Development of systematic procedure for the analysis of heavy truck traffic data
Quan-Yang Yao, W.G. Li, F T Najafi
Abstract
Quan-Yang Yao, W.G. Li, F T Najafi
Abstract
The 18 kip equivalent single axle loading (ESAL) process has been taken to develop the expected ESALs for the life of highway projects. The Florida Standard Urban Transportation Molding Structure (FSUTMS) does not forecast heavy truck traffic. The development of systematic truck forecasting model has not been received yet. In the absence of a departmental truck forecasting model, future truck traffic is based on the present day truck classification. The percentage of truck traffic is assumed to hold the same relationship to annual average daily traffic. For the purpose of pavement structural design, it is necessary to estimate the cumulative number of 18 kip ESALs for the design period. Since truck volume and damage factors are needed to calculate ESALs, estimating the frequency and predicting the trend of future heavy truck traffic is significant. The paper presents a systematic procedure for the classification and analysis of heavy truck traffic data and an attempt of developing a systematic truck traffic forecasting model.
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The 18 kip equivalent single axle loading (ESAL) process has been taken to develop the expected ESALs for the life of highway projects. The Florida Standard Urban Transportation Molding Structure (FSUTMS) does not forecast heavy truck traffic. The development of systematic truck forecasting model has not been received yet. In the absence of a departmental truck forecasting model, future truck traffic is based on the present day truck classification. The percentage of truck traffic is assumed to hold the same relationship to annual average daily traffic. For the purpose of pavement structural design, it is necessary to estimate the cumulative number of 18 kip ESALs for the design period. Since truck volume and damage factors are needed to calculate ESALs, estimating the frequency and predicting the trend of future heavy truck traffic is significant. The paper presents a systematic procedure for the classification and analysis of heavy truck traffic data and an attempt of developing a systematic truck traffic forecasting model.
Key concepts: Truck, Axle, Transport engineering, Traffic volume, Computer science, Heavy traffic, Engineering, Automotive engineering