Modeling speeding behaviour of light commercial vehicle drivers using binary logistic regression and machine learning techniques

No abstract is available for this paper.

• Publication year

  2026

• Venue

  Transportmetrica A: Transport Science

• Publication date

  2026-01-06

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1080/23249935.2025.2612593
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Effects of Behavior-Based Driver Feedback Systems on the Speeding Violations of Commercial Long-Haul Truck Drivers

  2024cited by this paper
• Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means.

  2024cited by this paper
• Factors affecting citizen safety of urban transportation service in Bangladesh: The case of Pabna municipality

  2024cited by this paper
• Assessment of the Impact of the Operation of Light Commercial Vehicle (LCV) on their Failure Rate and Transport Safety

  2024cited by this paper
• Investigating older driver crashes on high-speed roadway segments: a hybrid approach with extreme gradient boosting and random parameter model

  2024cited by this paper
• Modeling Driver Injury Severity using Machine Learning Algorithms

  2024cited by this paper
• Exploring speeding behavior using naturalistic car driving data from smartphones

  2023cited by this paper
• Modeling over speeding behavior of vehicles using a random parameter negative binomial approach: A case study of Mumbai, India

  2023cited by this paper
• Prediction and exploration of contributory factors related to fatigue driving among long-haul truck drivers traveling across India: A tree-based machine learning approach

  2023cited by this paper
• Driver Psychology Latent Classes as Predictors of Traffic Incident Occurrence in Naturalistic Driving Study Data

  2022cited by this paper
• Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their means

  2022cited by this paper
• Road traffic accident prediction for mixed traffic flow using artificial neural network

  2022cited by this paper
• Classification of Damaged Road Types Using Multiclass Support Vector Machine (SVM)

  2021cited by this paper
• Classifying travelers' driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning

  2021cited by this paper
• Dangerous driving behaviors among professional drivers of Kashan

  2020cited by this paper
• Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

  2020cited by this paper
• A critical overview of driver recording tools.

  2020cited by this paper
• Identifying key patterns in motorcycle crashes: findings from taxicab correspondence analysis

  2020cited by this paper
• Severity analysis of crashes on express lane facilities using support vector machine model trained by firefly algorithm

  2020cited by this paper
• Qualitative Investigation of the Influential Factors behind Unsafe Trucking Behaviors in India

  2020cited by this paper
• Are sociodemographic characteristics and attitude good predictors of speeding behavior among drivers on Malaysia federal roads?

  2019cited by this paper
• Effects of driver sleepiness and fatigue on violations among truck drivers in India

  2019cited by this paper
• Quantifying visual road environment to establish a speeding prediction model: An examination using naturalistic driving data.

  2019cited by this paper
• Analysis of factors associated with exceeding lawful speed traffic violations in Indian metropolitan city

  2019cited by this paper
• Speeding Violation Type Prediction Based on Decision Tree Method: A Case Study in Wujiang, China

  2019cited by this paper
• Improved Support Vector Machine Models for Work Zone Crash Injury Severity Prediction and Analysis

  2019cited by this paper
• Blind haste: As light decreases, speeding increases

  2018cited by this paper
• Taxi driver speeding: Who, when, where and how? A comparative study between Shanghai and New York City

  2018cited by this paper
• Factors associated with musculoskeletal pain and discomfort among Canadian truck drivers: A cross-sectional study of worker perspectives

  2018cited by this paper
• An insight of World Health Organization (WHO) accident database by cluster analysis with self-organizing map (SOM)

  2018cited by this paper
• Applied Logistic Regression, Second Edition

  1989cited by this paper

• No citing papers are available for this paper.