This paper focuses on the migratable traffic risk estimation problem in intelligent transportation systems using the social sensing. The goal is to accurately estimate the traffic risk of a target area where the ground truth traffic accident reports are not available by leveraging an estimation model from a source area where such data is available. Two important challenges exist. The first challenge lies in the discrepancy between source and target areas and such discrepancy would prevent a direct application of a model from the source area to the target area. The second challenge lies in the difficulty of identifying all potential features in the migratable traffic risk estimation problem and decide the importance of identified features due to the lack of ground truth labels in the target area. To address these challenges, we develop DeepRisk, a social sensing based migratable traffic risk estimation scheme using deep transfer learning techniques. The evaluation results on a real world dataset in New York City show the DeepRisk significantly outperforms the state-of-the-art baselines in accurately estimating the traffic risk of locations in a city.

本文着重于 利用社会感知的智能交通系统中可迁移交通风险估计问题。目标是通过利用源区域的估算模型来准确估算没有地面真实交通事故报告的目标区域的交通风险有此类数据的地方。存在两个重要的挑战。第一个挑战在于源区域和目标区域之间的差异，这种差异将阻止模型从源区域直接应用于目标区域。第二个挑战在于难以识别可迁移交通风险估算问题中的所有潜在特征，并且由于目标区域中缺乏地面真相标签而难以确定已识别特征的重要性。为了应对这些挑战，我们开发了DeepRisk，这是一种使用深度迁移学习技术的基于社会感知的可迁移交通风险估计方案。在纽约市真实数据集上的评估结果表明，DeepRisk在准确估算城市位置交通风险方面明显优于最新基准。