Exploring the prevalence and factors associated with self-reported traffic crashes among app-based motorcycle taxis in Vietnam

Transport Policy 81 (2019) 68–74

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Transport Policy journal homepage: www.elsevier.com/locate/tranpol

Exploring the prevalence and factors associated with self-reported traffic crashes among app-based motorcycle taxis in Vietnam

T

Duy Quy Nguyen-Phuoca,b,∗, Ha Anh Nguyenc, Chris De Gruyterd, Diep Ngoc Sue , Vinh Hoang Nguyenf a

Division of Construction Computation, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam c Department of Railway Transport Economics, Faculty of Transport – Economics, University of Transport and Communications, 03 Cau Giay Street, Dong Da District, Hanoi, Viet Nam d Centre for Urban Research, School of Global, Urban and Social Studies, RMIT University, City Campus, 124 La Trobe Street, Melbourne, Victoria, 3000, Australia e Faculty of Tourism, University of Economics – The University of Danang, 71 Ngu Hanh Son, Danang City, Viet Nam f Faculty of Bridge and Road Engineering, University of Science and Technology – The University of Danang, 54 Nguyen Luong Bang Street, Lien Chieu District, Danang City, Viet Nam b

ARTICLE INFO

ABSTRACT

Keywords: Mobile app Motorcycle taxi Traffic crash Ride-hailing Risk factor Vietnam

Motorcycle taxis play an important role in many developing countries, particularly in servicing areas where conventional public transport is not available. This form of transport has become more popular in recent years since online ride-hailing companies launched motorcycle taxi services. However, little is known about traffic crash risks among app-based motorcycle taxi riders. This study therefore aimed to investigate the prevalence and factors associated with road traffic crashes among app-based motorcycle taxi riders. A field survey and online survey were undertaken to obtain information about riders’ socio-demographics, work patterns, travel behaviour and crash involvement. A total of 602 valid survey responses from riders were obtained across three cities in Vietnam, including 571 males and 31 females. The overall reported prevalence of road traffic crashes among app-based motorcycle taxi riders over a one-year period was 30%. Binary logistic regression modelling showed that traffic crashes were associated with non-students, low education levels, high daily travel distances, regular smoking, and using a mobile phone while driving. Despite regulation of online ride-hailing motorcycle taxi services in Vietnam, the reported prevalence of crashes among riders is considered to be relatively high. Targeted interventions to reduce the risk of being involved in a crash should be considered, such as increasing road safety education for non-student riders and imposing a daily travel distance limit for riders.

1. Introduction While motorcycles are often used for leisure purposes in most developed countries, they are a major form of transport in many developing countries (Lin et al., 2001; Nguyen-Phuoc et al., 2019). Along with a growing level of motorisation, motorcycle ownership and use has been increasing rapidly in developing countries over the past decade (Tuan and Mateo-Babiano, 2013; Akaateba et al., 2014). Motorcycle taxis provide a common form of public transport in low and middle income countries (Akinlade and Brieger, 2003; Sopranzetti, 2012; Tuan and Mateo-Babiano, 2013; Lan et al., 2013; Oginni et al., 2007), offering a reliable, flexible and low-cost form of mobility (Iles, 2005). They can also act as useful feeder services to connect local communities

to main streets, together with other public transport modes such as buses and trains (Oshima et al., 2007; Kumar, 2011). In addition, motorcycle taxi riders can establish their business relatively easily due to the flexible work schedule and minimal entry requirements (riding licence and a motorcycle) (Wu and Loo, 2016). Recently, the introduction of online ride-hailing services has led to the emergence of app-based motorbike taxis in a number of Asian cities. These services use online-enabled platforms to connect passengers and local motorcycle riders. Ride-hailing services like Uber, Lyft or Grab are shaping the way that people move in major cities since they have a number of advantages such as ease of use, short waiting times and low fares. Passengers can know in advance who is going to pick them up, including the rider's age, face, name, and the vehicle registration

Corresponding author. Division of Construction Computation, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. E-mail addresses: [email protected] (D.Q. Nguyen-Phuoc), [email protected] (H.A. Nguyen), [email protected] (C. De Gruyter), [email protected] (D.N. Su), [email protected] (V.H. Nguyen). ∗

https://doi.org/10.1016/j.tranpol.2019.06.006 Received 9 November 2018; Received in revised form 7 June 2019; Accepted 12 June 2019 Available online 12 June 2019 0967-070X/ © 2019 Published by Elsevier Ltd.

