Abstract
Taiwan is one of the countries with the highest motorcycle per capita globally, and motorcycle crashes are predominant among traffic crashes. This study examines the impact of coronavirus disease 2019 restrictions on motorcycle crashes. We analyzed the trend of motorcycle crashes in Taipei City from 2019 to 2020 using the dataset provided by the Department of Transportation, Taipei City Government, Taiwan. We found 47,108 and 51,441 motorcycle crashes in 2019 and 2020, involving 61,141 and 67,093 motorcycles, respectively. Mopeds had the highest risk in 2020, followed by heavy motorcycles [≥550 cubic capacity (cc)] and scooters compared to 2019. Food delivery motorcycle crashes increased for scooters (0.93% in 2019 to 3.45% in 2020, P < .0001) and heavy motorcycles (250 < cc < 550) (0.90% in 2019 to 3.38% in 2020, P < .0001). While fatalities remained under 1%, 30% to 51% of motorcyclists sustained injuries. Food delivery with scooters or heavy motorcycles (250 < cc < 550) was significantly associated with motorcyclist injuries and deaths. Compared with 2019, the adjusted odds ratios of motorcyclist injuries and deaths in 2020 were 1.43 (95% confidence interval = 1.05–1.94) for heavy motorcycles (≥550 cc) and 1.07 (95% confidence interval = 1.04–1.09) for scooters. This study shows that coronavirus disease 2019 restrictions was associated with elevated risks of crashes, injuries, and deaths among motorcyclists, reflecting the general preference for private transport over public transport. The popularity of food delivery services also contributed to increased motorcycle crashes.
Keywords: COVID-19, food delivery, motorcycle, Taipei, traffic crash
1. Introduction
The impact of the coronavirus disease 2019 (COVID-19) lockdown on traffic crashes is an important public health issue because of its association with reduced traffic mobility and an increased opportunity to speed on empty streets.[1–3] For instance, societal lockdown aimed at reducing the transmission of COVID-19 has been shown to decrease traffic crashes leading to nonserious or no injuries, but not those resulting in severe injuries or deaths.[3–8]
Motorcycle crashes are a leading contributor to hospitalization and may crowd out resources allocated for COVID-19.[9–11] Unlike most Western countries, motorcycles are the most popular means of transportation in Taiwan. Notably, Taiwan is one of the most motorcycle-dense countries globally, with 1.5 persons per motorcycle.[12,13] Most traffic crash fatalities have been attributable to motorcycle crashes.[12,14,15] Moreover, more than half of road traffic fatalities involved motorcycles, and traffic crashes were 26 times more likely to be fatal than those involving other vehicles.[15,16] In Taiwan, the COVID-19 outbreak in the first half of 2020 was associated with decreased incidence of trauma patients compared to the same period from 2015 to 2019.[17] Nevertheless, it remains unclear regarding the impact of COVID-19 restrictions on motorcycle crashes.
In December 2019, Taiwan started an early alert system that involved passenger screening of all flights from Wuhan, China. In early February 2020, Taiwan banned all travelers who went to China in the past 14 days and implemented mandatory home quarantine for travelers from COVID-19-affected countries. As early as late March 2020, Taiwan implemented further restrictions on inbound and outbound international travel, physical distancing, and mass gatherings. For instance, indoor gatherings >100 people and outdoor gatherings >500 people were all canceled starting on March 25, 2020. In addition, residents were required to keep a social distance of 1.5 m indoors and 1 m outdoors starting from April 1, 2020. As a result, as of December 21, 2020, Taiwan maintained 253 consecutive days of zero locally transmitted cases without nationwide lockdown. However, the impact of COVID-19 restrictions (such as social distancing and mass gatherings) on motorcycle crashes remained unclear. Therefore, this study aims to investigate the impact of COVID-19 on motorcycle crashes from 2019 to 2020 in Taipei City, Taiwan.
2. Materials and methods
We obtained the 2019 to 2020 dataset from the Department of Transportation, Taipei City Government, Taiwan, as part of the government research project titled “Analysis and Recommendations for the Increase in the Number of Traffic Accidents and Injured Persons in Taipei City” (「臺北市交通事故件數及受傷人數上升原因分析及建議」獎勵研究案). The dataset contains data on all traffic crashes in Taipei City reported to the Police and Department of Transportation, including the type of vehicles and motorcycles, date and time, location, type of crashes, and individuals involved (sex, age, severity of injury).
In the motorcycle licensing system in Taiwan, motorcycles are categorized into 4 types according to their engine exhaust volume, measured in cubic capacity (cc): mopeds (≤50 cc), scooters (50 < cc ≤ 250), and 2 types of heavy motorcycles (250 < cc < 550 and ≥550 cc).
Age was categorized into 4 groups: under 18, 18 to 40, 41 to 64, and over 65 years old. The injury severity of the motorcyclist was coded as A1 (death within 2 days), A2 (injured but not fatal), A3 (i.e., motorcycle damage without injuries or fatalities), and A4 (i.e., settlement without injuries or fatalities). The crashes were classified based on their locations into 3 categories: central, which includes Songshan (松山區), Xinyi (信義區), Daan (大安區), Zhongshan (中山區), Zhongzheng (中正區), Datong (大同區), and Wanhua (萬華區) districts; north, which includes Beitou (北投區) and Shilin (士林區) districts; and east, which includes Neihu (內湖區), Nangang (南港區), and Wenshan (文山區) districts (Fig. S1, Supplemental Digital Content, http://links.lww.com/MD/M251).
