Search for dissertations about: "traffic prediction"
Showing result 1 - 5 of 125 swedish dissertations containing the words traffic prediction.
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1. Short-Term Traffic Prediction in Large-Scale Urban Networks
Abstract : City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. READ MORE
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2. Automated Traffic Time Series Prediction
Abstract : Intelligent transportation systems (ITS) are becoming more and more effective. Robust and accurate short-term traffic prediction plays a key role in modern ITS and demands continuous improvement. READ MORE
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3. The urban form and the sound environment - Tools and approaches
Abstract : Cities are always confronted with transition and adaptation. Awareness on urban environmental quality is leading the vision about the built environment’s resilience and sustainability, highlighting the importance of a multidisciplinary framework for urbanisation processes. READ MORE
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4. Development of traffic conflicts technique for different environments: A comparative study of pedestrian conflicts in Sweden and Jordan
Abstract : This study is aimed at improving the current Swedish Traffic conflicts Technique [TCT] in relation to vehicle-pedestrian conflicts. The present definition of conflict severity appears to produce less severe conflicts than they might be, particularly if the relevant road user (RRU) is the pedestrian. READ MORE
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5. Predictive models for accidents on urban links - A focus on vulnerable road users
Abstract : Much of earlier work on predictive models for accidents has been focused on rural traffic or urban intersections. This work has aimed at identifying and investigating possible improvements to predictive models for accidents on urban links. A special focus has been on the accidents of vulnerable road users. READ MORE