Analysis Of Tunnel Traffic Accident Factors Based On Big Data

In recent years,China's road tunnels have become more and more repaired,but when a large number of tunnels are completed and the national economy is accelerated,the safety of tunnel traffic is becoming more and more obvious.Therefore,this paper provides a theoretical basis for how to effectively prevent tunnel traffic accidents and ensure traffic safety while controlling the speed limit and lighting in the tunnel.This paper proposes an improvement based on FP-Growth algorithm for the factors affecting tunnel traffic accidents,namely,the characteristics extracted in the analysis,including driving age,weather,different time periods,tunnel illumination brightness,tunnel road load,tunnel section and accident level.WTFP-Growth algorithm.First,we assign weights to transactions and items so that we can effectively mine hidden,more meaningful association rules.Secondly,using the two-dimensional table to save the key information in the transaction database,the method only needs to traverse the transaction database once,which improves the time efficiency of the algorithm.The improved WTFP-Growth algorithm is used to mine the association rules.By mining the collected data,the frequent factors combination leading to tunnel traffic accidents are found out,and the analysis results are used to find the decision rules.Continue to analyze these rules,can effectively prevent the occurrence of tunnel traffic accidents and make some decisions on the adjustment of the speed limit of the tunnel under the premise of ensuring safety,improve the maximum capacity of the tunnel,and also ensure the safety.Realizing the intelligent adjustment of the light in the tunnel proposes some decisions to achieve the goal of energy saving.