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As food and parcel delivery has increased rapidly, two-wheeler riders now experience a lot of time pressure navigating crowded city roads. The study looks at the behavior of 300 delivery riders in Bhopal using a structured and data-driven process. The analysis confirmed that riders spent many hours behind the wheel, used their phones often and were always exposed to poorly constructed roads. Factor analysis uncovered four major risk categories: Risky Riding Behavior, Work-Related Stress, Environmental Factors and Accidents. I have applied Structural Equation Modeling (SEM) to explain how these factors are related to each other in terms of cause and effect. It was shown that risky actions were strongly linked to safety incidents, due to both stresses experienced at work and challenges from the environment. It helps divide riders among risk profiles to guide the development of customized safety strategies. With this information, policymakers, urban planners and service providers can make improvements in safety and gig employment conditions for urban India’s riders.

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Behavioral Analysis and Risk Segmentation of Two-Wheeler Delivery Riders: A Data-Driven Exploratory Study in Urban India

  • 会议论文

  • 发表日期：

    2026年

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  • Eslavath Vishnu Vardhan Naik,
  • Siddhartha Rokade,
  • Hitesh Malvi

摘要

As food and parcel delivery has increased rapidly, two-wheeler riders now experience a lot of time pressure navigating crowded city roads. The study looks at the behavior of 300 delivery riders in Bhopal using a structured and data-driven process. The analysis confirmed that riders spent many hours behind the wheel, used their phones often and were always exposed to poorly constructed roads. Factor analysis uncovered four major risk categories: Risky Riding Behavior, Work-Related Stress, Environmental Factors and Accidents. I have applied Structural Equation Modeling (SEM) to explain how these factors are related to each other in terms of cause and effect. It was shown that risky actions were strongly linked to safety incidents, due to both stresses experienced at work and challenges from the environment. It helps divide riders among risk profiles to guide the development of customized safety strategies. With this information, policymakers, urban planners and service providers can make improvements in safety and gig employment conditions for urban India’s riders.