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D.Q. Nguyen-Phuoc, et al.

number. However, road traffic crashes have become a major public health concern in many countries, particularly those in the developing world. Globally, more than 1.2 million people are killed in road crashes each year while the number injured is estimated to be as high as 50 million people (WHO, 2016). Around 85% of all global road deaths are due to crashes occurring in developing countries. With a high proportion of motorcycles in South East Asia, motorcyclists are estimated to constitute a third of traffic fatalities (WHO, 2015). In Vietnam, approximately 14,000 people lose their lives each year due to road traffic crashes, with 59% of these being motorcyclists (WHO, 2018). Motorcycle taxi riders have a higher risk exposure to road traffic crashes given that they ride on roads more frequently (Wolfe, 1982). With the increasing popularity of app-based motorcycle taxi services in developing countries, this form of transport is considered to contribute directly and indirectly to a high proportion of road traffic crashes. In contrast to traditional motorcycle taxi riders, app-based riders employed by ridehailing firms are required to attend safety awareness training sessions before commencing employment. Hence, they are expected to be involved in less traffic road crashes than traditional taxi riders. There have been many studies investigating safety risks among carbased taxi drivers in both developed and developing countries (La et al., 2013; Peltzer and Renner, 2003; Lam, 2004). Although motorcycle taxis are recognised as a popular form of public transport in developing countries, relatively few studies have been carried out to determine the crash prevalence among motorcycle taxi riders. Oginni et al. conducted a field survey of 224 commercial motorcyclists in Nigeria to determine crash prevalence and identify knowledge, attitudes and practices of these riders in the use of crash helmets and other motorcycling safety measures (Oginni et al., 2007). They found that 46% of motorcyclists have been involved in traffic road crashes at least one time. Poor road conditions and failure to observe road signs were found to be two major reasons which contributed to frequent crashes. Another study exploring the extent of traffic safety challenges from using motorcycles for commercial transport was carried out by Agyekum-Boamah (2012). She used questionnaires to collect data from 200 motorcyclists in the Greater Accra, Volta and Upper East Regions of Ghana. The results showed that approximately 50% of motorcyclists have been involved in road traffic crashes in which around 80% reported experiencing more than one crash. More recently, Wu and Lo explored the characteristics of both motorcycle taxi riders and non-occupational motorcyclists in Maoming, South China (Wu and Loo, 2016). Attitudes toward road traffic safety and self-reported riding behaviours between these two groups were also investigated in this study. They found that motorcycle taxi riders were more likely to be exposed to road safety risks than nonoccupational motorcyclists as taxi riders tended to run red-lights more often and travel above the speed limit early in the morning and late at night. In summary, limited research has explored the safety risks among conventional motorcycle taxi riders, with no study focused on motorcycle taxi riders working for online ride-hailing services. To the best of the authors’ knowledge, this paper represents the first study to explore the prevalence and factors associated with road traffic crashes among app-based motorcycle taxi riders working for online ride-hailing services. Data for this study was collected using survey questionnaires across three cities in Vietnam. This paper1 is structured as follows. The next section provides context for the research, followed by a description of the methodology used to explore factors affecting traffic crashes among app-based motorcycle taxi riders in Vietnam. A set of results are then presented, comprising of descriptive statistics and logistic regression model results.

