计算机科学 ›› 2024, Vol. 51 ›› Issue (4): 165-173.doi: 10.11896/jsjkx.221200171
林滨伟1, 於志勇1,2, 黄昉菀1,2, 郭贤伟1,2
LIN Binwei1, YU Zhiyong1,2, HUANG Fangwan 1,2, GUO Xianwei1,2
摘要： 随着城市汽车数量的持续增长,街道停车难已经成为一个热点问题。解决街道停车问题的关键在于准确预测街道未来的停车位信息。移动群智感知方式(CrowdSensing)通过在车辆上安装声呐以感知路边的停车位情况,是一种低成本、高效益的感知停车位的方式,然而这种方式感知的停车位数据在时间上存在高稀疏性问题,传统模型无法直接用于预测。针对此问题,提出了一种基于Transformer的停车位序列补全和预测网络,此网络通过编码器生成缺失停车位序列的记忆,进而解码器以自回归的方式补全停车位序列中缺失的部分,同时预测出未来的停车位信息。实验结果表明,所提方法在两个高缺失的街道停车位数据集上的补全和预测效果都优于传统的机器学习和深度学习方法。
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