Traffic-Load Forecasting Using Weigh-in-Motion Data

Qu, T.; Lee, C. E.; Huang, L.; University of Texas at Austin. Center for Transportation Research; United States. Federal Highway Administration; Texas. Dept. of Transportation. Research and Technology Transfer Office

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ROSA P serves as an archival repository of USDOT-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by USDOT or funded partners. As a repository, ROSA P retains documents in their original published format to ensure public access to scientific information.

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Traffic-Load Forecasting Using Weigh-in-Motion Data

1997-03-01
By Qu, T. ; Lee, C. E. ; Huang, L.

[PDF-632.69 KB]

English

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Details:

Creators:

Qu, T. ; Lee, C. E. ; Huang, L.

Qu, T. ; Lee, C. E. ; Huang, L. Less -
Corporate Creators:

University of Texas at Austin. Center for Transportation Research ; United States. Federal Highway Administration ; Texas. Dept. of Transportation. Research and Technology Transfer Office

University of Texas at Austin. Center for Transportation Research ; United States. Federal Highway Administration ; Texas. Dept. of Transportation. Research and Technology Transfer Office Less -
Subject/TRT Terms:

[+]

Axle Loads Forecasting Highway Maintenance Pavement Design Pavement Loading Pavement Maintenance Pavement Performance Pavement Wear Traffic Counts Traffic Forecasting Traffic Volume Trends Vehicle Classification Weigh In Motion Weight-in-motion Systems Weight Distribution WIM Systems
Publication/ Report Number:

987-6 ; TX-99/987-6

987-6 ; TX-99/987-6 Less -
Resource Type:

Research Paper
Geographical Coverage:

Texas ; United States

Texas ; United States Less -
Edition:

Research report (9/95-8/96)
Corporate Publisher:

University of Texas at Austin. Center for Transportation Research
Abstract:

Vehicular traffic loading is a crucial consideration for the design and maintenance of pavements. With the help of weigh-in-motion (WIM) systems, the information about date, time, speed, lane of travel, lateral lane position, axle spacing, and wheel load for each vehicle passing a WIM site can be recorded continuously on-site and transferred to a remote computer. This study focused on using the data from two WIM systems installed to support research on pavement performance. Data analysis involved processing the data from the two WIM systems, summarizing the data into respective TxDOT vehicle classes, analyzing the error records in the WIM data, and exploring trends and patterns in the observed traffic counts. The researchers also analyzed the axle-load frequency distribution for different axle groups within all truck classes, explored the trend of axle-load distribution among years, and compared the difference in axle-load distribution for the same axle type at different locations in different vehicle classes. A time-series methods was used to forecast traffic counts for each vehicle class based on the trend in the pattern of observed traffic and a growth rate for each vehicle class. Finally, cumulative traffic loading was forecasted by applying the estimates of future traffic count to the retrospective axle-load frequency distribution. A C-language computer program that runs on PC-compatible machines was developed to facilitate data processing for traffic-load forecasting.
Format:

PDF
Funding:

7-987
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:5915cf07c43c9867d2081f4023c0f8863399b38a5c07954e76df6604b7d2aa16
Download URL:

https://rosap.ntl.bts.gov/view/dot/5033/dot_5033_DS1.pdf
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Traffic-Load Forecasting Using Weigh-in-Motion Data

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