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James Haworth

James is a PhD student working on the STANDARD project. His background is in geography and GIS, and his research focuses on space-time modelling and forecasting of spatial and network data. He has a keen interest in machine learning techniques, particularly kernel based methods, and has used them to forecast travel times on London’s road network. His recent work involves quantifying the space-time dependencies in standardized travel times in order to gain insights into the spread of congestion during non-recurrent events.

Publications

• Haworth, J., Cheng, T., Nonparametric regression for space-time forecasting under missing data. Computers, Environment and Urban Systems UK: [Accepted]
• Tsapakis, I., Schneider, W.H., Nichols, A.P., Haworth, J. Alternatives in assigning short-term counts to seasonal adjustment factor groupings. Journal of Advanced Transportation. DOI: 10.1002/atr.1186
• Bolbol, A., Cheng, T., Tsapakis, I., Haworth, J. Variable selection for travel mode inference from GPS data using a moving window SVM classification. Computers, Environment and Urban Systems UK: [Accepted]
• Cheng, T., Haworth, J., Wang, J. Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems. DOI: 10.1007/s10109-011-0149-5
• Haworth, J., Cheng, T., Shawe-Taylor, J. Kernel Regression for Traffic Prediction Under Missing Data, . Proceedings of the 11th International Conference on Geocomputation (2011), July 20-22, London. pp. 280-284.
• Cheng, T., Wang, J., Haworth, J., Heydecker, B., Chow, A. Modelling dynamic space-time autocorrelations of urban transport network. Proceedings of the 11th International Conference on Geocomputation (2011), July 20-22, London. pp. 215-220.
• Bolbol, A., Cheng, T., Haworth, J. Using a moving window SVM classification to infer travel mode from GPS data. Proceedings of the 11th International Conference on GeoComputation. pp.262-270.
• Wang, J. Q., Cheng, T., Haworth, J.Space-time kernels. in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, Part II. pp. 57-62