In statistical analyses, we first analyzed the number of motorcycle crashes by the number of motorcycles involved, as follows: (1) 1 motorcycle, (2) 2 motorcycles, (3) 3 motorcycles, (4) 1 motorcycle and 1 non-motorcycle, (5) 1 motorcycle and 2 non-motorcycles, and (6) 2 motorcycles and 1 non-motorcycle. Next, we examined the number of motorcycles in traffic crashes by motorcycle category. We estimated the proportion of motorcycles involved in traffic crashes by the number of motorcycles involved in traffic crashes divided by the number of registered motorcycles in Taipei City. The data on registered motorcycles in Taipei City were obtained from the Directorate General of Highways, Ministry of Transportation and Communications, Taiwan. We then compared these proportions of motorcycle crashes between 2019 and 2020 using the relative risks (RRs) with the 95% confidence intervals (CI), which are depicted in a forest plot. Additionally, we compared the proportion of motorcycle traffic crashes involved in food delivery between 2019 and 2020 using the chi-square test. The Pearson correlation coefficient (r) was used to examine the relationship between the number of motorcycles involved in traffic crashes and the jurisdiction area of the crash location.
When examining the motorcyclist’s injuries during the motorcycle crash, we used the absolute standardized mean difference (ASMD) to examine the balance of demographic variables (i.e., age, gender, and crash location) among motorcyclists involved in traffic crashes between 2019 and 2020. We calculated ASMD using the multivariate Mahalanobis distance method to generalize the standardized difference metric in the analysis of multinomial samples.[18] We considered ASMD > 0.1 statistically significant.[19] Logistic regression was used to examine the association of age groups, gender, jurisdiction district of the crash location, and food delivery services involved in motorcycle crashes. Adjusted odds ratios (ORs) were obtained by logistic regression, and 95% CIs were presented in a forest plot. We conducted all statistical analyses in SAS 9.4, with a significance level set at 0.05.
2.1. Ethics approval
The Institutional Review Board of the Chang Gung Medical Foundation approved and waived the requirement of informed consent for this study based on deidentified data (IRB number: 202101139B1).
3. Results
3.1. Motorcycle crashes
Taipei had 163,043 and 166,749 traffic crashes in 2019 and 2020, respectively. Of these, 47,108 (28.89%) in 2019 and 51,441 (30.85%) in 2020 involved motorcycles. Among traffic crashes involving motorcycles, almost half (45.6% in 2019, 46.3% in 2020) involved 2 motorcycles, followed by 1 motorcycle with another non-motorcycle (34.6%, 34.1%), 1 motorcycle with 2 non-motorcycles (7.8%, 7.3%), and 2 motorcycles with 1 non-motorcycle (5.1%, 4.9%). February had the lowest traffic crashes because the Chinese New Year was in February. During the Chinese New Year Holiday, many people visited their families and were not in Taipei, which probably led to decreased traffic crashes. When comparing the number of motorcycle traffic crashes in the same month between 2019 and 2020, the traffic crashes involving 2 motorcycles in 2020 were consistently and significantly higher than those in 2019 (P < .001) (Fig. 1).
Figure 1.
Number of traffic crashes involving motorcycles in Taipei City, Taiwan, 2019 to 2020.
In 2019 and 2020, 61,141 and 67,093 motorcycles were involved in traffic crashes, respectively. Note that the number of motorcycles involved in traffic crashes was higher than that of traffic crashes involving motorcycles because 1 traffic crash can involve more than 1 vehicle. Figure 2 displays the number of motorcycles in traffic crashes by motorcycle type from 2019 to 2020. The number of scooters and mopeds in traffic crashes increased after the COVID-19 outbreak in 2020. Since there were more registered scooters and mopeds, the proportion of crashes was highest among heavy (250 < cc < 550) motorcycles and scooters, followed by heavy (≥550 cc) motorcycles and mopeds. When comparing the crash proportion between 2019 and 2020, the RR of mopeds was the highest (1.61, 95% CI: 1.51–1.72), followed by heavy (≥550 cc) motorcycles (1.16, 95% CI: 1.01–1.33) and scooters (1.08, 95% CI: 1.07–1.10) (Fig. 3).
Figure 2.
Number of motorcycles in traffic crashes for the 4 types of motorcycles in Taipei City, Taiwan, 2019 to 2020.
Figure 3.
Relative risks of motorcycles in traffic crashes for the 4 types of motorcycles before and after the COVID-19 outbreak in Taipei City, Taiwan, 2019 to 2020.
There was no correlation between the total number of motorcycles in traffic crashes and the area in square kilometers of jurisdiction district of the crash location. The correlation coefficient (r) was 0.034 (P = .916) for 2019 and 0.086 (P = .791) for 2020 (Fig. S2, Supplemental Digital Content, http://links.lww.com/MD/M252).
