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原文传递 Directional Distribution Factors for Bicycle Traffic: Development and Applications

题名：	Directional Distribution Factors for Bicycle Traffic: Development and Applications
正文语种：	英文
作者：	Ain Shams Univ
作者单位：	Mohamed El Esawey
关键词：	Bicycle volumes; Directional distribution; Peak hour volume; Annual average daily bicycle (AADB)
摘要：	The directional distribution, also known as the D factor, is an important traffic parameter that is frequently used for design and operational performance analysis. This research analyzes the variability of the D factor for bicycle traffic and identifies the factors that lead to such variability. The importance of the D factor stems from its potential application in estimating the daily volume of one travel direction at a particular count station using the daily count of the other direction. This case takes place when the sensor installed on one direction is down because of malfunction. This research explores the temporal and spatial transferability of the D factors of bicycle traffic. Different sets of bicycle D factors were developed according to different criteria, and were further used for estimation purposes. The study made use of daily bicycle volume data, which were collected at 10 bidirectional count stations in the City of Vancouver, Canada, between 2009 and 2011. The results showed that the spatial transferability of the D factors led to an average estimation error of the opposite-direction daily bicycle volume of 18.3-20.0% for various sets of the D factor; The use of the best set of D factors along with daily and monthly adjustment factors for the estimation of the annual average daily bicycle (AADB) volume led to an average estimation error of 27.5%. Moreover, the estimation of the peak hour bicycle volume (PHBV) of the missing count was explored by applying the D factor and the design hour factor (known as the K-factor) sequentially and the estimation error was found to be approximately 27.3%. In the temporal transferability analysis, the D factors were developed and applied using data of the same count locations but on other days. The average estimation errors dropped significantly to approximately 11 % for daily volume estimation, and 22% and 20.6% for the estimation of AADB and PHBV, respectively.
出版年：	2016
期刊名称：	Journal of Transportation Engineering
卷：	Vol.142
期：	No.10
页码：	04016046