Unbalanced Multiple Left Turn Lane Usage Modelling: From Individual Choice to Aggregate Volume
Diverse lane preferences of left-turn drivers lead to unbalanced traffic distribution on multiple left-turn lanes. The preferences can be measured in terms of lane usage at macroscopic level and individual lane choice at microscopic level. The data of lane volume and individual lane choices are coll...
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Series: | Journal of Advanced Transportation |
Online Access: | http://dx.doi.org/10.1155/2019/5107327 |
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doaj-5877a3653f2d480d8b7697deb7139ac22020-11-25T00:48:18ZengHindawi-WileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/51073275107327Unbalanced Multiple Left Turn Lane Usage Modelling: From Individual Choice to Aggregate VolumeLi Li0Qing-Chang Lu1Dong Zhang2Ping Wang3Gui-Ping Wang4School of Electronics & Control Engineering, Chang’an University, ChinaSchool of Electronics & Control Engineering, Chang’an University, ChinaSchool of Transportation & Logistics, Dalian University of Technology, ChinaSchool of Electronics & Control Engineering, Chang’an University, ChinaSchool of Electronics & Control Engineering, Chang’an University, ChinaDiverse lane preferences of left-turn drivers lead to unbalanced traffic distribution on multiple left-turn lanes. The preferences can be measured in terms of lane usage at macroscopic level and individual lane choice at microscopic level. The data of lane volume and individual lane choices are collected at eight dual or triple left-turn lanes equipped in signalized intersections in China. Linear regression model with dummy variables and discrete choice model were applied to analyse drivers’ lane choosing patterns at macroscopic and microscopic levels, respectively, and results of the two studies are mutually verified and complemented. The drivers’ lane preferences are found to vary with approach configurations, traffic control, and the number of lanes available. Static influential factors, such as turning radius inside the intersection, the design of shadowed lane, and intersection skewedness, as well as dynamic influential factors, including queue length, heavy vehicle in queue back and subject vehicle type, could affect the drivers’ lane preferences. The findings of this study have important implications for intersection design and traffic control in practice.http://dx.doi.org/10.1155/2019/5107327 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Li Li Qing-Chang Lu Dong Zhang Ping Wang Gui-Ping Wang |
spellingShingle |
Li Li Qing-Chang Lu Dong Zhang Ping Wang Gui-Ping Wang Unbalanced Multiple Left Turn Lane Usage Modelling: From Individual Choice to Aggregate Volume Journal of Advanced Transportation |
author_facet |
Li Li Qing-Chang Lu Dong Zhang Ping Wang Gui-Ping Wang |
author_sort |
Li Li |
title |
Unbalanced Multiple Left Turn Lane Usage Modelling: From Individual Choice to Aggregate Volume |
title_short |
Unbalanced Multiple Left Turn Lane Usage Modelling: From Individual Choice to Aggregate Volume |
title_full |
Unbalanced Multiple Left Turn Lane Usage Modelling: From Individual Choice to Aggregate Volume |
title_fullStr |
Unbalanced Multiple Left Turn Lane Usage Modelling: From Individual Choice to Aggregate Volume |
title_full_unstemmed |
Unbalanced Multiple Left Turn Lane Usage Modelling: From Individual Choice to Aggregate Volume |
title_sort |
unbalanced multiple left turn lane usage modelling: from individual choice to aggregate volume |
publisher |
Hindawi-Wiley |
series |
Journal of Advanced Transportation |
issn |
0197-6729 2042-3195 |
publishDate |
2019-01-01 |
description |
Diverse lane preferences of left-turn drivers lead to unbalanced traffic distribution on multiple left-turn lanes. The preferences can be measured in terms of lane usage at macroscopic level and individual lane choice at microscopic level. The data of lane volume and individual lane choices are collected at eight dual or triple left-turn lanes equipped in signalized intersections in China. Linear regression model with dummy variables and discrete choice model were applied to analyse drivers’ lane choosing patterns at macroscopic and microscopic levels, respectively, and results of the two studies are mutually verified and complemented. The drivers’ lane preferences are found to vary with approach configurations, traffic control, and the number of lanes available. Static influential factors, such as turning radius inside the intersection, the design of shadowed lane, and intersection skewedness, as well as dynamic influential factors, including queue length, heavy vehicle in queue back and subject vehicle type, could affect the drivers’ lane preferences. The findings of this study have important implications for intersection design and traffic control in practice. |
url |
http://dx.doi.org/10.1155/2019/5107327 |
work_keys_str_mv |
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