Demand-Driven Optimization Of Transport Systems

人员和货物的运输无处不在，并且正在经历一场变革，这将对社会，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种转变。这导致了综合交通系统（TS）的出现，在这种系统中，私人和公共交通模式之间以及人与货物之间的历史分离变得模糊。本研究计划的总体目标是开发方法，以预测需求和优化综合TS的供应考虑到需求响应。为了实现这一目标，我们开发了(i)统计学习算法（ii）优化模型和解决方法。

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.