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

ABSTRACT

Driver fatigue is a major cause of road crashes. However, there is limited knowledge regarding the potential consequences of driver fatigue in taxi (conventional and app-based), ride-hailing, and ridesharing services. Driver fatigue is likely to be significantly exacerbated in this population due to the multi-task characteristics of their jobs; thus, conducting a comprehensive study on driver fatigue in these transportation sectors is of utmost importance. This systematic review summarises the current state of knowledge about the causes and consequences of driver fatigue. We also suggested some potential control mechanisms for driver fatigue in taxi and ride-hailing services along a fatigue risk trajectory. We included studies published prior to September 2022 in three databases (Web of Science, Scopus, and PubMed) using a predefined search strategy. Eligible studies were critically appraised using the Joanna Briggs Institute (JBI) critical appraisal checklists. A total of 18 studies met our eligibility criteria as scoped from the 414 initially identified studies. Eight contributing factors to driver fatigue were revealed including long working hours, short rest breaks, limited driving experience, job demand, poor sleep, algorithmic management, traffic congestion, and additional workload. Furthermore, our review identified risk factors for driver fatigue in taxi and ride-hailing services, including road safety, work pressure and driver’s health, optimism bias, job precariousness, and lack of additional benefits. Findings to date suggest that driver fatigue in taxi and ride-hailing industries is as serious as, or more serious than, in other transportation sectors. Understanding the working conditions of these drivers is critical to establish effective policies and practices for reducing crash-related driver fatigue.

KEYWORDS:

• Driver fatigue
• safety
• ridesharing
• ride-hailing
• personalised transport
• taxi

1. Introduction

Road traffic crashes are among the most frequent causes of death every year (World Health Organization (WHO), 2020). A significant portion of these crashes was attributed to fatigue, accounting for 10%–30% of fatal crashes (Higgins et al., 2017; Hussain & Shi, 2022; Marcus & Rosekind, 2017). Fatigue was assessed as a significant factor for crashes on rural roads due to long and monotonous driving causing passive fatigue (Ahlström et al., 2018). However, many occupations require people to drive in urban areas as part of their work, and urban driver fatigue has received little attention. This is especially prevalent in taxi¹ (conventional and app-based), ride-hailing, and ridesharing (carpooling) services, which play an important role in the larger passenger transport system by providing door-to-door personalised services.

Understanding the definition of fatigue is crucial for examining the consequences and potential control mechanisms specific to driver fatigue in different contexts, including taxi and ride-hailing industries.² Phillips et al. (2017) defined fatigue as “the body–mind response to sleep loss or prolonged physical or mental exertion”. Accordingly, driver fatigue can be subcategorised into sleep-related and task-related fatigue, depending on the factors contributing to the fatigued state. Sleep related fatigue is influenced by factors such as circadian rhythm and sleep deprivation, which are determined by sleep duration and the amount of time awake since the last sleep episode (Williamson et al., 2011). However, task-related fatigue is caused by the driving task and driving environment and depends on time on task and the mental workload (see Figure 1 for a model of driver fatigue) (Hancock & Desmond, 2001). Task-related fatigue can be classified into active and passive fatigue (Desmond & Hancock, 2000). Active fatigue is associated with exertion resulting from overload in high-demand driving conditions, including factors such as high-density traffic in urban areas, time pressure, and secondary tasks beyond driving (e.g. searching for an address). Passive fatigue is induced by underload conditions, such as prolonged periods of driving in monotonous environments with little traffic while using cruise control (Tejero Gimeno et al., 2006). Taxi and ride-hail drivers are more susceptible to experiencing active than passive fatigue due to the multi-task characteristics of their job.

Figure 1. A model of task-related driver fatigue for this review. Source: (May & Baldwin, 2009).

Previous studies consistently documented the unfavourable aspects of ride-hailing jobs, including limited control over break times, sleep schedules, diet, and exercise, which can further increase the risk of fatigue-related issues (Bartel et al., 2019; Crain et al., 2020). While some conventional taxi drivers may work fixed shift hours as part of their schedule in certain countries, they may experience additional unfavourable working conditions compared to ride-hail drivers, including lower hourly earnings, increased effort to find passengers, and night shifts (Berger et al., 2018; Glöss et al., 2016). Indeed, conventional taxi drivers may work longer hours compared to ride-hail drivers as a means to compensate financially and earn more money (Glöss et al., 2016). For example, Fanxing Meng et al. (2015) found that 60% of airport taxi drivers reported fatigue to occur often or always due to long working hours. Driver fatigue is likely to be considerably exacerbated in this population due to the multi-task characteristics of their jobs (Poó et al., 2018). Therefore, it is important to develop effective measures to reduce or mitigate driver fatigue.

