Car pooling is expected to significantly help in reducing traffic congestion and pollution in cities by enabling drivers to share their cars with travellers with similar itineraries and time schedules. A number of car pooling matching services have been designed in order to efficiently find successful ride matches in a given pool of drivers and potential passengers. However, it is now recognised that many non-monetary aspects and social considerations, besides simple mobility needs, may influence the individual willingness of sharing a ride, which are difficult to predict. To address this problem, in this study we propose GoTogether, a recommender system for car pooling services that leverages on learning-to-rank techniques to automatically derive the personalised ranking model of each user from the history of her choices (i.e., the type of accepted or rejected shared rides). Then, GoTogether builds the list of recommended rides in order to maximise the success rate of the offered matches. To test the performance of our scheme we use real data from Twitter and Foursquare sources in order to generate a dataset of plausible mobility patterns and ride requests in a metropolitan area. The results show that the proposed solution quickly obtain an accurate prediction of the personalised user's choice model both in static and dynamic conditions.

翻译：拼车服务预期能通过让司机与行程相似、时间安排相近的乘客共享车辆，显著减少城市交通拥堵和污染。目前已设计出多种拼车匹配服务，以便在给定的司机和潜在乘客池中高效地找到成功的乘车匹配。然而，人们认识到，除了基本的出行需求外，许多非货币因素和社会考量（这些因素难以预测）可能影响个人共享乘车的意愿。为解决这一问题，本研究提出GoTogether，一种针对拼车服务的推荐系统，它利用学习排序技术，从用户的历史选择（即接受或拒绝的共享乘车类型）中自动推导出每个用户的个性化排序模型。然后，GoTogether构建推荐乘车列表，以最大化所提供匹配的成功率。为了测试我们方案的性能，我们使用来自Twitter和Foursquare的真实数据，生成一个包含大都市区域合理出行模式和乘车请求的数据集。结果表明，所提出的方案在静态和动态条件下均能快速获得对个性化用户选择模型的准确预测。