一种车载端为主的城市路网当前与未来速度查询方法  

A Mobile-Side-Dominant Method for Querying Present and Future Velocity on Urban Roads

作　　者：韩京宇[1] 王彦之 陈进 晏鑫鑫 张怡婷[1] HAN Jingyu;WANG Yanzhi;CHEN Jin;YAN Xinxin;ZHANG Yiting(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]南京邮电大学计算机学院,南京210003

出　　处：《电子与信息学报》2024年第9期3722-3730,共9页Journal of Electronics & Information Technology

基　　金：江苏省重点研发计划(BE2022065-5)。

摘　　要：城市智能交通管理中经常查询路段的当前和未来交通速度,该文提出一种车载边缘为主(VED)的城市路段速度查询和预测方法:车载端在速度低于一定阈值时,与其它车载端交换收集到的数据,并在本地构建轻量级的当前和历史速度索引,以支持当前速度查询。为了用尽可能少的模型支持速度预测,提出根据路段拓扑同构将路网划分成若干路段等价类,根据周期性时窗和路段等价类将整个时空划分成若干模型等价类,同一个模型等价类的路段在给定时窗呈现相似的交通运行模式。针对每个模型等价类,车载端和数据中心配合进行联邦学习,训练长短期记忆模型(LSTMs)并存储在车载端,以响应车载端对附近未来交通状况的查询。每个车载端本地索引数据、本地响应查询,避免了查询响应延迟和通信拥塞;数据保存在车载端,而非集中存放,避免了安全攻击导致的隐私泄漏。Querying present and future traffic velocities of road segments is a routine task in urban intelligence transportation management,and a Vehicle-equipped-Edge Dominant(VED)method is proposed to answer the querying of present and future velocity of urban road segments.The collected data is exchanged with the other mobile sides by every vehicle-equipped mobile side when the mobile side's speed falls below a given threshold,and the light-weighted present and history velocity indexes are constructed locally to support the querying of present velocity.To train as few models as possible to predict future velocities,a road network is proposed to be partitioned into a set of road-segment clusters based on the segments'topological morphism and the spatiotemporal space is proposed to be partitioned into a set of model-equivalence classes according to the periodic time windows and road-segment clusters.The similar traffic patterns are exhibited by the road segments in the same model-equivalence class within the given time window.For every model-equivalence class,the federated learning is performed between the mobile sides and the data center to train the Long Short-Term Memories(LSTMs)which are stored at the mobile sides to answer the querying of future velocities of nearby areas.Data is indexed by every mobile side and queries are answered locally,thus the query response latency and possible communication congestion can be avoided.Further,data is stored at the mobile sides,rather than at one data center,so as to prevent the privacy leakage due to security attacks.

关 键 词：智能交通 查询 预测 索引 联邦学习 

分 类 号：TN929.53[电子电信—通信与信息系统] TP311.132[电子电信—信息与通信工程]