Effective strategies to reduce or mitigate driver fatigue in taxi and ride-hailing services should be applied along a whole chain of events or error trajectory that could lead to fatigue crashes. Dawson and McCulloch (2005) developed the fatigue risk trajectory model to describe how transport organisations effectively manage and mitigate risks. This model includes five levels of hierarchy from insufficient sleep opportunity afforded by work, insufficient sleep obtained by operators, fatigue-behavioural symptoms, fatigue-related error, and actual incident (Dawson & McCulloch, 2005). Phillips et al. (2017) altered the first two levels of this model to account for work-related fatigue and insufficient recovery. Transport organisations need to monitor and mitigate fatigue risks along the whole levels of this trajectory in order to effectively manage fatigue risk (Dawson & McCulloch, 2005; Phillips et al., 2017).

The knowledge surrounding driver fatigue in taxi (conventional and app-based), ride-hailing, and ridesharing services is currently limited. Therefore, this paper aimed to summarise the available evidence on the contributing factors of driver fatigue in taxi and ride-hailing industries. This paper also outlined risk factors of driver fatigue and proposed potential countermeasures along each level of the fatigue risk trajectory to reduce or mitigate driver fatigue in taxi and ride-hailing industries.

2. Methods

2.1. Search strategy

We conducted a comprehensive search of three databases including Web of Science, Scopus, and PubMed. The search was conducted in September 2022 and there was no limit to the starting date. The following search terms were used to identify relevant articles: (fatigue OR drows* OR sleep* OR crash OR “Driver vigilance” OR “Road accident” OR “Traffic accident” OR “Road safety”) AND (“cab driver*” OR “Taxi drivers” OR rideshare OR ridesharing OR “ride share” OR “ride-share” OR “ride sharing” OR “ride-sharing” OR carpool* OR ridehail OR “ride-hail” OR “ride hail” OR “ride hailing” OR “limousine drivers”).

2.2. Eligibility criteria

Studies were included with a quantitative, qualitative, mixed-method design, or review if they met the following criteria: (1) reported one or multiple contributing factors of driver fatigue/fatigue-related crashes in taxi, ride-hail, rideshare, or limousine drivers; and (2) were published in English and their full texts were available. Records were excluded if they were editorials, conference proceedings, theses, or commentaries.

2.3. Study screening

Search outputs were uploaded into the reference management software (EndNote). Upon removing duplicates in EndNote, records were transferred to Covidence software to remove the remaining duplicates and perform screening process. Two reviewers independently conducted the title and abstract screening in Covidence and then the full text versions of relevant studies to determine eligibility. Any disagreements were resolved by a third reviewer. The rationale for excluding potentially relevant studies was documented in Covidence.

2.4. Data extraction

Two reviewers extracted data independently into a customised excel spreadsheet, which includes the characteristics of publication, participant, study design, method, and contributing factors to driver fatigue. Any discrepancies were resolved via discussion with a third reviewer, and data were summarised into a table.

2.5. Quality assessment

The methodological quality of included studies was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools (Aromataris & Munn, 2020). The JBI critical appraisal tools are known for their comprehensiveness and suitability for a wide range of study designs, including observational studies (e.g. cross-sectional, cohort, and case reports), systematic reviews, qualitative studies, and other scientific studies (Aromataris & Munn, 2020). Checklist items could be answered as “yes”, “no”, “unclear” or “not applicable” (see Appendix for appraisal checklists). A quality assessment score for each study was assigned based on the calculated percentage of affirmative responses (Kent et al., 2023). Studies with a JBI score higher than 80% were considered to be at low risk of bias and classified as high-quality studies. Those with a score between 50% and 80% were considered to be at moderate risk of bias and classified as moderate-quality studies. Finally, studies that met less than 50% of the criteria were deemed to be at high risk of bias and classified as low-quality studies. Assessments were performed independently by two reviewers and any disagreements were resolved by a third reviewer.

3. Results and discussion

A total of 686 studies were identified through the search strategy, of which 414 were unique. After reading through the title and abstract, 360 articles were excluded, and 54 articles were left for full text assessment. After conducting a full-text screening, a total of 18 studies met our inclusion criteria for analysis in this review. A flowchart of the screening process is summarised in Figure 2.

Figure 2. Flow chart of study selection.

Table 1 summarises each study included in the review. Of the 18 included studies, only four studies focused on exploring the contributing factors of driver fatigue within the ride-hailing industry (Berneking et al., 2018; Crain et al., 2020; Dang et al., 2022; Mao et al., 2021). In total, 14 studies (77%) were conducted during the last five years. Of the studies reviewed, 11 studies were cross-sectional in design (Brown, 1994; Chu, 2020; Dang et al., 2022; Kang et al., 2022; Li et al., 2019; Lim & Chia, 2015; Mao et al., 2021; Menéndez et al., 2019; Meng et al., 2015; Peng et al., 2020; Peng et al., 2022; Wang et al., 2019), while three studies used cohort design (Dalziel & Job, 1997; Husain et al., 2019; Meng et al., 2019), one experimental (Chen et al., 2022), one qualitative (Facey, 2003), one theoretical (Crain et al., 2020), and one position statement (Berneking et al., 2018). No study used objective measurements to evaluate driver fatigue or its likely contributing factors. All cross-sectional and cohort studies employed surveys and questionnaires to assess driver fatigue and related outcomes. One study used a semi-structured interview to estimate likely contributing factors of driver fatigue (Facey, 2003). Furthermore, one study used driving simulator to assess driver distraction by ride-hailing systems (Chen et al., 2022).

