Abstract

The observation data from ground surface meteorological stations is an important basis on which climate change research is carried out, while the homogenization of the data is necessary for improving the quality and homogeneity of the time series. This paper reviews recent advances in the techniques of identifying and adjusting inhomogeneity in climate series. We briefly introduce the results of applying two commonly accepted and well-developed methods (RHtest and MASH) to surface climate observations such as temperature and wind speed in China. We then summarize current progress and problems in this field, and propose ideas for future studies in China. Along with collecting more detailed metadata, more research on homogenization technology should be done in the future. On the basis of comparing and evaluating advantages and disadvantages of different homogenization methods, the homogenized climate data series of the last hundred years should be rebuilt. Cao, L.-J., and Z.-W. Yan, 2012: Progress in research on homogenization of climate data. Adv. Clim. Change Res., 3 (2), doi: 10.3724/SP.J.1248.2012.00059.

Abstract

In a bid to cost-effectively tackle congestion, traffic management is often seen as a key option to utilise road capacity. Prior to the application of traffic management measures, a-priori analysis allows the effectiveness of measures to be judged and where necessary adapted. However, current approaches do this without considering the effects of stochastic uncertainty and fluctuations in traffic flow. These stochastic effects have been shown to substantially influence the evaluation of traffic management measures. In this contribution, a methodological framework is proposed and demonstrated in a multi-part case study, applying approaches that explicitly consider stochastic variations and applications for traffic management. The results of the case study demonstrate the effectiveness of the models and highlight the necessity to consider uncertainty and fluctuations when a-priori evaluating traffic management measures. © 2018 World Conference on Transport Research Society