that selecting sensor locations according to the developed metric is beneficial both from the theoretical aspects of observability problems and from the practical point of view of flow-estimation methodologies.

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In urban traffic networks, delays at signal-controlled interactions are a main component of the travel time experienced by road users. Effective optimization of traffic signal control offers high opportunities to reduce delays and overall to improve traffic operations. A strategic network traffic signal control policy, also called anticipatory network traffic control, determines signal timings to optimize network-wide objectives, e.g. minimize network travel times. While optimizing control variables, anticipatory traffic control explicitly takes into account road users’ route choice responses and the resulting network flow patterns. A traffic assignment model is usually used for approximating this route choice response. The basic problem in implementing anticipatory network control in a real-life system is that: due to the inherent model-reality mismatch of this approximated response model, the true resulting traffic pattern will in general not be optimal; signal control using incorrect anticipation might even trigger undesired traffic conditions, for instance unexpected delays, traffic congestion and spillback. Hence, since a model will unavoidably represent a coarse approximation of reality, there is a need for introducing corrective measures to deal with the model-reality mismatch and in turn achieve an effective control. The general aim of this PhD research is to develop control methods that elevate the traffic system to its best achievable performance, by explicitly addressing the model-reality mismatch. We take advantage of the fact that routine traffic patterns repeat from day to day, allowing one to learn from observing the true traffic patterns, resulting from a response to control, to compensate for errors in the response model. The solution method is initially inspired from industrial applications in robot manipulator, which have a similar objective, i.e improving performance of unknown / uncertain systems that operate repeatedly. A rule-based Iterative Learning Control (ILC) is applied to the traffic signal setting. The operational signal timings are iteratively corrected to achieve the predefined desired traffic state. The basic idea behind the iterative learning approach is to learn from the errors between observations and the desired reference. Following this basic idea of learning from observed errors, an optimization-based iterative learning approach is elaborated in the anticipatory control context. By making use of the traffic assignment model, the desired state is no longer predefined, but rather endogenously optimized. A method of Iterative Optimizing Control with Model Bias Correction (IOCMBC) is then proposed. IOCMBC generates a sequence of control settings based on a first-order model bias correction. Control performance is improved by learning from the model bias between real measurements and model predictions. It is proven that IOCMBC has two desirable properties: convergence in real traffic operations, and consistency between the convergent point and the real optimum. An integrated framework is then presented to jointly perform IOCMBC and model parameter estimation. The objective is to complete the iterative optimizing control scheme in which the underlying network flow modeling is imperfectly calibrated. Two objectives are fulfilled by this integrated framework, i.e. better control for the real system, and better model calibration. This integrated approach is advantageous to applications in which the underlying model is not only used for designing optimal control, but also for supporting other traffic management measures, for instance traffic information or route guidance. In order to link the methodology to general network applications, the key algorithmic implementation issue regarding the calculation of flow sensitivity is later addressed. We present a method to estimate the derivative of real flows with respect to signal control variables; this derivative information is particularly important in choosing directions to improve control optimization objectives. The derivative estimation method obviates the need to explicitly explore sensitivity by adding perturbations on each individual control variable, and hence allows for multiple control variables. By an evaluation on the derivative estimation, two types of estimation errors are revealed, i.e. truncation error and noise error. The presence of measurement noise is particularly detrimental to the estimation, which ultimately affects the quality of control solutions. A reliable dual method, i.e. dual IOCMBC is then proposed, improving both optimization objective and derivative estimation during the control process. Dual IOCMBC is able to generate a control sequence that converges to the real optimal point, and meanwhile forms a basis for estimation of the real flow derivatives. Numerical experiments on a general midsize network present an important intermediate step towards field implementations.

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This study aims to discover the effects of introducing a MaaS operator in a residential area on the sustainability of that area. In addition, it aims to reveal whether such a MaaS operator is able to be economically viable. The price setting of the MaaS operator will be the major parameter to influence sustainability and profitability. In contrast with most pilot projects where MaaS in introduced in an urban environment, this study performs a case study in a non-urbanised, residential area in Herent, Flanders, Belgium. The demand of the area is simulated based on statistical averages. Each inhabitant is attributed a package that may consist of MaaS or may consist of a personal vehicle. To attribute these packages, a utility-based model is used that calculates the expected utility of a package for an inhabitant's set of weekly trips. It will turn out that MaaS is able to reduce the total $CO_2$ emission in residential areas substantially. In addition, whether such a MaaS operator is able to make profit seems to depend on scale advantages, subsidies and deals with public transport providers.