Table 1. Characteristics of included studies.

Study	Study location	Study design	Population	Method	Outcome measure	Contributing factors	Quality
Dalziel and Job (1997)	Australia	Cohort study	42 taxi drivers from a single regional taxi network	Questionnaire	Accident records & self-reported fatigue and crash involvement	Rest break	Medium
Facey (2003)	Canada	Qualitative	A convenience sample of taxi drivers: not reported	Interview	Self-reported fatigue	Economic uncertainty / working hours	Low
Lim and Chia (2015)	Singapore	Cross-sectional	Random selection of 340 taxi drivers from a taxi company	Questionnaire	Epworth sleepiness scale	Sleep quality / holding an additional part time job / working hour	Medium
Meng et al. (2015)	China	Cross-sectional	274 truck drivers and 286 airport taxi drivers	Questionnaire	Self-reported fatigue & self-reported fatigue-related crash involvement	Driving at night / heavy traffic / sleep time and quality / rest break / working hours	Medium
Berneking et al. (2018)	-	Position statement	ride-hailing industry	–	–	Holding multiple jobs / screening for medical problems	High
Husain et al. (2019)	Malaysia	Cohort study	225 taxi drivers from 8 taxi terminals	Questionnaire	Occupational fatigue exhaustion recovery scale & self-reported crash involvement	Individual and daily emotional demands	High
Li et al. (2019)	China	Two cross-sectional	300 taxi drivers for time point one & 105 taxi drivers for time point two	Questionnaire	Self-reported fatigue-related accident data	Working hours / lower rest ratios / driving experience / confident about their fatigue resistance	Medium
Meng et al. (2019)	China	Cohort study	A convenience sample of 50 taxi drivers	Questionnaire & driving simulator	Self-perceived fatigue using the Brief Fatigue Inventory (BFI)	Driver’s income / age / Working hours	High
Menéndez et al. (2019)	USA	Cross-sectional	996 taxi drivers from two large U.S. cities	Questionnaire	Occupational Driver Behavior Questionnaire	Job demands / the number of miles and hours / experiencing passenger-related violence	Medium
Wang et al. (2019)	China	Cross-sectional	1021 taxi drivers from 21 taxi companies in four cities	Questionnaire	Self-reported fatigue & self-reported crash involvement	Working hours / lack of off days during the week / sleep problems	Medium
Crain et al. (2020)	–	Theoretical	ride-hailing industry	–	–	Algorithmic management / working hours / work-related stressor	–
Peng et al. (2020)	China	Cross-sectional	2391 taxi drivers from four cities	Questionnaire	Self-reported fatigue & crash involvement	Working hours	Medium
Chu (2020)	Taiwan	Cross-sectional	Old taxi drivers (age 60–68), 2,246 crash reports	Administrative data	National traffic crash statistics at the Police Agency	Shift types	High
Mao et al. (2021)	China	Cross-sectional	189,815 ride-hail drivers from a major city in China	Administrative data	The ride-hailing data from Didi Chuxing	Working hours / operation during peak hours / driving experience /passenger rating	High
Dang et al. (2022)	China	Cross-sectional	Ride-hail drivers; number not reported	A computing framework	–	Driver’s income	Medium
Kang et al. (2022)	Singapore	Cross-sectional	16,705 taxi drivers, 41,538 crash records	Administrative data	The 36-month taxi crashes	Working hours, rest break	Medium
Peng et al. (2022)	China	Cross-sectional	2391 taxi drivers from four cities	Questionnaire	Self-reported fatigue & crash involvement	Working hours, age, sleep problems	Medium
Chen et al. (2022)	–	Experimental design	51 taxi drivers	Simulation	Distractions by ride-hailing systems	Increased subjective and cognitive workload by distraction	High

Note. Only significant results are shown for contributing factors to driver fatigue.

3.1. Quality appraisal

Quality assessments were conducted using JBI critical appraisal tools for various study designs, including cross-sectional, cohort, qualitative, and expert opinion studies (Table 1). Overall, the study quality ranged from high to medium, except for one qualitative study, which was of low quality (Facey, 2003). One study employed a theoretical design and was consequently excluded from the quality appraisal due to the unavailability of quality assessment tools designed for this specific study design (Crain et al., 2020). Detailed appraisal checklists and JBI scores can be found in the Appendix.

Studies assessed using the cross-sectional study-specific tool had a mean appraisal score of 70%. The majority of “no” selections resulted from a lack of clarity about confounding factors and the absence of a valid and reliable measurement tool. Studies appraised with the cohort study-specific tool achieved a higher mean appraisal score of 75% (Dalziel & Job, 1997; Husain et al., 2019; Meng et al., 2019). Similarly, “no” selections resulted from a lack of clarity about confounding variables. Furthermore, the qualitative (Facey, 2003) and position statement (Berneking et al., 2018) studies achieved scores of 40% and 83.5%, respectively.

