We propose a novel approach for trip prediction by analyzing user's trip histories. We augment users' (self-) trip histories by adding 'similar' trips from other users, which could be informative and useful for predicting future trips for a given user. This also helps to cope with noisy or sparse trip histories, where the self-history by itself does not provide a reliable prediction of future trips. We show empirical evidence that by enriching the users' trip histories with additional trips, one can improve the prediction error by 15%-40%, evaluated on multiple subsets of the Nancy2012 dataset. This real-world dataset is collected from public transportation ticket validations in the city of Nancy, France. Our prediction tool is a central component of a trip simulator system designed to analyze the functionality of public transportation in the city of Nancy.

翻译：我们提出一种新的旅行预测方法,分析用户的旅行史。我们通过增加其他用户的“类似”旅行,增加用户(自我)旅行史,这可以提供信息,对预测特定用户未来旅行有用。这也有助于应对繁忙或稀少的旅行史,因为自传本身无法提供对未来旅行的可靠预测。我们展示了经验证据,证明通过增加旅行丰富用户的旅行史,可以将预测误差改善15%-40%,根据Nancy2012数据集的多个子集进行评估。这个真实世界数据集是从法国南希市公共交通机票验证中收集的。我们的预测工具是旨在分析南希市公共交通功能的行程模拟系统的核心组成部分。