The proportion of food delivery motorcycles involved in traffic crashes increased after the COVID-19 outbreak in 2020 compared to 2019. Such increase reached statistical significance for scooters (from 0.93% in 2019 to 3.45% in 2020, P < .0001) and heavy (250 < cc < 550) motorcycles (from 0.90% in 2019 to 3.38% in 2020, P < .0001) (Table 1).
Table 1.
The proportion of motorcycle crashes involving in food delivery before and after the COVID-19 outbreak in Taipei City, Taiwan, 2019 to 2020.
| Type of motorcycles | 2019 | 2020 | P-values | ||
|---|---|---|---|---|---|
| n | Related to food delivery | n | Related to food delivery | ||
| Mopeds (≤50 cc) | 1577 | 4 (0.25%) | 2263 | 14 (0.62%) | .103 |
| Scooters (50 < cc ≤ 250) | 58,310 | 541 (0.93%) | 63,349 | 2187 (3.45%) | <.0001 |
| Heavy (250 < cc < 550) | 889 | 8 (0.90%) | 1036 | 35 (3.38%) | <.0001 |
| Heavy (≥550 cc) | 365 | 1 (0.27%) | 445 | 3 (0.67%) | .761 |
P-values are obtained from the chi-square test.
cc = cubic capacity.
Table 2 compares the age, sex of the motorcyclist, and crash locations of the motorcycles involved in traffic crashes by the type of motorcycle between 2019 and 2020. The only significant differences between 2019 and 2020 were in crash locations for heavy (250 < cc < 550) motorcycles (ASMD = 0.1258), female motorcyclists for heavy (≥550 cc) motorcycles (ASMD = 0.1432), and young age for mopeds (ASMD = 0.3402).
Table 2.
Comparing the age, sex of the motorcyclists, and crash locations of the motorcycles involved in traffic crashes before and after the COVID-19 outbreak in Taipei City, Taiwan, 2019 to 2020.
| Mopeds (≤50 cc) | Scooters (50 < cc ≤ 250) | Heavy (250 < cc < 550) | Heavy (≥550 cc) | |||||
|---|---|---|---|---|---|---|---|---|
| 2019 (n = 1577) | 2020 (n = 2263) | 2019 (n = 58,310) | 2020 (n = 63,349) | 2019 (n = 889) | 2020 (n = 1036) | 2019 (n = 365) | 2020 (n = 445) | |
| Age (yrs) | 41.19 ± 20.39 | 35.22 ± 19.24 | 33.58 ± 15.9 | 33.99 ± 15.81 | 34.18 ± 11.33 | 34.14 ± 11.79 | 35.74 ± 12.79 | 36.07 ± 13.07 |
| <18 | 90 (5.71%) | 147 (6.5%) | 2479 (4.25%) | 2461 (3.88%) | 12 (1.35%) | 22 (2.12%) | 5 (1.37%) | 4 (0.9%) |
| 18–40 | 694 (44.01%) | 1359 (60.05%) | 39,138 (67.12%) | 42,193 (66.6%) | 638 (71.77%) | 714 (68.92%) | 232 (63.56%) | 285 (64.04%) |
| 41–64 | 577 (36.59%) | 539 (23.82%) | 14,035 (24.07%) | 15,745 (24.85%) | 232 (26.1%) | 292 (28.19%) | 122 (33.42%) | 147 (33.03%) |
| ≥65 | 216 (13.7%) | 218 (9.63%) | 2658 (4.56%) | 2950 (4.66%) | 7 (0.79%) | 8 (0.77%) | 6 (1.64%) | 9 (2.02%) |
| ASMD* | 0.3402* | 0.0513 | 0.0972 | 0 | ||||
| Female | 852 (54.03%) | 1148 (50.73%) | 15,665 (26.87%) | 17,346 (27.38%) | 26 (2.92%) | 33 (3.19%) | 4 (1.1%) | 13 (2.92%) |
| ASMD* | 0.0619 | 0.0548 | 0.0824 | 0.1432* | ||||
| Location of the motorcycle crash† | ||||||||
| Central | 957 (60.68%) | 1444 (63.81%) | 36,527 (62.64%) | 38,942 (61.47%) | 556 (62.54%) | 603 (58.2%) | 206 (56.44%) | 244 (54.83%) |
| East | 350 (22.19%) | 496 (21.92%) | 11,307 (19.39%) | 12,870 (20.32%) | 172 (19.35%) | 246 (23.75%) | 76 (20.82%) | 98 (22.02%) |
| North | 270 (17.12%) | 323 (14.27%) | 10,476 (17.97%) | 11,537 (18.21%) | 161 (18.11%) | 187 (18.05%) | 83 (22.74%) | 103 (23.15%) |
| ASMD* | 0.0852 | 0.0259 | 0.1258* | 0.0254 | ||||
cc = cubic capacity.
Absolute standardized mean difference (ASMD) > 0.1 is considered statistically significant.
Central [including Songshan (松山區), Xinyi (信義區), Daan (大安區), Zhongshan (中山區), Zhongzheng (中正區), Datong (大同區), and Wanhua (萬華區) districts]; north [including Beitou (北投區) and Shilin (士林區) districts], and east [including Neihu (內湖區), Nangang (南港區), and Wenshan (文山區) districts].