3.2. Factors contributing to fatigue

Figure 3 outlines the number of studies included in the current review that address the statistically significant contributing factors to driver fatigue in taxis and ride-hailing industry. In this review, we identified that one study found a non-significant association between shift type (e.g. afternoon, overnight, 24-hour shift) and fatigue or fatigue-related crashes (Lim & Chia, 2015). Similarly, two studies reported contrary results regarding age including that older taxi drivers had a higher level of fatigue/fatigue-related crashes (Kang et al., 2022; Peng et al., 2020), which differed from the overall pattern observed in the reviewed studies.

Figure 3. Identified factors contributing to driver fatigue in taxi and ride-hailing industry.

Long working hours: Long working hours were identified as a significant contributing factor to driver fatigue in the majority of included studies (66%, n = 12). For example, Li et al. (2019) reported that taxi drivers with a high risk of fatigue-related crashes were involved in long driving hours per working day. Furthermore, driving for longer than 10 h per day (fatigue driving) was positively associated with a higher risk of crashes in taxi drivers (Kang et al., 2022; Lim & Chia, 2015; Peng et al., 2022). These findings can be attributed to the negative relationship between working hours and income (Kang et al., 2022), as drivers often extend their shifts to maximise their earnings. Ride-hail drivers may work longer hours to achieve a specific number of rides within a specified time frame, such as completing 10 rides in a day or 50 rides in a week, to qualify for bonuses. Additionally, they may tend to overestimate their ability to mitigate the negative impact of fatigue on driving performance (Meng et al., 2015). Despite working long hours, taxi and ride-hail drivers often earn low monthly incomes, which in many cases are even lower than the minimum wage (Rani et al., 2022; Shi et al., 2014).

Rest break/shift type: Total average break time was negatively related to fatigue related crash rates (Dalziel & Job, 1997; Kang et al., 2022; Mao et al., 2021; Wang et al., 2019). A survey conducted by Fanxing Meng et al. (2015) revealed that more than 50% of taxi drivers in Beijing did not choose to take breaks when they felt fatigued. This category of drivers, compared to those who stopped to rest, were involved in more crashes. Furthermore, Li et al. (2019) found that taxi drivers with a high risk of fatigue-related crashes, compared to low-risk drivers, had lower rest ratios (proportion of break time within working hours). Some ride-hailing companies (e.g. Uber) have implemented a policy that requires a six-hour rest period after 12 h of continuous driving. However, the American Academy of Sleep Medicine (AASM) suggested that a six-hour break may not be sufficient for drivers who are already working extended hours and driving during non-traditional times when their sleep levels are at peak (Berneking et al., 2018). Therefore, there is a need to extend the rest period in order to effectively reduce crashes associated with fatigued driving (Berneking et al., 2018). The adverse effects of lengthy uninterrupted working hours have been considered by policymakers in other industries. In the European Union, commercial truck drivers must take a break at least 45 min after four and a half hours of driving, monitored by a mandatory digital device, a tachograph. However, implementing similar regulations for ride-hail drivers can be challenging, particularly those who work overnight shifts (Berneking et al., 2018).

Extending hours of wakefulness or driving during the night significantly increased the risk of drowsy incidents in taxi and ride-hailing industries, particularly among drivers who work late nights in addition to their primary daytime jobs (Berneking et al., 2018; Chu, 2020). Chu (2020) reported a significant increase in the likelihood of fatigue-related crashes among taxi drivers who drive at night, with increases of 4.0% (from 8:00 PM to midnight) and 2.6% (from midnight to 6:00am). However, Lim and Chia (2015) found no significant association between working in the afternoon, overnight, or 24-hour shift and experiencing sleepiness and fatigue among taxi drivers. The misalignment of this finding in our review could be due to personal adaptation to the shift status and the likelihood of participants underestimating their reported levels of fatigue and sleepiness. Given that reducing driving hours to prioritise rest can have a negative impact on a driver’s income, it is critical to develop comprehensive strategies and interventions that address driver fatigue while mitigating any negative financial consequences.

3.2.1. Driving experience/driver age

Four studies reported that less experienced drivers had a higher likelihood of being involved in fatigue-related crashes compared to highly experienced drivers (Li et al., 2019; Mao et al., 2021; Meng et al., 2019; Peng et al., 2022). Young drivers were found to be more vulnerable to driving fatigue when they worked longer hours (Meng et al., 2019). This is consistent with the observed income differences between younger and older drivers, with younger drivers reporting higher incomes due to longer driving durations (Meng et al., 2019). In contrast, Peng et al. (2020) reported higher fatigue levels in older drivers, potentially due to the presence of sleep problems as reported by participants. Furthermore, Kang et al. (2022) found that less experienced taxi drivers (aged 30–50 years old) had a lower frequency of crashes compared to drivers over 50. This finding may be attributed to the minimum age requirement of 30 years for qualifying as a taxi and ride-hail driver in Singapore. This requirement ensures that younger drivers have gained driving experience before entering the industry (Kang et al., 2022). Taken together, these findings highlight the importance of considering both driving experience and age as influential factors in driver fatigue, particularly in the ride-hailing industry, where becoming a driver often needs relatively little driving experience.

