Driver fatigue in taxi, ride-hailing, and ridesharing services: a systematic review

Jaydarifard, Saeed; Behara, Krishna; Baker, Douglas; Paz, Alexander

doi:10.1080/01441647.2023.2278446

Cited by 3 publications

(2 citation statements)

References 59 publications

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“…Ride-hailing app companies have instituted competitive elements into their management systems to encourage driver participation and motivate them to meet certain targets. While this competitive approach is designed to incentivize drivers, it can also have the unintended effect of causing driver fatigue due to the pressures of continuous participation and target achievement (Jaydarifard et al, 2023). Thus, competition has an indirect impact on driver safety.…”

Section: Discussionmentioning

confidence: 99%

“…This persistent pressure not only contributes to driver fatigue but can also have implications for their safety on the job. It's crucial for drivers to manage their workloads to safeguard their well-being and ensure they can operate their vehicles safely (Jaydarifard et al, 2023). The intense competition within ride-hailing apps compels drivers to devise strategic approaches to maximize their earnings.…”

Section: Discussionmentioning

confidence: 99%

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Dillema of Multi-Platform Ride-Hailing Drivers in Yogyakarta: Between Economic Demands and Psychological Impact in Achieving Well-Being

Nurrahmad,

Rahmasari,

Pradana

et al. 2023

Preprint

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Online transportation services, initially presented as an innovative employment approach by categorizing workers as 'independent contractors', have come to be recognized for their exploitative nature. The notion of 'flexibility' suggests that workers have the autonomy to tailor their hours and location according to their income goals. However, this disguises the true situation for informal workers who lack rights and protections. This study delves into these issues from a psychological standpoint to uncover the mental and emotional interplay between the motivations and exploitation experienced by online drivers affiliated with multiple platforms. The study adopts a qualitative methodology with a phenomenological lens. Data was gathered through thorough interviews, observations, and document analysis, with Interpretative Phenomenological Analysis (IPA) employed as the data analysis technique. The study's participants were five male multi-platform online drivers from the Special Region of Yogyakarta, ranging in age from 18 to 64, and had been associated with at least two platforms for more than a year. The findings highlight two primary themes: firstly, the decision to join multiple platforms is chiefly driven by economic reasons, including a decline in orders, daily earnings, bonus incomes, and the conclusion of the initial profitable phase. Secondly, the exploitative conditions are linked to the platforms' algorithms, which exert control and influence the psychological state of the drivers. Nonetheless, the participants have developed effective strategies to manage fatigue, anxiety, and stress.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Dillema of Multi-Platform Ride-Hailing Drivers in Yogyakarta: Between Economic Demands and Psychological Impact in Achieving Well-Being

Nurrahmad,

Rahmasari,

Pradana

et al. 2023

Preprint

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Sleep mediates the relationship between precarious employment and mental health

Jaydarifard,

Smith,

Rossa

et al. 2023

Sleep Medicine: X

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The Application of the Piecewise Linear Method for Non-Linear Programming Problems in Ride-Hailing Assignment Based on Service Level, Driver Workload, and Fuel Consumption

Megantara,

Supian,

Chaerani

et al. 2024

Mathematics

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Ride-hailing services have grown rapidly, presenting challenges such as increased traffic congestion, inefficient driver workload distribution, and environmental concerns like higher fuel consumption and emissions. This study develops a non-linear ride-hailing assignment model addressing these issues by considering service level, driver workload, and fuel consumption. A piecewise linear method was employed to handle a non-linear programming model, and the method was modified to function autonomously without operator intervention. The model’s performance was evaluated using a publicly accessible dataset of taxi trips in Manhattan, focusing on indicators such as passenger waiting time, driver workload distribution, and fuel consumption. Numerical simulations demonstrated significant improvements: a 15% reduction in average passenger waiting time, a 20% improvement in balancing driver workloads, and a 10% decrease in overall fuel consumption, contributing to reduced emissions and environmental impact. The modified piecewise linear method proved effective in optimizing ride-hailing assignments, providing a more efficient and sustainable solution. The model also showed robustness in handling large datasets, ensuring scalability and applicability to various urban settings. These findings highlight the model’s potential to enhance operational efficiency and promote sustainability in ride-hailing services. By integrating considerations for service level, driver workload, and fuel consumption, the model offers a holistic approach to addressing the key challenges faced by the ride-hailing industry. This study provides valuable insights for future ride-hailing development and implementations of ride-hailing systems, promoting practices that are both efficient and environmentally friendly.

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Driver fatigue in taxi, ride-hailing, and ridesharing services: a systematic review

Cited by 3 publications

References 59 publications

Dillema of Multi-Platform Ride-Hailing Drivers in Yogyakarta: Between Economic Demands and Psychological Impact in Achieving Well-Being

Dillema of Multi-Platform Ride-Hailing Drivers in Yogyakarta: Between Economic Demands and Psychological Impact in Achieving Well-Being

Sleep mediates the relationship between precarious employment and mental health

The Application of the Piecewise Linear Method for Non-Linear Programming Problems in Ride-Hailing Assignment Based on Service Level, Driver Workload, and Fuel Consumption

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