Congestion Prediction for Urban Areas by Spatiotemporal Data Mining
Traffic congestion is a fairly serious problem in many cities over the world.Thus,congestion prediction is a significant research problem for society.However,traffic congestion is affected by several factors and vary with spatial and temporal environments,which make it difficult to define and predict.This paper proposes a spatiotemporal traffic prediction mining model based on extending association rule mining algorithm.This model can predict the traffic congestion by extracting the frequent congestion areas and spatiotemporal congestion propagation rules.The feasibility and effectiveness of the proposed model have been evaluated through the analysis of congestion areas using the real floating car data in Beijing,China.
congestion prediction association rule mining frequent itemsets congestion propagation rule
LiHua Wang Zijun Zhou
BeiHang University Software Engineer laboratory Software College Beijing,China
国际会议
南京
英文
290-297
2017-10-12（万方平台首次上网日期，不代表论文的发表时间）