Reuben Tamakloe

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Korea Advanced Institute of Science and Technology

South Korea

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Reuben Tamakloe is a Research Professor at the Korea Advanced Institute of Science and Technology, South Korea. His research focuses on transportation safety, including factors affecting crash severity and analysis of individual heterogeneity in traffic incidents. Recent publications explore topics such as pedestrian and motorcycle crash dynamics, urban transit efficiency amidst the COVID-19 pandemic, and the adoption of drone delivery services in urban contexts.

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Exploring the overall efficiency improvement potential of including shared autonomous vehicles in public transit networks

Open Date: 2022-03-01

Close Date: 2025-02-01

Reuben Tamakloe

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About

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Articles (19)

Uncovering Individual Heterogeneity in Pedestrian Crash Severity with Mixed Logit Models: A Louisiana Case Study

Pattern recognition in crash clusters involving vehicles with advanced driving technologies

Decoding the patterns of critical factor associations driving electric vehicle recommendations

Examining urban delivery service user profiles and determinants of drone delivery adoption in Ghana considering usage before and after the COVID-19 pandemic

Efficiency variations of integrated urban transit stations before and during the COVID-19 pandemic: Insights from a Mega-City without pandemic lockdown measures

Temporal instability of the determinants of fatal/severe elderly pedestrian injury outcomes in intersections and non-intersections before, during, and after the COVID-19 pandemic

Assessing the factors associated with pedestrian injury severity in hit-and-run crashes in Ghana

Critical risk factors associated with fatal/severe crash outcomes in personal mobility device rider at-fault crashes: A two-step inter-cluster rule mining technique

Factors affecting fatal PTW at-fault crash outcome metrics at intersections and non-intersections in South Korea

Analyzing the time-varying patterns of contributing factors in work zone-related crashes

Key factors affecting motorcycle-barrier crash severity: an innovative cluster-regression technique

Pattern recognition from injury severity types of frontage roadway crashes

Factors influencing fatal vehicle-involved crash consequence metrics at spatio-temporal hotspots in South Korea: application of GIS and machine learning techniques

Discovering latent topics and trends in autonomous vehicle-related research: A structural topic modelling approach

Factors associated with driver injury severity of motor vehicle crashes on sealed and unsealed pavements: Random parameter model with heterogeneity in means and variances

Risk Factors Influencing Fatal Powered Two-Wheeler At-Fault and Not-at-Fault Crashes: An Application of Spatio-Temporal Hotspot and Association Rule Mining Techniques

Exploring senior motorcyclist injury severity crashes: Random parameter model with heterogeneity in mean and variance

Bicyclist injury severity classification using a random parameter logit model

Stability of factors influencing walking-along-the-road pedestrian injury severity outcomes under different lighting conditions: A random parameters logit approach with heterogeneity in means and out-of-sample predictions

Collaborators (6)

Ahmed Hossain

Jungyeol Hong

Md Nasim Khan

Livingstone Divine Caesar

Subasish Das

Dongjoo Park

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