Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3273
Title: Introduction of a Simple Estimation Method for Lane-Based Queue Lengths with Lane-changing Movements
Authors: Jayatilleke, S
Wickramasinghe, V
Amarasingha, N
Keywords: Mixed traffic
Queue prediction
Signalized intersections
Time series analysis
AR model
Issue Date: Mar-2023
Publisher: Springer
Citation: Jayatilleke, S., Wickramasinghe, V. & Amarasingha, N. Introduction of a Simple Estimation Method for Lane-Based Queue Lengths with Lane-changing Movements. J. Inst. Eng. India Ser. A 104, 143–153 (2023). https://doi.org/10.1007/s40030-022-00698-2
Series/Report no.: Journal of The Institution of Engineers (India): Series A;Volume 104, Issue 1, Pages 143 - 153
Abstract: Traffic congestions are increased globally due to rapid urbanization and expedited economic developments in many countries. Vehicle queue is a governing aspect of traffic congestion, studied over the past decades. Most of the existing queue estimation approaches are limited to homogeneous traffic conditions. However, the traffic conditions in many developing countries are heterogeneous and are heavily influenced by mixed vehicle composition, lane changing, and gap-filling behaviours. This study aims to estimate the queue length at signalized intersections having heterogeneous traffic conditions. The heterogeneity was assimilated with the consideration of Passenger Car Units (PCU) in the measurements of the traffic flow and the lane-changing movement within the considered road section. The influential factors of the queue length were contemplated with the arrival flow, discharge flow, outbound lane change, inbound lane change, and signal configuration. A Vector Auto Regression (VAR) model was developed to estimate queue length, with a lag time of 15 s for each variable. The results have indicated a higher accuracy in the queue estimation as well as the practical application for prediction, constituting the traffic characteristics of the formed vehicle queue. The R squared of the VAR model was 0.97, along with a Mean Absolute Percentage Error (MAPE) of 21.55%. The model estimation results of right turning lanes were well accurate with MAPE ranging from 15 to 17%, whilst for through movement lanes, accuracy was slightly low with MAPE in the range of 23–26%. The study manifests the functionality of the developed methodology for accurate queue estimations, asserting the practical applicability of VAR models in other locations constituting mixed traffic. © 2022, The Institution of Engineers (India).
URI: https://rda.sliit.lk/handle/123456789/3273
ISSN: 22502149
Appears in Collections:Department of Civil Engineering
Research Papers - Department of Civil Engineering
Research Papers - SLIIT Staff Publications

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