The paper concludes with a discussion of the implications for practice and areas for further research. 2. Research context The research was undertaken in Vietnam where motorcycles are the dominant transport mode. In most Vietnamese towns, traditional motorcycle taxis, called ‘xe om’, are ubiquitous, especially at intersections, train stations, bus stops and shopping centres (Cervero, 2000). Although this form of transport is very popular, traditional motorcycle taxis have been operating without any regulation. Fares are always negotiated between riders and passenger/s. In late 2014, Grab, an online ride-hailing company, launched the ‘GrabBike’ service in Ho Chi Minh City and later in Hanoi and Da Nang (Roscher, 2018). This app-based motorcycle taxi service has become very popular and has attracted a considerable user base in Vietnam (Grab, 2018a). The service uses a smartphone's GPS and allows users to book a ride from the nearest motorcycle taxi registered with GrabBike. Given that the app displays the estimated fare for the ride, it is perhaps no surprise that the launch of GrabBike was successful. Online appbased motorcycle taxi riders are regulated by ride-hailing firms and are able to legally operate motorcycle taxis in Vietnam in line with local transport laws. Since its establishment, the Grab app has been downloaded from over 100 million mobile devices (Grab, 2018b), offering private car, motorcycle, taxi, and carpooling services across 7 countries and 142 cities in South East Asia, with one out of every three passengers using multiple services. Grab's current market share is 95% in thirdparty taxi-hailing and 72% in private vehicle hailing, with the largest land fleet in South East Asia (Grab, 2018a). Following the success of Grab, a number of other online ride-hailing companies have been introduced in Vietnam such as Go-Viet, VATO or Aber. Currently, there are approximately 175,000 riders who are working for ride hailing firms in Vietnam (Cafef, 2018). News reports suggest that around 80% of riders are university students (Vietq, 2017; VTCNEWS, 2017), with no other published evidence on rider socio-demographics available. 3. Methodology 3.1. Data collection The data used in this research was obtained from field surveys and an online survey undertaken in the three largest cities of Vietnam (including Hanoi, Ho Chi Minh City and Da Nang) where a high proportion of app-based motorcycle taxi riders operate (see Fig. 1). Data was collected during May and June 2018. To design the questionnaire, the most relevant studies in the literature were reviewed to determine factors related to drivers/riders which have influenced road traffic crashes (Oginni et al., 2007; Agyekum-Boamah, 2012; Truong et al., 2016b). These factors were adapted to this study to explore factors associated with traffic crashes among app-based motorcycle taxi riders. The questionnaire consisted of four major sections. The first section collected information about the socio-demographics of motorcycle taxi riders such as age, gender, home town, place of residence, marital status, job, education level, and years of motorcycle license ownership. In addition, basic information about the type of taxi service (i.e. full-time or part-time) and motorcycle used (i.e. manual or automatic) was also collected. The second section asked a set of closed questions about respondents' average daily travel time and travel distance. Information about riders' lifestyle and behaviour towards safety while riding was asked in section three. Here, a number of Likert scale questions were designed to determine whether or not riders used a mobile phone while riding given that this device is used by all app-based motorcycle taxi riders. The last section of the questionnaire asked riders to self-report any road traffic crashes that they had experienced over the last 12 months (2017-18). Details of crashes were taken into account, including reasons, time of day, location,

1 This paper builds upon earlier work presented at the Transportation Research Board (TRB) 98th Annual Meeting, Washington D.C., United States, 13–17 January 2019.

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initially hesitant to participate. In these cases, the surveyors clearly explained the purpose of the study and highlighted that the survey was confidential and that their responses would be anonymous. Riders completing the survey received a prepaid scratch mobile phone recharge card to the value of 20,000 Vietnamese Dong (VND, approximately US$1) as encouragement and acknowledgment of their participation in the survey. After four weeks of data collection during May 2018, a total of 462 app-based motorcycle taxi riders had completed the field survey in Hanoi (189 responses), Da Nang (178 responses) and Ho Chi Minh (95 responses). To increase the survey response further, a web-based questionnaire was also developed and posted on the Facebook groups of app-based motorcycle taxi riders in the three cities while the field surveys commenced. During May and June 2018, a total of 177 webbased respondents completed the survey in Hanoi (23 respondents), Da Nang (33 respondents) and Ho Chi Minh (121 respondents). The surveyors sent private messages to each member of the Ho Chi Minh appbased motorcycle taxi rider Facebook group to boost survey participation so that an approximately equal number of survey responses could be achieved in each of three cities. During May to June 2018, a total of 639 riders out of a sampling frame of approximately 175,000 app-based motorcycle taxi riders in Vietnam completed the questionnaire, comprising 177 web-based respondents and 462 field-based respondents. After discarding questionnaires with no meaningful value on any of the key outcome variables, a total of 602 valid responses including 172 web-based (97.2%) and 430 field-based (93.1%) formed the basis for the analysis, as detailed in Table 1. The highest number of valid respondents was in Ho Chi Minh City with 211 riders, followed by Da Nang (198 respondents) and Hanoi (193 respondents). Table 1 also provides a basic demographic summary of survey respondents. While demographics are discussed further in the next section, the average age of respondents was relatively similar across the three cities, as was the proportion of male respondents. However, the proportion of student respondents was lower in Ho Chi Minh (38.9%) compared to Hanoi (58.0%) and Da Nang (44.4%). 3.2. Data analysis The field-based data was first transferred from the paper forms to a computer. Observations without value or with no meaningful value on any of the variables were discarded from further analysis. SPSS was then used to analyse the data. Using descriptive statistics, the prevalence of road traffic crashes with 95% confidence intervals was calculated and classified by a number of variables such as age and gender. A binary logistic regression model was then developed to determine factors associated with road traffic crashes among app-based motorcycle taxi riders.