3.2. Injuries of motorcyclists
Regarding the severity of motorcyclist injuries due to motorcycle crashes, <1% of the motorcyclists died within 2 days (=A1), 30–51% were injured without mortality (=A2), 5–15% were damaged motorcycles without motorcyclist injuries or mortalities (=A3), and 39–59% settled without injuries or mortalities (=A4). The mortality among motorcyclists did not vary with the type of motorcycle, with 6 (0.156%) mortalities involving mopeds, 116 (0.095%) involving scooters, 7 (0.364%) involving heavy (250 < cc < 550) motorcycles, and 6 (0.325%) involving heavy (≥550 cc) motorcycles. Hence, we defined the injury group (i.e., injuries and mortalities) by combining A1 and A2, and the noninjury group was defined by combining A3 and A4. The percentage of motorcyclist injuries (including <1% of deaths, i.e., A1 + A2) decreased from 50.47% in mopeds to 30.62% in heavy (≥550 cc) motorcycles (Table 3).
Table 3.
Univariate analysis of factors associated with injuries (including <1% of deaths) of motorcyclists involved in traffic crashes before and after the COVID-19 outbreak in Taipei City, Taiwan, 2019 to 2020.
| Mopeds (≤50 cc) | Scooters (50 < cc ≤ 250) | Heavy (250 < cc < 550) | Heavy (≥550 cc) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | Injuries | OR (95% CI) | n | Injuries | OR (95% CI) | n | Injuries | OR (95% CI) | n | Injuries | OR (95% CI) | |
| Total | 3840 | 1938 (50.47%) | 121,659 | 55,954 (45.99%) | 1925 | 634 (32.94%) | 810 | 248 (30.62%) | ||||
| Year | ||||||||||||
| 2019 | 1577 | 810 (51.36%) | Ref | 58,310 | 26,291 (45.09%) | Ref | 889 | 298 (33.52%) | Ref | 365 | 96 (26.3%) | Ref |
| 2020 | 2263 | 1128 (49.85%) | 0.94 (0.83–1.07) | 63,349 | 29,663 (46.82%) | 1.07 (1.05–1.1) | 1036 | 336 (32.43%) | 0.95 (0.79–1.15) | 445 | 152 (34.16%) | 1.45 (1.07–1.97) |
| Age (yrs) | ||||||||||||
| <18 | 237 | 57 (24.05%) | 0.36 (0.27–0.49) | 4940 | 1147 (23.22%) | 0.36 (0.34–0.39) | 34 | 3 (8.82%) | 0.2 (0.06–0.66) | 9 | 1 (11.11%) | 0.26 (0.03–2.13) |
| 18–40 | 2053 | 958 (46.66%) | Ref | 81,331 | 36,971 (45.46%) | Ref | 1352 | 441 (32.62%) | Ref | 517 | 166 (32.11%) | Ref |
| 41–64 | 1116 | 654 (58.6%) | 1.62 (1.4–1.87) | 29,780 | 14,708 (49.39%) | 1.17 (1.14–1.2) | 524 | 181 (34.54%) | 1.09 (0.88–1.35) | 269 | 76 (28.25%) | 0.83 (0.6–1.15) |
| ≥65 | 434 | 269 (61.98%) | 1.86 (1.51–2.3) | 5608 | 3128 (55.78%) | 1.51 (1.43–1.6) | 15 | 9 (60%) | 3.1 (1.1–8.76) | 15 | 5 (33.33%) | 1.06 (0.36–3.14) |
| Sex | ||||||||||||
| Male | 1634 | 780 (47.74%) | Ref | 84,440 | 38,759 (45.9%) | Ref | 1831 | 617 (33.7%) | Ref | 784 | 239 (30.48%) | Ref |
| Female | 2000 | 1118 (55.9%) | 1.39 (1.22–1.58) | 33,011 | 16,486 (49.94%) | 1.18 (1.15–1.21) | 59 | 15 (25.42%) | 0.67 (0.37–1.21) | 17 | 8 (47.06%) | 2.03 (0.77–5.32) |
| Missing | 206 | 40 (19.42%) | 0.26 (0.18–0.38) | 4208 | 709 (16.85%) | 0.24 (0.22–0.26) | 35 | 2 (5.71%) | 0.12 (0.03–0.5) | 9 | 1 (11.11%) | 0.29 (0.04–2.29) |
| Location of the motorcycle crash* | ||||||||||||
| Central | 2401 | 1184 (49.31%) | Ref | 75,469 | 33,859 (44.86%) | Ref | 1159 | 366 (31.58%) | Ref | 450 | 138 (30.67%) | Ref |
| North | 593 | 306 (51.6%) | 1.1 (0.92–1.31) | 22,013 | 10,177 (46.23%) | 1.06 (1.03–1.09) | 348 | 123 (35.34%) | 1.18 (0.92–1.52) | 186 | 56 (30.11%) | 0.97 (0.67–1.41) |
| East | 846 | 448 (52.96%) | 1.16 (0.99–1.35) | 24,177 | 11,918 (49.29%) | 1.19 (1.16–1.23) | 418 | 145 (34.69%) | 1.15 (0.91–1.46) | 174 | 54 (31.03%) | 1.02 (0.7–1.49) |
| Food delivery | ||||||||||||
| Yes | 18 | 9 (50%) | 0.98 (0.39–2.48) | 2728 | 1504 (55.13%) | 1.46 (1.35–1.57) | 43 | 25 (58.14%) | 2.9 (1.57–5.36) | 4 | 1 (25%) | 0.75 (0.08–7.29) |
| No | 3822 | 1929 (50.47%) | Ref | 118,931 | 54,450 (45.78%) | Ref | 1882 | 609 (32.36%) | Ref | 806 | 247 (30.65%) | Ref |
cc = cubic capacity, CI = confidence interval, OR = odds ratio, ref = reference.
