Analysis of Young Drivers’ Confidence and Behaviour: Binary Logistic Regression Approach

Road crashes are a global concern, and they disproportionately affect younger drivers due to their inexperience and risk-taking behaviour. In Sri Lanka, the higher risk of traffic crashes among younger drivers underscores the need for researchers to study their driving confidence and behaviour. Such research could help mitigate the risks of future crashes. This research surveyed 400 young adults using a paper-based questionnaire to assess their confidence in various driving conditions: daytime, peak hours, nighttime, rainy weather, and hilly roads. It also examined risky behaviour such as smoking while driving, driving under the influence of alcohol, using a phone, and making illegal U-turns. The analysis employed Cronbach's Alpha reliability statistics, inter-item correlation, and Binary Logistic Regression (BLR) modelling. Participants' opinions on the Sri Lankan driving license process revealed that 16% rated it as good, 25% as average, and 59% as bad. Findings highlighted that increasing young drivers' confidence during nighttime and high-traffic congestion conditions reduces the likelihood of crashes. Confidence during nighttime and rainy conditions, along with perceptions of the licensing process, significantly influence crash involvement. Furthermore, risky manoeuvres, like overtaking in restricted areas, increase crash risk. The study highlights the importance of improving driver education and fostering safer behaviour to enhance road safety among young drivers in Sri Lanka.

Authors

Institutions

Publication Details

Journal
Journal of South Asian Logistics and Transport
Published
2025-03-28
DOI
https://doi.org/10.4038/jsalt.v5i1.99
Citations
1
Primary Topic
Forecasting Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Young Drivers’ Confidence and Behaviour: Binary Logistic Regression Approach

Niranga Amarasingha, Singithi Chandrasiri
1 citations
Journal of South Asian Logistics and Transport
Forecasting Techniques and Applications
2.11
article

Analysis of Young Drivers’ Confidence and Behaviour: Binary Logistic Regression Approach

Niranga Amarasingha, Singithi Chandrasiri
article en
1 citations

Abstract

Road crashes are a global concern, and they disproportionately affect younger drivers due to their inexperience and risk-taking behaviour. In Sri Lanka, the higher risk of traffic crashes among younger drivers underscores the need for researchers to study their driving confidence and behaviour. Such research could help mitigate the risks of future crashes. This research surveyed 400 young adults using a paper-based questionnaire to assess their confidence in various driving conditions: daytime, peak hours, nighttime, rainy weather, and hilly roads. It also examined risky behaviour such as smoking while driving, driving under the influence of alcohol, using a phone, and making illegal U-turns. The analysis employed Cronbach's Alpha reliability statistics, inter-item correlation, and Binary Logistic Regression (BLR) modelling. Participants' opinions on the Sri Lankan driving license process revealed that 16% rated it as good, 25% as average, and 59% as bad. Findings highlighted that increasing young drivers' confidence during nighttime and high-traffic congestion conditions reduces the likelihood of crashes. Confidence during nighttime and rainy conditions, along with perceptions of the licensing process, significantly influence crash involvement. Furthermore, risky manoeuvres, like overtaking in restricted areas, increase crash risk. The study highlights the importance of improving driver education and fostering safer behaviour to enhance road safety among young drivers in Sri Lanka.

Journal of South Asian Logistics and TransportVol. 5(1)
Sri Lanka Institute of Information Technology (LK)
Openalex Percentile: Top 15%
Forecasting Techniques and Applications
2.11
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.

Analysis of Young Drivers’ Confidence and Behaviour: Binary Logistic Regression Approach — Niranga Amarasingha, Singithi Chandrasiri · Journal of South Asian Logistics and Transport (2025) | TGRS Research Map | TGRS