Fig. 1. Location of Hanoi, Da Nang and Ho Chi Minh City in Vietnam.

weather, road conditions as well as riders’ health status, and their level of attention while riding. Riders were also asked whether they had run a red light, drank alcohol before riding, or exceeded the speed limit. Students from universities in Hanoi, Da Nang and Ho Chi Minh City were recruited and trained before carrying out the surveys. In this regard, three groups of surveyors were formed in both Da Nang and Hanoi, while one survey team was formed in Ho Chi Minh City. Each group included 2–3 students as the main surveyors. Before conducting the field surveys, all groups were provided with sufficient information about the aims and objectives of both the study and the survey, with detailed guidance on how to conduct the survey efficiently. Surveyors were allocated to do the surveys at various sites where major trip generators were located, such as universities, bus stations, railway stations or shopping centres. An online ride-hailing app was also used to search for places where riders typically gather. At these locations, the questionnaires were distributed randomly to riders. As the survey related to traffic crashes, some app-based motorcycle taxi riders were

Table 1 Details of the data collection process and basic demographic summary of respondents.

70

City

Hanoi

Da Nang

Ho Chi Minh

Total

Number of survey teams Number of surveyors in a team

3 2

3 2

1 2

7 6

Number of valid respondents collected from online survey Number of valid respondents collected from field surveys Total respondents

20

32

120

172

173

166

91

430

193

198

211

602

Average age of respondents (years) Male respondents (%) Student respondents (%)

24.4 94.3 58.0

24.9 98.5 44.4

27.7 91.9 38.9

25.7 94.9 46.8

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Table 2 Characteristics of app-based motorcycle taxi riders in Vietnam. Variable

Overall Demographics Mean age in years (standard deviation) Gender Female Male Marital status Single Married Migrant status Non-migrant Migrant Occupation Other (including workers, office staff, graduated students who are looking for work) Student Education level Above high school High school Less than high school Mean riding experience in years (standard deviation) Working history Employment status Part-time Full-time Perceived sufficiency of income No Yes Motorcycle type Automatic Manual Motorcycle ownership Inherited from relatives Bought with their own money Daily travel distance < 50 km/day ≥ 50 km/day and < 100 km/day ≥ 100 km/day Weekly working hours < 40 h/week ≥ 40 h/week and < 60 h/week ≥ 60 h/week Rider lifestyle and behaviour Smoking status Never Sometimes Regularly Drinking status Never Sometimes Regularly Use of mobile phone while riding Never Seldom (few times a year) Sometimes (few times a month) Often (few times a week) Regularly (few times a day)

n

602

%

100

No crash (n = 419)

Crash (n = 183)

Rate (%) (n1)

95% CI

Rate (%) (n2)

95% CI

69.60 (419)

65.93–73.28

30.40 (183)

26.72–34.07

25.74 (7.32)

24.46 (6.18)

31 571

5.15 94.85

83.87 (26) 68.83 (393)

70.92–96.82 65.03–72.63

16.13 (5) 31.17 (178)

3.18–29.08 27.37–34.97

421 181

69.93 30.07

76.48 (322) 53.59 (97)

72.43–80.54 46.33–60.86

23.52 (99) 46.41 (84)

19.46–27.57 39.14–53.67

105 497

17.44 82.56

68.57 (72) 69.82 (347)

59.69–77.45 65.78–73.85

31.43 (33) 30.18 (150)

22.55–40.31 26.15–34.22

320 282

53.16 46.84

57.19 (183) 83.69 (236)

51.77–62.61 79.38–88.00

42.81(137) 16.31(46)

37.39–48.23 12.00–20.62

107 17.77 447 74.25 48 7.79 6.95 (5.59)

80.37 (86) 67.34 (301) 66.67 (32) 5.94 (4.65)

72.85–87.90 62.99–71.69 53.33–80.00

19.63 (21) 32.66 (146) 33.33 (16) 9.26 (6.78)

12.10–27.15 28.31–37.01 20.00–46.67

477 125

79.24 20.76

73.79 (352) 53.60 (67)

69.85–77.74 44.86–62.34

26.21(125) 46.40 (58)

22.26–30.15 37.66–55.14

186 416

30.90 69.10

80.65 (150) 64.66 (269)