3.2.2. Job demand

Taxi and ride-hail drivers are often working in stressful and hazardous conditions and are expected to interact frequently with passengers (Bartel et al., 2019; Menéndez et al., 2019). Such poor working conditions, such as passenger-related violence, which is emotionally demanding may contribute to driver fatigue (Husain et al., 2019; Menéndez et al., 2019). Husain et al. (2019) reported that high levels of daily emotional demands increased acute fatigue among taxi drivers in Malaysia. Taxi and ride-hail drivers must also manage job demands associated with distractions caused by mobile phones. Distraction while driving has been found to increase the subjective and cognitive workload for taxi drivers, which can contribute to driver fatigue (Chen et al., 2022). Therefore, taxi and ride-hail drivers who face high job demands and distractions may often experience a feeling of reduced energy, which can ultimately lead to fatigue.

3.2.3. Sleep time/quality

Poor sleep has been identified as a contributing factor to driver fatigue among taxi drivers (Lim & Chia, 2015; Meng et al., 2015; Peng et al., 2022). In a study conducted by Peng et al. (2022) examining the effects of working conditions on taxi drivers, it was found that the combination of long working hours and sleep problems was associated with the highest probability of taxi crashes. Another study (Wang et al., 2019) reported that one-third of taxi drivers from four major cities in China frequently experienced sleep problems, which were identified as a contributing factor to driver fatigue. Additionally, short sleep time was also found to be a contributing factor to driver fatigue (Meng et al., 2015). The combination of long working hours, heavy workload, and traffic congestion in urban areas can contribute to sleep loss, which in turn may exacerbates the impact of fatigue on driving performance (Irwin et al., 2012). Given the challenging working conditions experienced by taxi and ride-hail drivers, they may be more susceptible to the negative impact of sleep loss. Indeed, sleep loss has been found to result in higher decrements in driving performance among taxi drivers compared to non-professional drivers, primarily due to frequent exposure to conditions such as reduced sleep (<7 h) and long working hours (Mahajan & Velaga, 2022). These findings highlight the importance of getting sufficient sleep to mitigate the risks associated with fatigue driving.

3.2.4. Algorithmic management

The reviewed studies on ride-hailing have consistently demonstrated that algorithmic management has a significant impact on the overall experience of drivers, which can contribute to driver fatigue (Crain et al., 2020; Dang et al., 2022; Mao et al., 2021). The algorithms adjust prices based on demand and location and assess drivers’ performance using customer ratings (Crain et al., 2020). Ride-hail drivers may be suspended from using the app and lose a day or more of employment if they consistently receive low ratings (Lim & Chia, 2015). A report by Uber has shown that 2%–3% of drivers had ratings less than 4.6, getting them at the verge of permanent deactivation (Cook, 2015). If the ratings were poor, drivers received the message, “Unfortunately, your driver rating last week was below average” (Cook, 2015). Given that ratings are based on the number of rides accepted or cancelled, drivers have limited flexibility to cancel rides due to the potential consequences (Rani et al., 2023). While these consequences of algorithmic management have been reported higher in the ride-hailing industry, workers in other industries (e.g. freelance writing or graphic design platforms) may pay less attention towards algorithms because the platform worked relatively well for them (Jarrahi & Sutherland, 2019; Wood, 2021). Furthermore, these algorithmic management tactics encourage drivers to work during late nights, peak times, and weekend hours by providing bonuses (Crain et al., 2020), and often leads to long working hours. Therefore, this incentive system may enhance drivers’ willingness to ignore the feeling of fatigue and continue driving in order to obtain higher income (Dang et al., 2022; Facey, 2003). Although algorithmic management has mainly been identified as a challenge for drivers depending on apps, the latest research showed that platforms (e.g. Didi) are targeting taxi drivers to recruit them (Rani et al., 2022).

3.2.5. Traffic condition

Driving in an urban area is a more challenging activity than driving in a rural area due to high traffic density, particularly during peak hours (Mao et al., 2021). Fanxing Meng et al. (2015) found that driving in congested and high demanding urban environments for long hours caused high mental loads, which reduced attention and cognitive ability of taxi drivers. This active fatigue can be attributed to the demanding characteristic of driving in congested traffic conditions. However, low traffic conditions can lead to decreased alertness and the development of passive fatigue among drivers (Gastaldi et al., 2014).

3.2.6. Additional workload

Evidence has shown that drivers in taxi and ride-hailing industries tend to supplement their income with holding multiple jobs (Berg, 2015; Hall & Krueger, 2018). Lim and Chia (2015) found that holding multiple jobs while working as a taxi driver increased the level of fatigue experienced by the drivers. The percentage of drivers feeling fatigued was higher when they were involved in additional part-time jobs compared to those without the additional workload (Lim & Chia, 2015). Furthermore, ride-hail drivers who may have a primary occupation and drive during their “off” time faced an increased crash risk due to driver fatigue after extended periods of wakefulness or at night (Berneking et al., 2018). Managing multiple jobs can be mentally and physically challenging, as it requires spending more time and energy on different tasks without sufficient rest in between (Bouwhuis et al., 2018). This lack of rest can lead to fatigue and poor performance in both their primary and secondary jobs.