Central [including Songshan (松山區), Xinyi (信義區), Daan (大安區), Zhongshan (中山區), Zhongzheng (中正區), Datong (大同區), and Wanhua (萬華區) districts]; north [including Beitou (北投區) and Shilin (士林區) districts]; and east [including Neihu (內湖區), Nangang (南港區), and Wenshan (文山區) districts].
Table 3 shows the univariate factors associated with motorcyclist injuries (including <1% of deaths, i.e., A1 + A2) in traffic crashes before and after the COVID-19 outbreak. Compared to those between 18 to 40 years old (reference), individuals under 18 years old were less likely to sustain injuries during motorcycle crashes, with statistical significance observed in mopeds, scooters, and heavy (250 < cc < 550) motorcycles with ORs ranging from 0.2 to 0.36. On the other hand, those between 41 to 64 years old were more likely to have injuries during motorcycle crashes and reached statistical significance in mopeds (OR = 1.62, 95% CI = 1.40–1.87) and scooters (OR = 1.17, 95% CI = 1.14–1.20). Similarly, those ≥65 years old were more likely to have injuries during motorcycle crashes with higher ORs and reached statistical significance in mopeds (OR = 1.86, 95% CI = 1.51–2.30), scooters (OR = 1.51, 95% CI = 1.43–1.60), and heavy (250 < cc < 550) (OR = 3.1, 95% CI = 1.10–8.76) motorcycles. Female motorcyclists were more likely to have injuries during motorcycle crashes than male motorcyclists, and statistical significance was reached in mopeds (OR = 1.39, 95% CI = 1.22–1.58) and scooters (OR = 1.18, 95% CI = 1.15–1.21). Scooter crashes occurring in the North (OR = 1.06, 95% CI = 1.03–1.09) and East (OR = 1.19, 95% CI = 1.16–1.23) areas were significantly associated with increased risk of injuries. Furthermore, there was a significant association between food delivery on scooters (OR = 1.46, 95% CI = 1.35–1.57) or heavy (250 < cc < 550) (OR = 2.9, 95% CI = 1.57–5.36) motorcycles and motorcyclist injuries in traffic crashes (Table 3).
In Figure 4, after adjusting for age, gender, jurisdiction district of the crash location, and food delivery involvement, the adjusted ORs of motorcyclist injuries in 2020 were compared with 2019 for all types of motorcycles. The adjusted ORs for heavy (≥550 cc) motorcycles and scooters were 1.43 (95% CI = 1.05–1.94) and 1.07 (95% CI = 1.04–1.09), respectively.
Figure 4.
Adjusted odds ratios of motorcyclist injuries in motorcycle crashes in 2020 before and after the COVID-19 outbreak in Taipei City, Taiwan, 2019 to 2020.
4. Discussion
4.1. Main findings
This study demonstrates the impact of the COVID-19 restrictions on motorcycle crashes in Taipei City, Taiwan. Compared to 2019, after the COVID-19 outbreak in 2020, mopeds had the highest risk of being involved in traffic crashes, followed by heavy (≥550 cc) motorcycles and scooters. In both scooters and heavy (250 < cc < 550) motorcycles, the proportion of crashes, injuries and deaths in food delivery increased significantly after the COVID-19 outbreak in 2020 compared to 2019. Furthermore, both heavy (≥550 cc) motorcycles and scooters had a higher odds ratio for motorcyclist injuries and deaths in 2020 compared to 2019, after adjusting for age, gender, jurisdiction district of the crash location, and food delivery involvement.
4.2. Implications of motorcycle crashes and injuries
This study found that approximately one-third of all traffic crashes involved motorcycles. Almost half of these involved collisions between 2 motorcycles, followed by collisions between a motorcycle and a non-motorcycle vehicle. These high proportions of traffic crashes involving motorcycles or 2 motorcycle collisions were likely attributable to Taiwan’s high concentration of motorcycles and their preference as a mode of transportation.[20] Although scooters were involved in most motorcycle crashes, the highest proportion of crashes (around 8%) involved heavy (250 < cc < 550) motorcycles, followed by scooters (around 7%). Possible reasons for the high traffic crashes involving these 2 types of motorcycles in Taiwan are their predominance in the country and their higher involvement in crashes compared to other types.[21,22] In Taiwan, there is no mandatory training before taking the licensing exam for motorcycles with engines <250 cc. To obtain a license, these riders are legally required to pass a written examination on traffic regulations and a closed field driving test. However, without mandatory road safety classes, we suspect their ability to recognize risks, prevent crashes and deter traffic violations may be inadequate, leading to increased risk of crash involvement.
