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Diss. (sammanfattning) Linköping : Linköpings universitet, 2021

Traffic congestion is one of the most severe problems in modern urban areas. Besides the amplified travel times, traffic congestion intensifies the amount of emitted pollutants impacting human health and the environment. By making the appropriate interventions in traffic, transportation planners can mitigate congestion and enhance the performance of a traffic system. One crucial step in traffic planning and management is the estimation of the current or historical traffic state of a network. The estimation of the traffic state variables (traffic flow, density and speed) reveals the problematic parts of a network, namely, the parts associated with severe congestion and high emission rates. Traffic-related observations and traffic models constitute two core elements of a traffic state estimation approach. While the available observation data explicitly or implicitly provide partial information on the traffic state, traffic models define the traffic behaviour and contribute to estimating the variables when they are not directly observable. The estimated traffic state variables form the input to the so-called emission models, which estimate the mass of the emitted pollutants. The type and availability level of the observation data play a key role in traffic state and emission estimation. Traditionally, the primary source of traffic-related field data are stationary detectors (loop detectors, radar sensors or cameras). Today, following the late advances in communication systems, a vast amount of traffic-related data from mobile sources (GPS or cellular networks) is available. Such high data availability may give transportation planners new insights into understanding traffic behaviour. Appropriate exploitation of data coming from mobile sources can improve the existing approaches for estimating the traffic state and emissions. The broad aim of this thesis is to enhance the quality of traffic state and emission estimation. A special focus is given to the development of methods for exploiting the growing availability of traffic-related field data. By combining traffic data and models, the thesis proposes data-driven approaches for traffic state and emission estimation.