3.3. Prevalent risk factors for the taxi and ride-hailing industries

The consequences of fatigue can be short or long term. Short term effects consist of falling asleep at work, short term memory loss, bad-decision making and judgement, headaches, dizziness, blurred vision, or reduced hand-eye co-ordination. However, long-term effects include heart disease, diabetes, high blood pressure, gastrointestinal disorders, lower fertility, anxiety, and depression (Lock et al., 2018). Our review identified the risk factors of fatigue relevant to the taxi and ride-hailing industries as follows:

3.3.1. Road safety

The working conditions of taxi and ride-hail drivers can contribute to a lack of road safety. Evidence suggested that experienced drivers’ hazard perception skills are generally less affected by mild increases in sleepiness, while inexperienced drivers experience significant impairment (Smith et al., 2009). This issue can be problematic in the ride-sharing industry, which often attracts many young and inexperienced drivers. Icasiano and Taeihagh (2021) conducted a study in Singapore, revealing that the ride-sharing sector attracts drivers who lack professional driving training and may prioritise money over safety. Furthermore, ride-hailing companies lack control over independent contractors, making it challenging to enforce the same safety standards as they would on employees (Icasiano & Taeihagh, 2021). Given the lack of safety control over ride-hail drivers, they may be more likely to be involved in traffic collisions.

3.3.2. Work pressure and driver’s health

Continuous pressure to work long hours has been shown to significantly increase fatigue levels and trigger unhealthy risk factors among taxi drivers (Marani et al., 2020), thus posing a significant risk to road safety. Christie and Ward (2019) surveyed workers in gig economy including Uber drivers and found that pressure for timely trip completion along with fatigue caused them to violate speed limits and use mobile phones which increased likelihood of crashes. The rise of the ride-hailing industry and increased competition in the urban market has introduced new pressures to traditional taxi drivers (Contreras & Paz, 2018; Crain et al., 2020; Marani et al., 2020). For example, Crain et al. (2020) found that taxi drivers are under a high pressure to work longer hours and/or during late at night due to competition from the ride-hailing industry, which negatively impacts their sleep health and contributes to fatigue among them. Indeed, taxi drivers who experience low levels of health and higher job stress are more likely to be at an increased risk of traffic accidents (Wang & Delp, 2014).

Optimism bias: Many studies identified optimism bias as a major risk factor in taxi drivers (Dalziel & Job, 1997; Li et al., 2019; Meng et al., 2015). Optimism bias is defined as “the difference between a person’s expectation and the outcome that follows” (Sharot, 2011). This was observed in Chinese drivers who work long hours and feel fatigued but consider themselves at low/no risk of being involved in a road crash (Meng et al., 2015). Li et al. (2019) found that taxi drivers with a high risk of fatigue-related crashes were confident about their resistance to fatigue and considered this impairment more serious for other drivers. Similarly, this unrealistic optimism was found in Australian taxi drivers who believed that they could drive safely when fatigued (Dalziel & Job, 1997). Therefore, high-risk drivers may prefer to drive longer hours and rest less frequently, resulting in less effective rests.

3.3.3. Job precariousness and lack of additional benefits

According to the International Labour Organisation (ILO, 2016) precarious work is characterised by employment insecurity, income inadequacy, and lack of rights and social protections. Given that ride-hail drivers are working as independent contractor, online labour platform companies are not required to provide benefits such as health insurance (Crain et al., 2020). The lack of additional benefits and ambiguity about the future can have a negative and cumulative impact on drivers’ health by forcing them to work long hours (Jaydarifard et al., 2023; Marani et al., 2020). Taxi drivers have also been reported to experience characteristics of precarious employment (Marani et al., 2020). The precarious conditions of taxi driving exacerbates work-related stressors, making it challenging for them to adopt healthier behaviours (Marani et al., 2020). Therefore, job precariousness is potentially an important risk factor for these drivers (Jaydarifard et al., 2023). It is critical to recognise the working conditions of ride-share drivers to design regulations and best practices that improve their health and safety.

3.4. Mitigating driver fatigue in taxi and ride-hailing industries

The relatively high occurrence of fatigue driving in taxi and ride-hailing industries (Berneking et al., 2018; Meng et al., 2015) suggests that regardless of whether drivers recognise they are fatigued, optimism bias may induce them to engage in risky behaviour or to fail to take preventive measures. Therefore, transport managers may seek to use the fatigue risk trajectory model to monitor and reduce driver fatigue. As shown in Figure 4, we outlined the potential methods to reduce driver fatigue in taxi and ride-hailing industries by using the principles of fatigue risk trajectory model (Dawson & McCulloch, 2005; Phillips et al., 2017).

