居民出行的智能化程度是衡量智慧城市发展水平的重要性能指标。智能手机等移动终端的普及使得乘客和车辆的实时位置、状态等信息容易获得，催生并使基于移动端的出行模式—移动出行成为新趋势。基于出租车移动出行实现了乘车需求与出租车载客资源的实时匹配，但仍无法满足大量乘车需求。基于专车移动出行因具备更加广泛的载客资源，为满足大量乘车需求提供了新途径，但新的动态价格特征也为智能化带来了新挑战。本项目聚焦于动态价格，基于用户与物理信息二元空间交互产生的大数据，以乘客为中心，为实现移动出行智能化研究三方面关键技术：1、乘客反应行为建模方法，以刻画乘客对动态价格的反应行为、动态价格、出行需求弹性之间的关系；2、动态价格预测方法，以高精度预测动态价格；3、个人出行意图实时推测及验证方法，以及时准确推测乘客乘车意图。项目预期形成乘客智能移动出行的系统理论和关键技术框架，也将为构建未来移动出行生态系统提供理论支撑。

The smartness of people transportation is a key indicator of the development of smart cities. The increasing popularity of smart phones and other mobile devices has made the real-time location of passengers and vehicles easily available. In this context, a new transportation mode relying on the mobile devices is gradually becoming dominant. We name the new mode as mobile transportation. Mobile transportation based on taxis is the most popular and effective in matching taxi demands and supplies in real time, however, it is still far from smart. To make the matter worse, it fails to meet the huge transportation demands due to the lack of taxi vehicular resource. Thanks to rich delivery resources resulted by the huge number and rapid growth of private cars, mobile transportation based on ridesourcing cars is thus recognized as a new solution, however, the newly introduced “dynamic price” also creates new research challenges. In this project, based on the big data collected when passengers interacting with physical and cyber spaces, we intend to build a smart passenger-centric mobile transportation system to address the new challenges. More specifically, focusing on the new feature of dynamic pricing, we intend to investigate the following three main research issues: (1) the modelling passengers’ reaction to dynamic prices, with the objective of building the mathematical relationship among the passengers’ reaction, dynamic prices and the demand elasticity, (2) the prediction of dynamic prices, with the objective of predicting dynamic prices for passengers accurately, (3) the real-time inference of trip purposes and its validation, with the objective of inferring the trip purposes in real time. This project aims to develop key techniques for mobile transportation systems based on ridesourcing cars for passengers, laying necessary theoretical foundation for the design and build of the future transportation ecosystem.