This study also shows that the proportion of motorcyclist injuries in traffic crashes decreases as engine displacement capacity increases. In Taiwan, heavy motorcycles (i.e., those with engine capacity above 250 cc) are usually ridden by experienced riders who must have at least 1 year of experience with a motorcycle license and certified training.[23] On the other hand, novice motorcyclists are typically riders of mopeds and scooters, with some of them underaged or unlicensed, leading to a higher risk of traffic crashes and injuries.[13,24,25] These reasons may result in the proportion of motorcyclist injuries decreasing with higher engine displacement capacity.
Moreover, we found that the motorcyclist fatality in traffic crashes was <1% and did not vary with the type of motorcycle. However, the factors associated with severity and mortality in motorcycle crashes were not considered, including crash location, time of the crash, collision type, and the type and number of vehicles involved.[25,26] In Taiwan, motorcyclist fatality is associated with collisions with motorcycles more than 250 cc, being unlicensed motorcyclists, speed driving, and right-of-way violations with positive blood alcohol concentration values.[25]
Compared to riders between 18 and 40, those under 18 who rode mopeds, scooters, or heavy (250 < cc < 550) motorcycles had lower odds of sustaining injuries or fatalities in traffic crashes. We suspect this might be because underage, younger riders in Taiwan are usually accompanied by older, more experienced riders (such as parents). It was also possible that the smaller and lighter motorcycles could contribute to their lower odds of injuries and fatalities because they are more accessible for young riders to maneuver. Additional research would be required to confirm any of these speculations.
4.3. Impact of COVID-19 restrictions
Regarding the impact of COVID-19 restrictions on motorcycle crashes in Taiwan, we found that the proportion of motorcycles involved in traffic crashes increased after the COVID-19 outbreak, and the RR was the highest for mopeds (1.61, 95% CI: 1.51–1.72), followed by heavy motorcycles (≥550 cc) (1.16, 95% CI: 1.01–1.33) and scooters (1.08, 95% CI: 1.07–1.10). We suspect this might be because of the avoidance of public transport and increasing preference for private transport (such as motorcycles) due to perceived health risks triggered by the pandemic, consistent with previous findings.[27] Notably, in Taiwan, after the outbreak of COVID-19, every new confirmed case of COVID-19 was shown to reduce 1.43% of metro use.[28] These findings suggest that COVID-19 increased awareness of the potential health risk of mass gatherings in public transport, indirectly increasing the proportion of motorcycles involved in traffic crashes in 2020.
The rising trend of motorcycles in traffic crashes might also be partially due to the increased usage of motorcycles for food delivery during the COVID-19 pandemic. When analyzing the motorcycle crashes involved in food delivery, scooters and heavy (250 < cc < 550) motorcycles showed a significant increase in 2020 compared to 2019. This was reasonable because heavy (250 < cc < 550) motorcycles and scooters are commonly used for food delivery in Taiwan. Unfortunately, we could not find any statistics about Taipei’s registered motorcycles for food delivery. Because of the restrictions on gatherings and social distancing after the outbreak of COVID-19 in 2020, the utilization of food delivery services increased by 20 to 30%.[29] Moreover, other aspects of online food shopping, such as grocery delivery and agri-food products, have also increased in demand.[29] These reasons contributed to the increased number of motorcycles involved in traffic crashes. The finding that motorcycle traffic crashes increased after the COVID-19 outbreak highlights the urgent need to promote driving safety for motorcycle drivers, especially those involved in essential services such as food and grocery delivery. This is particularly true for regions with a high density of hotels and restaurants, where food delivery motorcycle crashes are likely to occur.[30] Food delivery companies and riders must prioritize safety by providing adequate training, ensuring proper equipment and protective gear, and promoting safe riding practices.
Mopeds had the most significant relative risk of traffic crashes among all motorcycle types in 2020 compared with 2019. We suspect this might have been related to limited visibility, stability, and speed compared other motorcycle types. Another possible reason might be that those operating mopeds were often inexperienced in safely navigating them on the road.[13,24] Furthermore, this study found an elevated risk of motorcyclist injuries (including <1% of deaths) during traffic crashes in 2020 when compared with those of 2019 in heavy (≥550 cc) motorcycles and scooters, after adjusting for age, sex, district, and food delivery involvement in the multivariate analysis. In Taiwan, being a rider of heavy (≥550 cc) motorcycles is a risk factor for mortality in motorcycle crashes.[15] Additionally, scooters are more likely to be novice riders, neglecting safety checks and committing risky driving behaviors.[13] Measures against COVID-19 such as physical distancing, school closure, and travel restrictions may lead to reduced traffic congestion and increased speeding.[3] These reasons might have increased the fatality risk among heavy (≥550 cc) and scooters.
Interestingly, in mopeds, the number of female riders involved in crashes was approximately one-half of the total in 2019 and 2020. In contrast, the motorcyclists in other types of motorcycle crashes were predominantly male. Nevertheless, there is a lack of consensus regarding the role of sex on traffic crashes and injuries. This study found that female motorcyclists had greater odds of injuries and fatalities in mopeds and scooters. In contrast, it was found that the mortality of female motorcyclists was lower than that of male motorcyclists in Taiwan after adjusting confounders such as helmet use and drunk driving.[31] We suspect this might be because our study does not include confounders such as the type of helmet use, alcohol intoxication, and the specific vehicle type involved in the collision.
