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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Main Authors: Li Li, Qing-Chang Lu, Dong Zhang, Ping Wang, Gui-Ping Wang
Format: Article
Language:English
Published: Hindawi-Wiley 2019-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2019/5107327
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spelling 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 AT lili unbalancedmultipleleftturnlaneusagemodellingfromindividualchoicetoaggregatevolume
AT qingchanglu unbalancedmultipleleftturnlaneusagemodellingfromindividualchoicetoaggregatevolume
AT dongzhang unbalancedmultipleleftturnlaneusagemodellingfromindividualchoicetoaggregatevolume
AT pingwang unbalancedmultipleleftturnlaneusagemodellingfromindividualchoicetoaggregatevolume
AT guipingwang unbalancedmultipleleftturnlaneusagemodellingfromindividualchoicetoaggregatevolume
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