74.97–86.32 60.07–69.26

19.35 (36) 35.34 (147)

13.68–25.03 30.74–39.93

101 501

16.78 83.22

62.38 (63) 71.06 (356)

52.93–71.82 67.09–75.03

37.62 (38) 28.94 (145)

28.18–47.07 24.97–32.91

266 336

44.19 55.81

78.95 (210) 62.20 (209)

74.05–83.85 57.02–67.39

21.05 (56) 37.8 (127)

16.15–25.95 32.61–42.98

140 188 274

23.26 31.23 45.51

86.43 (121) 77.13 (145) 55.84 (153)

80.76–92.10 71.12–83.13 49.96–61.72

13.57 (19) 22.87 (43) 44.16 (121)

7.90–19.24 16.87–28.88 38.28–50.04

296 168 138

49.17 27.91 22.92

71.28 (211) 80.36 (135) 52.90 (73)

66.13–76.44 74.35–86.36 44.57–61.23

28.72 (85) 19.64 (33) 47.10 (65)

23.56–33.87 13.64–25.65 38.77–55.43

366 189 47

60.80 31.40 7.81

78.96 (289) 57.14 (108) 46.81 (22)

74.79–83.14 50.09–64.20 32.54–61.07

21.04 (77) 42.86 (81) 53.19 (25)

16.86–25.21 35.80–49.91 38.93–67.46

235 349 18

39.04 57.97 32.99

75.74 (178) 65.62 (229) 66.67 (12)

70.26–81.22 60.63–70.60 44.89–88.44

24.26 (57) 34.38 (120) 33.33 (6)

18.78–29.74 29.40–39.37 11.56–55.11

289 82 59 42 130

48.01 13.62 9.80 6.98 21.59

75.78 50.00 69.49 73.81 66.92

70.84–80.72 39.18–60.82 57.74–81.24 60.51–87.11 58.84–75.01

24.22 50.00 30.51 26.19 33.08

19.28–29.16 39.18–60.82 18.76–42.26 12.89–39.49 24.99–41.16

(219) (41) (41) (31) (87)

28.67 (8.77)

(70) (41) (18) (11) (43)

CI = Confidence Interval; n = total number of respondents; n1 = number of respondents not involved in a crash in last 12 months; n2 = number of respondents involved in a crash in last 12 months; Student = person studying at a college or university.

4. Results

automatic motorcycle (17%). Table 2 provides additional descriptive statistics about the sample, including crash and non-crash prevalence among app-based motorcycle taxi riders. Variables are classified into three groups: demographics, working history, and rider lifestyle and behaviour. The overall self-reported prevalence of road traffic crashes among surveyed riders was 30.4% (95% CI: 26.7–34.1). A greater proportion of male riders reported to be involved in crashes compared to female riders (31% compared to 16%). The reported prevalence of crashes was much lower for riders who were students (16%, 95% CI: 12.0–20.6)

4.1. Crash prevalence among app-based motorcycle taxi riders Of the 602 valid survey responses, a high majority of riders (571, or 95%) were male. The average age of riders was relatively low at 25.7 years old. Unsurprisingly, this corresponds to the occupation and employment status of riders as approximately half were students (47%), with most operating app-based motorcycle taxis as their part-time job (79%). Most riders used a manual motorcycle (83%) in contrast to an 71

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compared to other riders (43%, 95% CI: 37.4–48.2). Lower crash prevalence was also reported by riders with a higher education level. In this regard, the lowest crash prevalence was found among riders with an education level above high school (e.g. undergraduate degree), with only 20% of these riders reporting to have been involved in a crash in the last 12 months. By contrast, one-third of riders (33%) with an education level less than high school reported involvement in a crash in the last 12 months. Although the number of automatic motorcycles used by surveyed riders was far less than that of manual motorcycles, the former was found to have a higher reported prevalence of crashes (38%, 95% CI: 28.2–47.1) than the latter (29%, 95% CI: 25.0–32.9). Possible reasons for this are discussed later in this paper. Crash prevalence significantly increased with greater daily travel distance. The reported crash rate was the highest among riders who travelled more than 100 km per day (44%, 95% CI: 38.3–50.0), followed by those who drove between 50 and 100 km per day (23%, 95% CI: 16.9–28.9), and less than 50 km per day (14%, 95% CI: 7.9–19.2). However, a similar trend was not found with riders’ weekly working hours. While crash prevalence was reported to be highest among riders who worked more than 60 h per week (47%, 95% CI: 38.8–55.4), those who worked less than 40 h per week encountered more crashes than riders who worked 40–60 h per week (29% vs. 20%). Regarding riders' lifestyle, the highest prevalence of crashes was among riders who regularly smoke cigarettes (53%, 95% CI: 38.9–67.5). Crash rates were relatively similar between riders who drink alcohol ‘sometimes’ and ‘regularly’ (34% vs. 33%), followed by those who never drink alcohol (24%). However, reported crash prevalence was higher among riders who seldom use a mobile phone while riding (50%, 95% CI: 39.2–60.8), compared to those who reported sometimes (31%), often (26%), or regularly (33%) using a mobile phone while riding. As expected, riders who had never used a mobile phone while riding reported the lowest crash prevalence (24%, 95% CI: 19.3–29.2).