4.4. Strengths and limitations
The strength of this study is analyzing the proportions, not the number, of motorcycles involved in traffic crashes because proportions better reflect whether a vehicle type is more likely to be involved in traffic crashes than other types. However, there are several limitations in our study. First, mild cases of motorcycle crashes, such as noninjury and minor injury incidents, might go unreported to the Police or the Department of Transportation. Additionally, single-motorcycle crashes are susceptible to underreporting in police-reported crash datasets. However, we believe their impact on our analysis is minimal due to the low proportion (<4%) of single-motorcycle crashes among crashes involving motorcycles. Furthermore, in Taiwan, all motorcycles must have insurance by law, providing compensation for injuries to riders and requiring incident reporting to the police for claims. Therefore, the issue of underreporting is unlikely to be a major concern. Second, our analysis is limited to Taipei City. Hence, extrapolation of our results to other areas in Taiwan or other countries needs to be cautious because our analysis might not be able to capture sociodemographic differences across countries. Third, we could not obtain data on the total number of motorcycles and each type involved in food delivery. Therefore, we could not estimate the rate of motorcycle crashes among motorcycles involved in food delivery. Fourth, our analysis failed to consider certain confounding factors that could impact injuries and fatalities, such as helmet usage, alcohol intoxication, and the specific type of vehicle involved in the collision. Finally, our dataset does not include the total number of motorcycles involved in each type of food delivery service (such as Foodpanda, UberEat, Lalamove, Dliveroo, Foodomo, Yo-woo, and others). Thus, we could not estimate the rate of motorcycle crashes by food delivery service type.
5. Conclusions
In conclusion, this study demonstrates that the COVID-19 restrictions was associated with higher odds of injuries and fatalities in motorcycle crashes. The COVID-19 restrictions was associated with an increased proportion of motorcycles involved in traffic crashes, which may indicate the widespread preference for private transportation over public transportation. Furthermore, crashes involving motorcycles used for food delivery have increased, likely due to the growing popularity of point-to-point food delivery services in response to COVID-19 restrictions. These observations underscore the necessity for implementing measures to mitigate the risk of motorcycle crashes during pandemics.
Acknowledgments
We thank the help from the Department of Transportation, Taipei City Government, Taiwan.
Author contributions
Conceptualization: Lai-Chu See.
Data curation: Wei-Sheng Peng, Wei-Min Chen.
Formal analysis: Wing Hin Stanford Siu, Wei-Sheng Peng, Wei-Min Chen.
Funding acquisition: Lai-Chu See.
Investigation: Wing Hin Stanford Siu, Wei-Sheng Peng, Wei-Min Chen, Lai-Chu See.
Methodology: Wing Hin Stanford Siu, Wei-Sheng Peng, Wei-Min Chen, Lai-Chu See.
Project administration: Lai-Chu See.
Resources: Lai-Chu See.
Supervision: Lai-Chu See.
Validation: Wei-Min Chen.
Visualization: Wing Hin Stanford Siu, Wei-Sheng Peng, Wei-Min Chen.
Writing – original draft: Wing Hin Stanford Siu.
Writing – review & editing: Wing Hin Stanford Siu, Lai-Chu See.
Supplementary Material
Abbreviations:
- ASMD
- absolute standardized mean difference
- cc
- cubic capacity
- CI
- confidence interval
- COVID-19
- coronavirus disease 2019
- OR
- odds ratio
- RR
- relative risk
This work was supported by the Chang Gung Memorial Hospital under Grants CMRPD1M0011, CMRPD1M0012, and CMRPD1M0013.
Supplemental Digital Content is available for this article.
The authors have no conflicts of interest to disclose.
The Institutional Review Board (IRB) of the Chang Gung Medical Foundation approved and waived the requirement of informed consent for this study based on deidentified data (IRB number: 202101139B1).
The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.
How to cite this article: Siu WHS, Peng W-S, Chen W-M, See L-C. The impact of COVID-19 restrictions on motorcycle crashes in Taiwan. Medicine 2024;103:16(e37901).
Contributor Information
Wing Hin Stanford Siu, Email: siustanford@gmail.com.
Wei-Sheng Peng, Email: wade9933@gmail.com.
Wei-Min Chen, Email: weiming@mail.cgu.edu.tw.
