A Model for Predicting Capacity of Right-turn Traffic at Closely Spaced Unsignalised Junctions

Right-turn traffic at closely spaced un-signalised junctions is associated with delay and the resulting queuing since it cuts through main stream traffic. Through traffic volumes and spacing between junctions have significant bearing on the capacity and delay for right-turns. No relationship has been developed in the literature to predict capacity of right-turn traffic considering through traffic volume, right-turn traffic volumes and spacing between closely spaced junctions. In this study, model for predicting right-turning capacity of two successive unsignalised close junctions was developed. Traffic volume data from six paired junctions with intermediate spacing ranging between 30.0m and 185.0m on selected major roads in Kawempe Division of Kampala City were collected by video recording entered in Microsoft Excel and analysed using the RStudio modelling software. The critical right-turning traffic volume that yielded maximum queue length on the observed junctions was 230 vehicles per hour irrespective of the spacing between junctions. A model to predict right-turning capacity between two successive unsignalised junctions basing on spacing between them, traffic volume discharged and merging traffic was proposed. The study recommends the use of aerial data collection methods, extending observation time and inclusion of geometric characteristics in modelling

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-10
DOI
https://doi.org/10.5281/zenodo.24104316
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

A Model for Predicting Capacity of Right-turn Traffic at Closely Spaced Unsignalised Junctions

Mugumya Peter, Bagampadde Umaru
Zenodo (CERN European Organization for Nuclear Research)
Traffic control and management
article

A Model for Predicting Capacity of Right-turn Traffic at Closely Spaced Unsignalised Junctions

Mugumya Peter, Bagampadde Umaru
article en

Abstract

Right-turn traffic at closely spaced un-signalised junctions is associated with delay and the resulting queuing since it cuts through main stream traffic. Through traffic volumes and spacing between junctions have significant bearing on the capacity and delay for right-turns. No relationship has been developed in the literature to predict capacity of right-turn traffic considering through traffic volume, right-turn traffic volumes and spacing between closely spaced junctions. In this study, model for predicting right-turning capacity of two successive unsignalised close junctions was developed. Traffic volume data from six paired junctions with intermediate spacing ranging between 30.0m and 185.0m on selected major roads in Kawempe Division of Kampala City were collected by video recording entered in Microsoft Excel and analysed using the RStudio modelling software. The critical right-turning traffic volume that yielded maximum queue length on the observed junctions was 230 vehicles per hour irrespective of the spacing between junctions. A model to predict right-turning capacity between two successive unsignalised junctions basing on spacing between them, traffic volume discharged and merging traffic was proposed. The study recommends the use of aerial data collection methods, extending observation time and inclusion of geometric characteristics in modelling

Zenodo (CERN European Organization for Nuclear Research)
Makerere University (UG)
Traffic control and management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon