Chen Cai

Chen Cai

Sydney, New South Wales, Australia
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Experience

  • neucore.ai Graphic

    neucore.ai

    Sydney, New South Wales, Australia

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    Sydney, New South Wales, Australia

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      Sydney, Australia

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      Sydney, Australia

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      Sydney, Australia

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  • Company ghost image

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      Syndey

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      Sydney, Australia

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    Beijing

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Education

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    A thesis on the use of approximate dynamic programming, or reinforcement learning with artificial neural networks, to control the traffic signal groups for optimizing traffic flows in a road corridor.

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    Provided knowledge of the major issues affecting the global economy and financial markets.

    Essay: The prospect of east asian economic growth on total factor productivity;

    Course Grade: A

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    It was an intercollegiate course delivered by Imperial College London and University College London. It has core units in transport engineering, quantitative methods, transport economics, transport modelling, and transport policy. Chosen optional unit included advanced quantitative methods, traffic engineering, project management and freight transport.

    It was also a privilege to be lectured by an "all-star" team.

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    This degree provided knowledge in shipping management, port and terminal operation, multi-modal transport, transport economics, with solid development in mathematics, statistics and operations research.

Publications

  • Estimate Link Speed Distribution from Probe Vehicle Data

    Washington D.C., Trasport Research Board Annual Meeting

    Bayesian Network + Markov Chain Monte Carlo Simulation.

    Other authors

Honors & Awards

  • 2015 ITS Australia National Awards: Research Award

    ITS Australia

    This research provides a new approach to one of the central problems in urban transportation: reliable estimation of origin-destination traffic flows (that is, flows that specify where traffic comes from, and where traffic goes to). The research operates on two key insights. First, in most large-scale urban networks, most traffic is concentrated around a few dominant origins and destinations. This allows one to automatically select a parsimonious, interpretable set of origin-destination flows…

    This research provides a new approach to one of the central problems in urban transportation: reliable estimation of origin-destination traffic flows (that is, flows that specify where traffic comes from, and where traffic goes to). The research operates on two key insights. First, in most large-scale urban networks, most traffic is concentrated around a few dominant origins and destinations. This allows one to automatically select a parsimonious, interpretable set of origin-destination flows that explain the observed traffic on a network. Second, it is possible to automatically determine a finegrained zoning of the traffic network purely from observed traffic data. This allows one to zone the transport network in a way that facilitates tractable analyses, without undue sacrifice in accuracy.

  • 2014 ITS Australia National Awards: Research Award

    ITS Australia

    The main focus of the Advanced Data Analytics in Transportation (ADAIT) Project is to apply modern machine learning techniques to transport science, specifically transport modelling. By utilising everincreasing volume of transport data in an advanced computing framework it will be possible to produce transport models that are more accurate and accountable than ever before. ADAIT team members: Fang Chen, Chen Cai, Hoang Nguyen, Weihong Wang, Aditya Menon, Tao Wen (Student), Tuo Mao (student).

Languages

  • German

    Limited working proficiency

  • English

    Native or bilingual proficiency

  • Chinese

    Native or bilingual proficiency

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