References
- [1].Inada H, Ashraf L, Campbell S. COVID-19 lockdown and fatal motor vehicle collisions due to speed-related traffic violations in Japan: a time-series study. Inj Prev. 2021;27:98–100. [DOI] [PubMed] [Google Scholar]
- [2].Vingilis E, Beirness D, Boase P, et al. Coronavirus disease 2019: what could be the effects on road safety? Accid Anal Prev. 2020;144:105687. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Yasin YJ, Grivna M, Abu-Zidan FM. Global impact of COVID-19 pandemic on road traffic collisions. World J Emerg Surg. 2021;16:51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Qureshi AI, Huang W, Khan S, et al. Mandated societal lockdown and road traffic accidents. Accid Anal Prev. 2020;146:105747. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [5].Sutherland M, McKenney M, Elkbuli A. Vehicle related injury patterns during the COVID-19 pandemic: what has changed? Am J Emerg Med. 2020;38:1710–4. [DOI] [PubMed] [Google Scholar]
- [6].Sedain B, Pant PR. Road traffic injuries in Nepal during COVID-19 lockdown. F1000Res. 2020;9:1209. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Alhajyaseen WKM, Almukdad A, Hussain Q, et al. Road safety status during COVID-19 pandemic: exploring public and road safety expert’s opinions. Int J Inj Contr Saf Promot. 2022;29:135–51. [DOI] [PubMed] [Google Scholar]
- [8].Paramasivan K, Subburaj R, Sharma VM, et al. Relationship between mobility and road traffic injuries during COVID-19 pandemic—the role of attendant factors. PLoS One. 2022;17:e0268190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Pai CW, Lin HY, Tsai SH, et al. Comparison of traffic-injury related hospitalisation between bicyclists and motorcyclists in Taiwan. PLoS One. 2018;13:e0191221. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Liang CC, Liu HT, Rau CS, et al. Motorcycle-related hospitalization of adolescents in a Level I trauma center in southern Taiwan: a cross-sectional study. BMC Pediatr. 2015;15:105. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Hsieh CH, Liu HT, Hsu SY, et al. Motorcycle-related hospitalizations of the elderly. Biomed J. 2017;40:121–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Lam C, Lin MR, Chu SF, et al. The effect of various types of motorcycle helmets on cervical spine injury in head injury patients: a multicenter study in Taiwan. Biomed Res Int. 2015;2015:487985. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Yu S, Tsai WD. The effects of road safety education on the occurrence of motorcycle violations and accidents for novice riders: an analysis of population-based data. Accid Anal Prev. 2021;163:106457. [DOI] [PubMed] [Google Scholar]
- [14].Hsu IL, Li CY, Chu DC, et al. An epidemiological analysis of head injuries in Taiwan. Int J Environ Res Public Health. 2018;15:2457. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Jou RC, Yeh TH, Chen RS. Risk factors in motorcyclist fatalities in Taiwan. Traffic Inj Prev. 2012;13:155–62. [DOI] [PubMed] [Google Scholar]
- [16].Chiu PW, Lin CH, Wu CL, et al. Ambulance traffic accidents in Taiwan. J Formos Med Assoc. 2018;117:283–91. [DOI] [PubMed] [Google Scholar]
- [17].Kuo LW, Fu CY, Liao CA, et al. How much could a low COVID-19 pandemic change the injury trends? A single-institute, retrospective cohort study. BMJ Open. 2021;11:e046405. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Yang D, Dalton JE. A unified approach to measuring the effect size between two groups using SAS. SAS Global Forum 2012. 2012; paper 335. Available at: http://support.sas.com/resources/papers/proceedings12/335-2012.pdf [access date February 1, 2024].
- [19].Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat – Simul Comput. 2009;38:1228–34. [Google Scholar]
- [20].Yang CS, Chen SC, Yang YC, et al. Epidemiology and patterns of facial fractures due to road traffic accidents in Taiwan: a 15-year retrospective study. Traffic Inj Prev. 2017;18:724–9. [DOI] [PubMed] [Google Scholar]
- [21].Lam C, Pai CW, Chuang CC, et al. Rider factors associated with severe injury after a light motorcycle crash: a multicentre study in an emerging economy setting. PLoS One. 2019;14:e0219132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Chen PL, Pai CW. Evaluation of injuries sustained by motorcyclists in approach-turn crashes in Taiwan. Accid Anal Prev. 2019;124:33–9. [DOI] [PubMed] [Google Scholar]
- [23].Motor Vehicle Driver Information Service, Directorate General of Highways, Taiwan. Driver’s license test for motor vehicles and motorcycles general regulations. Available at: https://www.mvdis.gov.tw/m3-emv-eng/public_en/about_02_01En [access date July 20, 2023].
- [24].Jou RC, Chao MC. An analysis of the novice motorcyclist crashes in Taiwan. Traffic Inj Prev. 2022;23:140–5. [DOI] [PubMed] [Google Scholar]
- [25].Wang MH. Investigating the difference in factors contributing to the likelihood of motorcyclist fatalities in single motorcycle and multiple vehicle crashes. Int J Environ Res Public Health. 2022;19:8411. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Wahab L, Jiang H. Severity prediction of motorcycle crashes with machine learning methods. Int J Crashworthiness. 2020;25:485–92. [Google Scholar]
- [27].Dingil AE, Esztergár-Kiss D. The influence of the Covid-19 pandemic on mobility patterns: the first wave’s results. Transp Lett. 2021;13:434–46. [Google Scholar]
- [28].Chang HH, Lee B, Yang FA, et al. Does COVID-19 affect metro use in Taipei? J Transp Geogr. 2021;91:102954. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29].Chang H-H, Meyerhoefer CD. COVID-19 and the demand for online food shopping services: empirical evidence from Taiwan. Amer J Agr Econ. 2020;103:448–65. [Google Scholar]
- [30].Lin P-C, Shen C-W, Wang J, et al. Spatial analysis of accidents involving food delivery motorcycles in Taiwan. Transp Plan Technol. 2022;45:335–57. [Google Scholar]
- [31].Hsieh CH, Hsu SY, Hsieh HY, et al. Differences between the sexes in motorcycle-related injuries and fatalities at a Taiwanese level I trauma center. Biomed J. 2017;40:113–20. [DOI] [PMC free article] [PubMed] [Google Scholar]