Figure 4. Potential control mechanisms along a risk trajectory for driver fatigue in taxi and ride-hailing industries.

The error trajectory consists of five levels, including work related fatigue, insufficient recovery, fatigue symptoms, fatigue-related errors, and fatigue related incidents. Each of these levels provides insight into potential hazard areas and identifies potential mitigation strategies that can be implemented. At the most basic level (level 1), individuals may experience fatigue due to factors such as long working hours and inadequate rest breaks. Insufficient opportunities for recovery and quality sleep (level 2) can contribute to the manifestation of fatigue-related symptoms (level 3). These symptoms increase the risk of fatigue-related errors (level 4) and ultimately lead to fatigue-related incidents (level 5). A series of control measures can be used to monitor and mitigate fatigue to reduce the risk of fatigue-related errors (Figure 4).

Reviewed factors contributing to driver fatigue in taxi and ride-hailing services can be addressed at the lower levels of the fatigue risk trajectory. For example, long working hours and break time may be monitored and controlled through online platforms and a driver risk assessment. However, the flexibility and autonomy provided by these digital platforms can result in drivers having greater control over their work schedules and potentially driving for extended periods without proper monitoring or regulation. Therefore, the responsibility for managing driving hours and fatigue prevention may currently fall on the individual drivers themselves. Strategies for managing fatigue involving drivers who rely on apps can include providing fatigue management education and training, integrating fatigue management tools into the app (e.g. Just Drive), establishing peer support networks, and offering incentives for taking regular breaks. Indeed, some ride-hail apps go offline after being online for 12 or 13 cumulative hours without a straight 8–10 h break (Uber Team, 2020). However, this may not be effective because many drivers feel fatigued because they are involved in multiple jobs rather than long-shifts (Berneking et al., 2018). Given that most drivers using apps work across multiple platforms, app-based services need to collaborate with each other to share data and monitor working hours across them. Furthermore, collaboration with stakeholders can also help in the development of platform-specific guidelines and best practices. Taxi drivers without an online platform may use an available tool to evaluate their potential risk of accidents (Li et al., 2019). They can reschedule their break times to increase rest duration and frequency, particularly during periods when the risk of fatigue-related crashes is higher.

Ride-hail drivers only require a valid driver’s license to enter the industry, in contrast to traditional taxi drivers who are normally required to have a commercial license in addition to additional permissions and training from transport management. Therefore, they need to be screened for sleep disorders (e.g. such as obstructive sleep apnea) and other medical conditions that can reduce alertness while driving (Berneking et al., 2018). Furthermore, performance-based indicators of fitness for duty, such as psychomotor vigilance tests, can be administered either before starting duty or during breaks to determine the extent to which drivers have recovered from previous work in their free time (Phillips et al., 2017). Regardless of monitoring fatigue by transport managers, drivers require further training to improve their fatigue awareness and correct drivers’ optimism bias (Menéndez et al., 2019). This training can be conducted during orientation programs for new ride-hail and taxi drivers or in seminars as a form of reminder. This education may enable them to recognise fatigue and its associated factors, hence decreasing the possible adverse effects of diver fatigue.

Transport organisations can effectively use advanced technologies to monitor and measure fatigue risks considering risk trajectory approaches. Commercially available fatigue detection technologies use sensors to monitor and record a variety of measures including physiological data, driver positioning and movement behaviours, and driver performance metrics (Mabry et al., 2019). These systems use algorithms to monitor decreases in alertness, which may trigger feedback or information to be sent either directly to the driver (e.g. time spent with eyes closed) or to a control center. For example, the Guardian system, by Seeing Machines, has undergone successful testing and uses cameras and sensors to detect fatigue and distraction in real time (Accident Research Centre, 2020). When the driver is fatigued or distracted, the system sends a warning, and the driver’s seat vibrates rapidly. This technology can also provide an alert by satellite to transportation companies, allowing them to monitor and implement fatigue management measures. Other technologies, such as SmartCap and B-Alert, have been suggested for measuring brain waves as fatigue indicators using polysomnography (Dawson et al., 2014). Furthermore, implementing systems to monitor driver performance indicators (e.g. lane position, speed, braking, distance to the vehicle ahead, acceleration, and fuel economy) enables the detection and management of fatigue-related issues (Liu et al., 2009).

3.5. Limitations

This review is subject to some limitations to be considered when interpreting the findings; (1) the inclusion of literature considering only taxi (conventional and app-based) and ride-hailing industries is not sufficient to understand driver fatigue in other transportation sectors (e.g. food delivery riders and taxi motorcyclists), where the characteristics of the job and risks of fatigue are likely to be very different; (2) While there are some shared characteristics, such as economic insecurity, low and unpredictable incomes, and the challenges faced by drivers in terms of fatigue, between taxi and ride-hailing industries (Wood et al., 2017), it is important to consider the unique aspects of each sector. For example, drivers depending on apps may experience shorter waiting times compared to conventional taxi drivers (Rayle et al., 2016). To the best of our knowledge, this is the first review that summarised all evidence regarding contributing factors of driver fatigue in taxi and ride-hailing industries.