Table 3 Logistic regression model results for the prevalence of traffic crashes.

4.2. Factors associated with road traffic crashes Results of the logistic regression model in Table 3 show the relationship between the characteristics of motorcycle taxi riders and road traffic crashes. Five factors were found to be significantly associated with the prevalence of road traffic crashes. These were: occupation, education level, daily travel distance, smoking status and mobile phone use while riding. Riders who were students were less likely to report being involved in road traffic crashes than others who were not studying (OR = 0.29, p < 0.001). Riders with a high school degree only were around three times more likely to report being involved in road traffic crashes than those who had graduated from a college/university (OR = 3.05, p < 0.01). An increase in daily travel distance was associated with greater reported prevalence of road traffic crashes (OR = 2.54, p < 0.01 and OR = 3.97, p < 0.001 for 50–100 km and ≥100 km respectively). In terms of riders’ lifestyle and behaviour, riders who regularly smoke were around three times more likely to report being involved in a crash than riders who did not smoke (OR = 3.25, p < 0.01). Similarly, riders who seldom used a mobile phone while riding were almost three times more likely to report being involved in a crash than those who did not use a mobile phone while riding (OR = 2.87, p < 0.001).

Variable

Coefficient

Std. Error

Adj. OR

95% CI

Intercept Demographics Age Gender Female Male Marital status Single Married Migrant status Non-migrant Migrant Occupation Other (including workers, office staff, graduated students who are looking for work) Student Education level Above high school High school Less than high school Riding experience Working history Employment status Part-time Full-time Perceived sufficiency of income No Yes Motorcycle type Automatic Manual Motorcycle ownership Inherited from relatives Bought with their own money Daily travel distance < 50 km/day ≥ 50 km/day and < 100 km/ day ≥100 km/day Weekly working hours < 40 h/week ≥ 40 h/week and < 60 h/week ≥ 60 h/week Rider lifestyle and behaviour Smoking status Never Sometimes Regularly Drinking status Never Sometimes Regularly Use of mobile phone while riding Never Seldom (few times a year) Sometimes (few times a mouth) Often (few times a week) Regularly (few times a day) Log likelihood AIC BIC

−3.695

1.00

0.02

0.01–0.17

0.022

0.02

1.02

0.98–1.06

Ref 0.410

Ref 0.60

Ref 1.51

Ref 0.47–4.87

Ref 0.065

Ref 0.30

Ref 1.07

Ref 0.59–1.91

Ref 0.081

Ref 0.29

Ref 1.08

Ref 0.61–1.93

Ref

Ref

Ref

Ref

−1.239***

0.34

0.29

0.15–0.56

Ref 1.116** −0.297 −0.003

Ref 0.39 0.45 0.01

Ref 3.05 0.74 1.00

Ref 1.42–6.58 0.31–1.80 0.99–1.01

Ref 0.036

Ref 0.30

Ref 1.04

Ref 0.57–1.88

Ref 0.388

Ref 0.25

Ref 1.47

Ref 0.90–2.41

Ref −0.172

Ref 0.29

Ref 0.84

Ref 0.48–1.48

Ref 0.106

Ref 0.26

Ref 1.11

Ref 0.67–1.85

Ref 0.934**

Ref 0.32

Ref 2.54

Ref 1.36–4.78

1.380***

0.32

3.97

2.12–7.45

Ref −0.632

Ref 0.28

Ref 0.53

Ref 0.31–0.92

0.105

0.33

1.11

0.59–2.10

Ref 0.384 −1.177**

Ref 0.27 0.40

Ref 1.47 3.25

Ref 0.87–2.47 1.48–7.10

Ref −0.081 −0.125

Ref 0.25 0.61

Ref 0.92 0.88

Ref 0.56–1.51 0.26–2.94

Ref 1.053*** 0.549 0.550 0.604* −289.280 628.560 738.566

Ref 0.31 0.37 0.44 0.28

Ref 2.87 1.73 1.73 1.83

Ref 1.55–5.31 0.83–3.61 0.73–4.12 1.06–3.16

*p < 0.05, **p < 0.01, ***p < 0.001, Adj. OR = Adjusted Odds Ratio, CI = Confidence Interval.

