A COMPOSITIONAL APPROACH FOR TRAFFIC DISTRIBUTION EVALUATION OF TRIPLE LEFT-TURN LANES FROM AN INDIVIDUAL PERSPECTIVE

• Published in: Transport (Vilnius, Lithuania) Vol. 36; no. 2; pp. 110 - 122
• Main Authors: Li, Li, Zhang, Dong, Cheng, Xiao-Yun, Wang, Ping, Wang, Gui-Ping
• Format: Journal Article
• Language: English
• Published: Vilnius Vilnius Gediminas Technical University 04.05.2021
• Subjects: Dependent variables; Design factors; Engineering; Independent variables; Left-turns; Regression analysis; Statistical analysis; Traffic control; Traffic flow; Variance analysis
• ISSN: 1648-4142; 1648-3480
• DOI: 10.3846/transport.2021.14676

This study analysed unbalanced traffic distribution on Triple Left-Turn Lanes (TLTLs) at signalized intersections that is caused by left-turn drivers’ unequal lane preferences. To develop statistical bonding between the multilane traffic flow and individual lane choices, the lane volumes are formatted as compositional data to subject the sum-constant constraint. One-way and two-way Compositional ANalysis Of VAriance (CANOVA) models were formulated respectively to estimate the independent effect of one factor and its joint effects with other factors on the multilane traffic distribution. TLTL volume composition was the dependent variable of the models, while the factors of geometric design and traffic control that could affect left-turn drivers’ lane choice were the independent variables. Results indicate that variance of vehicle turning curve, length of the upstream segment, the location of triple left-turn sign, signal phase / cycle length, could affect the traffic distribution, and its balance could be achieved at specific levels of a factor. The joint effects of some factor couples could improve the unbalanced traffic distribution while others could not work.