4. Conclusion and future directions

This review explored the current research and knowledge of driver fatigue in taxi and ride-hailing industries. While it is widely acknowledged in the literature that driver fatigue in these transportation sectors is as serious, or more serious than truck driver fatigue, its extent is unknown because there is a paucity of research investigating the cause and contributing factors to driver fatigue. The findings of this review highlighted the importance and impact of driver fatigue in taxi and ride-hailing industries. The prevalence of fatigue-related crashes in taxi and ride-hailing industries suggests that optimism bias may induce drivers to engage in risky behaviours, even if they don’t recognise their fatigue. Therefore, transport managers can consider using the fatigue risk trajectory model to monitor and reduce driver fatigue.

The literature revealed that factors including long working hours, short rest, limited driving experience, job demand, poor sleep, algorithmic management, traffic congestion, and additional workload increase driver fatigue in taxi and ride-hailing services. The relationship between these contributing elements, such as the combination of long working hours and poor sleep, can exacerbate driver fatigue in taxi and ride-hail drivers. None of the reviewed articles employed objective measurements. Therefore, we call on researchers to objectively evaluate driver fatigue and its contributing factors by employing direct monitoring of behavioural indicators, such as eye movements, electroencephalography (EEG), actigraphy, and posture. Furthermore, advanced technologies should be used to monitor likely contributing factors of driver fatigue, such as driving hours, rest breaks, workload (e.g. number of rides completed), and sleep patterns. Additionally, the limited number of reviewed studies on driver fatigue in the ride-hailing industry makes it difficult to have a thorough understanding of the contributing factors to driver fatigue in this population. While the ride-hailing industry is growing across the globe, there is a need for more research into the causes and effects of driver fatigue among ride-hail drivers.

Drivers often engage in secondary activities to manage fatigue while driving. However, engaging in these secondary activities can also cause distraction, which can further increase the risk of feeling fatigued while driving. Interaction with apps while driving, and the behaviour of passengers cause major distractions to drivers in the ride-hailing industry. Strategies to mitigate driver distraction include strict enforcement of laws and use of technology to support driving including improved system integration and interface, in-vehicle navigation systems, and mobile applications such as Just Drive. Just Drive allows drivers to earn points if they stay off their phones while driving. These points can be redeemed for discounts on everything from restaurants, shops to car insurance (Hill et al., 2021). Effective policies and regulations for the ride-hailing industry are dependent on strong evidence-based research. Future studies are recommended involving comprehensive data collection using approaches such naturalistic experiments and statistically sound road safety analysis methods (Behara et al., 2021).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by “Transport Academic Partnership” between the Queensland Department of Transport and Main Roads and the Queensland University of Technology.

Notes

1 In this study, the term “taxi” includes both conventional and app-based services.

2 Given that the words “ride-hailing” and “ridesharing” are often used interchangeably in many studies, we used the term “ride-hailing” to refer to all research that pertains to either ride-hailing or ridesharing. The reader should note that a simplified term “taxi and ride-hailing” used in the rest of the paper refers to taxi (conventional and app-based), ride-hailing, and ridesharing service.

Appendix

Table A1. JBI cross sectional quality assessment.

Study	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	JBI score	%	Quality
Lim and Chia (2015)	Y	Y	Y	–	Y	N	Y	Y	6	75%	Medium
Meng et al. (2015)	Y	Y	N	–	N	N	Y	Y	4	50%	Medium
Li et al. (2019)	Y	Y	Y	–	Y	N	Y	Y	6	75%	Medium
Menéndez et al. (2019)	Y	Y	Y	–	N	N	Y	Y	5	62.5%	Medium
Wang et al. (2019)	Y	Y	Y	–	Y	N	Y	Y	6	75%	Medium
Peng et al. (2020)	Y	Y	N	–	N	N	Y	Y	4	50%	Medium
Chu (2020)	Y	Y	Y	Y	Y	N	Y	Y	7	87.5%	High
Mao et al. (2021)	Y	Y	Y	–	Y	Y	Y	Y	7	87.5%	High
Dang et al. (2022)	Y	Y	N	–	N	N	Y	Y	4	50%	Medium
Kang et al. (2022)	Y	Y	Y	Y	N	N	Y	Y	6	75%	Medium
Peng et al. (2022)	Y	Y	Y	–	N	N	Y	Y	5	62.5%	Medium
Chen et al. (2022)	Y	Y	Y	Y	Y	N	Y	Y	7	87.5%	High

Note. Y = yes; N = no; U = unclear; – = not applicable. Higher total scores are indicative of higher quality documents. Q1. Were the criteria for inclusion in the sample clearly defined? Q2. Were the study subjects and the setting described in detail? Q3. Was the exposure measured in a valid and reliable way? Q4. Were objective, standard criteria used for measurement of the condition? Q5. Were confounding factors identified? Q6. Were strategies to deal with confounding factors stated? Q7. Were the outcomes measured in a valid and reliable way? Q8. Was appropriate statistical analysis used?