5. Discussion

figure is lower than the crash rates reported by conventional motorcycle taxi riders in African countries such as Ghana and Nigeria, with 50% and 46% respectively (Oginni et al., 2007; Agyekum-Boamah, 2012). Before app-based motorcycle taxi riders can commence operating in Vietnam, their backgrounds are checked by ride-hailing companies, with training courses on traffic rules and safety delivered to all riders.

This study aimed to explore the prevalence and factors associated with road traffic crashes among app-based motorcycle taxi riders, using a case study of Vietnam. The results showed that approximately onethird of surveyed motorcycle taxi riders (30.4%) reported to be involved in at least one road traffic crash over a one-year period. This 72

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Additionally, vehicle checks are also undertaken to ensure motorcycles are in satisfactory condition (Grab, 2018c). However, it is unclear to what extent these checks contribute to reducing the prevalence of traffic crashes among app-based motorcycle taxi riders as crash rates for conventional (non-app-based) motorcycle taxi riders in Vietnam are not available for comparison. This highlights a key area for future research. The findings show that nearly 50% of app-based motorcycle taxi riders who participated in the survey were students. The remaining riders were mostly workers, office staff and graduated students who were looking for work. This explains why the mean age of the riders (25.7 years old) is much lower than that of conventional motorcycle taxi riders in Vietnam who are typically aged between 40 and 60 years old (Tuan and Mateo-Babiano, 2013). It is therefore clear that online ride-hailing motorcycle taxi services have attracted a vast majority of younger riders who are likely to have been the first and most aggressive users of new technology (Lee, 2007). Indeed, 93% of university students in Vietnam have smartphones (Q&ME, 2017). The findings from this study also show a positive association between the prevalence of road traffic crashes and the riders’ age. The mean age of app-based motorcycle taxi riders who reported not being involved in a crash was around 24.5 years old compared to 28.7 years old for riders reporting to have experienced a crash. In fact, students in Vietnam usually receive road safety education in their first year of university so their safety awareness is likely to be higher during this time (Hung, 2011). Increasing the time and frequency of safety awareness training organised by railhailing companies for new and current registered riders, particular for non-student riders and older riders, could therefore assist in reducing crash rates and improving road safety outcomes. The findings also showed that the vast majority of app-based motorcycle taxi riders were male (approximately 95%). This gender ratio is similar to traditional motorcycle taxi riders in Vietnam (Tuan and Mateo-Babiano, 2013). Risky riding behaviours such as speeding, running red lights, not wearing a helmet or riding on the wrong side of a road have been found by previous studies to be more prevalent among males than females, especially younger riders (Rowland et al., 1996; Lin et al., 2003; Creaser et al., 2009). The results of this study also found that the crash rate among male riders was nearly double that of female riders (31% vs. 16%). However, due to the domination of male riders in the sample, a greater sample of female riders would be needed to confirm this finding. More than 80% of surveyed riders reported using a manual motorcycle. This type of motorcycle is generally favoured due to its cheaper price, lower fuel consumption and higher reliability compared to automatic motorcycles. Interestingly, automatic motorcycles were more likely to be involved in road traffic crashes than manual motorcycles. In contrast to manual motorcycles, automatic motorcycles are generally designed with a light head and most of the weight distributed in the rear of the vehicle. Hence, if riders do not apply both the front and rear brakes correctly in an emergency stop, it is much easier for the wheels to lock-up and slide, resulting in a loss of control. As expected, exposure to the road environment (daily travel distance) was found to significantly impact reported crash prevalence among riders. The results of the logistic regression model revealed that riders who travelled more than 50 km per day consistently reported a higher rate of road traffic crashes. This result points to the opportunity for ride-hailing taxi companies to identify those at risk through increased exposure and initialise interventions to reduce the risks associated with this group. For instance, a limit on the daily travel distance could be applied to app-based motorcycle taxi riders to reduce their risk of being involved in a crash. In terms of lifestyle, riders who smoke regularly were found to be more likely to report being involved in road traffic crashes than others. The relationship between crash involvement and smoking habits among motorcycle riders has been rarely explored in the literature. However, this association among car riders has been a more common topic investigated in many studies (La et al., 2013; Hutchens et al., 2008). Sacks

