2015•Unpublished venueRequires access

Annual average daily traffic estimation from short traffic counts

Zongfan Luo, Zhong Ming

Open publisher page 4 citations

Abstract

Highway agencies have gathered traffic data for a wide range of engineering and management purposes since the early days. Traffic summary statistics, such as annual average daily traffic (AADT) and design hourly volume (DHV), estimated from such data are the foundation of decision-makings related to the planning, design, operation and management of highway transportation system. Traditional factoring method is recommended by the Federal Highway Administration (FHWA) Traffic Monitoring Guide (TMG 2001) [4] for estimating AADTs from short-period or sample traffic counts. Several studies in the past have attempted to rationalize duration and schedules of sample traffic counts that are shorter than 24 hours in length. This study focuses on reliability of AADT estimates from 1-day, 2-day, 3-day and 5-day traffic counts. The results of this study reveal that shorter counts (1 or 2-day counts) conducted between Monday and Wednesday) may produce AADT estimates as good as the estimates from longer counts (5-day counts). The analysis and findings of this study on the adequacy of short period traffic counts from the perspective of AADT estimation will help highway agencies rationalize their traffic counting program, especially when they have a tight budget.

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What this paper is about

Highway agencies have gathered traffic data for a wide range of engineering and management purposes since the early days. Traffic summary statistics, such as annual average daily traffic (AADT) and design hourly volume (DHV), estimated from such data are the foundation of decision-makings related to the planning, design, operation and management of highway transportation system. Traditional factoring method is recommended by the Federal Highway Administration (FHWA) Traffic Monitoring Guide (TMG 2001) [4] for estimating AADTs from short-period or sample traffic counts. Several studies in the past have attempted to rationalize duration and schedules of sample traffic counts that are shorter than 24 hours in length. This study focuses on reliability of AADT estimates from 1-day, 2-day, 3-day and 5-day traffic counts. The results of this study reveal that shorter counts (1 or 2-day counts) conducted between Monday and Wednesday) may produce AADT estimates as good as the estimates from longer counts (5-day counts). The analysis and findings of this study on the adequacy of short period traffic counts from the perspective of AADT estimation will help highway agencies rationalize their traffic counting program, especially when they have a tight budget.

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Available abstract

Highway agencies have gathered traffic data for a wide range of engineering and management purposes since the early days. Traffic summary statistics, such as annual average daily traffic (AADT) and design hourly volume (DHV), estimated from such data are the foundation of decision-makings related to the planning, design, operation and management of highway transportation system. Traditional factoring method is recommended by the Federal Highway Administration (FHWA) Traffic Monitoring Guide (TMG 2001) [4] for estimating AADTs from short-period or sample traffic counts. Several studies in the past have attempted to rationalize duration and schedules of sample traffic counts that are shorter than 24 hours in length. This study focuses on reliability of AADT estimates from 1-day, 2-day, 3-day and 5-day traffic counts. The results of this study reveal that shorter counts (1 or 2-day counts) conducted between Monday and Wednesday) may produce AADT estimates as good as the estimates from longer counts (5-day counts). The analysis and findings of this study on the adequacy of short period traffic counts from the perspective of AADT estimation will help highway agencies rationalize their traffic counting program, especially when they have a tight budget.

Key concepts: Traffic count, Traffic volume, Transport engineering, Sample (material), Statistics, Reliability (semiconductor), Road traffic, Factoring

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