Demand-Driven Optimization Of Transport Systems

人流和货流无处不在，正在经历着一场变革，将对社会、环境和经济产生巨大影响。需求通过采用新兴的运输服务来推动这一转变。这导致了综合运输系统(TS)的出现，在这种系统中，私人和公共运输方式以及人和货运之间的历史区别变得模糊。该研究计划的总体目标是开发方法来预测需求，并在考虑需求响应的情况下优化集成TS的供应。为了实现这一目标，我们开发了(I)统计学习算法(Ii)优化模型和求解方法。

The transportation of people and freight is ubiquitous and undergoes a transformation that will have tremendous impact on the society, environment and economy. Demand drives this transformation by adopting emerging transport services. This results in integrated transport systems (TS) where the historical separations between private and public transport modes as well between people and freight are blurred. The overall objective of this research program is to develop methodologies to predict demand and optimize supply of integrated TS taking the demand response into account. To achieve this objective we develop (i) statistical learning algorithms (ii) optimization models and solutions methods.