3 research outputs found

    PENINGKATAN KINERJA DAN KESELAMATAN PERSIMPANGAN DI KAWASAN PUSAT KOTA MALANG

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    The needs of transportation is increasing following the urban area development. When the transport demands is higher than available transport infrastructure, the effect will be some problem such as traffic jam, accident, delay, noise and air pollution. The main purpose of the research is to improve the performance and the safety level of intersection in the city centre of Malang. The research used street performance analysis based on Indonesian Highway Capasity Manual with three steps: first, analyzing street intersection’s performance for the existing condition and the next five years. Second, compared street and intersection’s performance with the standard that has occured. Third, analyzed the performance of street and intersection after application of traffic management. The result showed that appropriate traffic management to be applied in Basuki Rahmat Street  is alternative 1 of the demand management, such as the application of one way system for the whole segments and it equipped by public transport lanes which can increase the level of service of the whole segments in Basuki Rahmat Street, as well as meet the level of service that has been ideal for the secondary arterial roads

    Design and validation of novel methods for long-term road traffic forecasting

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    132 p.Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowe

    Design and validation of novel methods for long-term road traffic forecasting

    Get PDF
    132 p.Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowe
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