摘要
交通大数据的质量对于精准交通流量预测和智能交通系统的效能提升至关重要。以惠州市为例,利用Python语言对1个月的运营车辆数据进行处理,采用基于位置和速度阈值的方法过滤错误值,利用均值修复法修复异常值,基于时间间隔分割轨迹,并采用隐马尔可夫模型进行路网匹配,以获取用于预测的数据成果并进行可视化分析,选定特定路段和时间段进行测试。结果显示,交通大数据处理方法在实际应用中展现精准预测的潜力和较高的实用性。
The quality of traffic big data is crucial for accurate traffic flow prediction and the efficiency improvement of intelligent transportation systems.Taking Huizhou City as an example,the Python language was used to process the operating vehicle data for one month,the error values were filtered by the method based on location and speed thresholds,the outliers were repaired by the mean repair method,the trajectory was segmented based on time intervals,and the hidden Markov model was used to match the road network,so as to obtain the data results for prediction and visual analysis,and select specific road sections and time periods for testing.The results show that the traffic big data processing method shows the potential and high practicability of accurate prediction in practical applications.
作者
罗海正
郭亚东
LUO Hai-zheng;GUO Ya-dong
出处
《智能城市》
2024年第7期18-20,共3页
Intelligent City
基金
广东省教育科学规划项目(2023GXJK502)
粤港澳大湾区高校在线开放课程联盟教育教学研究和改革项目(WGKM2023141)
惠州学院校级大学生创新创业训练计划项目(2022315)。
关键词
惠州市
交通大数据
均值修复法
数据质量
精准预测
Huizhou city
traffic big data
mean repair method
data quality
precise prediction