and Nelson reviewed more than 90 studies and stated a telling association between smoking and many types of unintentional injury, such that smokers had a 50% increased risk of motor vehicle crashes, as well as 50% more traffic violations (Sacks and Nelson, 1994). Possible explanations given for the association of smoking habits with crashes are that smokers have a high tendency to smoke while driving which generates distractibility involved in the act of smoking (including onehanded steering), direct toxicity (potentially contributing to reduced night vision, vision performance, and reduced driving performance), and associated risk-taking (Avi et al., 2001). However, these aspects could differ for motorcyclists, compared to car drivers, so further research on this topic is needed. Mobile phone use while riding was reported by 52% of app-based motorcycle taxi riders, in which over 21% reported that they use their device/s regularly (a few times per day). This figure is nearly double the proportion of students in Vietnam who report talking on a mobile phone regularly while riding a motorcycle (10.2%) (De Gruyter et al., 2017). The findings from the logistic regression model showed that the crash rate among motorcycle taxi riders was significantly associated with mobile phone use while riding, consistent with the findings of previous studies (Truong et al., 2016a; De Gruyter et al., 2017). Targeted enforcement of mobile phone use while riding, particularly among app-based motorcycle taxi riders, is therefore recommended to reduce the prevalence of road traffic crashes. 6. Conclusion In closing, this study has investigated the prevalence and factors associated with road traffic crashes among app-based motorcycle taxi riders in a country where motorcycles are the dominant form of transport. However, this study is also subject to a number of limitations. First, information about riders who have left the occupation due to serious injuries could not be obtained as part of the survey. Hence, the reported crash rate may be underestimated. Second, potential bias in the self-reported data may be present. While surveyors clearly explained the purpose of the study and results were anonymous, some respondents may still not have been willing to report their crash history. Third, risky behaviours among riders and the main causes of selfreported crashes were not investigated in detail in this paper. Further research on these aspects is essential to inform the development of more effective interventions to reduce the prevalence of crashes. Given that app-based motorcycle taxi services are regulated by online ride-hailing companies, the self-reported crash prevalence among riders (30.4%) is concerning. With continual growth and expansion of these services in Vietnam as well as other South East Asian countries, targeted interventions are needed to reduce crash risks among appbased motorcycle taxi riders. References Agyekum-Boamah, P., 2012. The growing use of motorcycles for commercial transport and traffic safety in Ghana. Inj. Prev. 18 A190-A190. Akaateba, M.A., Amoh-Gyimah, R., Yakubu, I., 2014. A cross-sectional observational study of helmet use among motorcyclists in Wa, Ghana. Accid. Anal. Prev. 64, 18–22. Akinlade, O.C., Brieger, W.R., 2003. Motorcycle taxis and road safety in southwestern Nigeria. Int. Q Community Health Educ. 22, 17–31. Avi, A., Yehonatan, S., Alexandra, H., Arieh, E., 2001. Do accidents happen accidentally? A study of trauma registry and periodical examination database. J. Trauma Acute Care Surg. 50, 20–23. Cafef, 2018. 4 năm sau cuộc cách mạng di chuyển của Grab, Uber bạn có nhận ra đi xe ôm bây giờ giá còn chưa đến 1/2 so với ngày trước? Hanoi, Vietnam. Cervero, R., 2000. Informal transport in the developing world. UN-HABITAT. Creaser, J.I., Ward, N.J., Rakauskas, M.E., Shankwitz, C., Boer, E.R., 2009. Effects of alcohol impairment on motorcycle riding skills. Accid. Anal. Prev. 41, 906–913. Grab, 2018a. Grab Celebrates its 1 Billionth Ride. Vietnam. Grab, 2018b. Toyota to Invest US$1 Billion in Grab as Lead Investor for Grab's New Round of Financing. Grab, 2018c. Your Safety Begins with Safe Drivers. De Gruyter, C., Truong, L.T., Nguyen, H.T., 2017. Who's calling? Social networks and mobile phone use among motorcyclists. Accid. Anal. Prev. 103, 143–147.

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