WisDOT Traffic Forecasting Methods and Best Practices Peer Exchange

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WisDOT Traffic Forecasting Methods and Best Practices Peer Exchange

2015-01-01
By Wisconsin. Department of Transportation ; Travel Model Improvement Program (U.S.) ; United States. Department of Transportation. Federal Highway Administration

File Language:

English

Details

Corporate Creators:

Wisconsin. Department of Transportation ; Travel Model Improvement Program (U.S.) ; United States. Department of Transportation. Federal Highway Administration
Corporate Contributors:

Wisconsin Department of Transportation. Research and Library Unit
Subject/TRT Terms:
Publication/ Report Number:

0092-14-19
Resource Type:

Tech Report
Geographical Coverage:

Wisconsin ; United States ; Florida ; Iowa ; Michigan ; Minnesota ; North Carolina ; Virginia
Corporate Publisher:

Wisconsin. Department of Transportation
Abstract:

The Wisconsin Department of Transportation (WisDOT) traffic forecasting section and the WisDOT Research Program funded, organizationally supported, and hosted a peer exchange May 20-22, 2014 in Madison, Wisconsin. The peer exchange participants represented the following Departments of Transportation: Florida, Iowa, Michigan, Minnesota, North Carolina, Virginia and Wisconsin. Representatives from the Federal Highway Administration Travel Model Improvement Program and Wisconsin Division Office also attended. Participants shared their experiences related to traffic forecasting methods. The event centered on facilitated discussion of the following areas: traffic forecasting reporting process, procedures, traffic forecasting statistical models/methods, travel demand model practices for forecast development, and specific traffic forecasting policy issues/questions. This report presents the key observations from the peer exchange discussions.
Format:

PDF
Funding:

WisDOT SPR# 0092-14-19
Download URL:

https://rosap.ntl.bts.gov/view/dot/67681/dot_67681_DS1.pdf
File Type:

[PDF - 1.45 MB]
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha256:de736344fe84ff963c44c5dfcfad4334f686a83122229b9a32633e12005999af

File Language:

English

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