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MAIDS - Motorcycle Accident In Depth Study

Findings on general accident characteristics
  • There were 103 cases involving a fatality of either the rider or the passenger.
  • L1 vehicles were over-represented in the accident sample when compared with the exposure sample.
  • More L1 vehicles were involved in accidents which took place in an urban area than L3 vehicles. (85.9% v. 62%).
  • 54.3% of the PTW accidents took place at an intersection.
  • Passenger cars were the most frequent collision partner (60%).
  • 72% of the accidents took place in urban areas.
  • A PTW was more likely to collide with a passenger car in an urban area than in a rural area. (64.1% v. 46.7%).
  • Due to the absence of comparable exposure data, it was not possible to determine if any month, day of the week or time of day was a risk factor.
Index of Tables & figures of this page:
To get to the corresponding sections, please click on the  title

A complete summary of all cases collected by each research team is presented in Table 3.1.

Table 3.1: Total number of cases collected  

 

Cases

Controls

Total

University of Pavia (Italy)

200

200

400

TNO (Netherlands)

200

200

400

REGES (Spain)

121

123

244

ARU-MUH (Germany)

250

250

500

CEESAR (France)

150

150

300

Total       

921

923

1844

      There were 103 accident cases (11.2%) within the MAIDS database that involved a fatality of either the rider or the passenger (Table 3.2). A fatality was defined as any death within 30 days of the accident. The number of fatal cases collected by the German research team was the result of their focus upon more seriously injured riders.

Table 3.2: Number of fatal cases

 

Fatal

Not fatal

Total

University of Pavia (Italy)

11

189

200

TNO (Netherlands)

15

185

200

REGES (Spain)

12

109

121

ARU-MUH (Germany)

49

201

250

CEESAR (France)

16

134

150

Total

103

818

921

Note: There were multiple fatalities in two cases.

 

Table 3.3 gives the distribution of the PTW legal categories for both the accident data and the exposure or case control data. The data shows that the majority of vehicles were L3 vehicles; however, when compared to the exposure data, they were neither over- nor under-represented in the accident data. Therefore, there is no increased risk in the operation of an L3 vehicle when compared to other PTW legal categories. Similar findings can be reported for the mofa legal categories. A chi-square test of the relationship between the L1 accident data and the L1 exposure data shows that the L1 vehicles are over-represented in the MAIDS database (p < .05).

Table 3.3: PTW legal category

  

Accident data

Exposure data

 

Frequency

Percent

Frequency

Percent

L1 vehicle - mofa

28

3.0

49

5.3

L1 vehicle - other

370

40.2

324

35.1

L3 vehicle

523

56.8

550

59.6

Total

921

100.0

923

100.0

 

Table 3.4 presents the PTW collision partners for all cases collected during this research project. Passenger cars were the most frequent collision partner (60.0%), followed by the roadway (9.0%). The high percentage of passenger car, truck, sport utility vehicle (SUV) and bus collision partners is not unusual since most of the accidents took place in an urban environment where PTWs must share the roadway with other motorized vehicles. This distribution only represents the object with which the PTW ultimately collided, and does not suggest accident causation, since there were many cases in which the PTW rider successfully avoided colliding with a car, PTW, truck, etc., but instead impacted the roadway or some other fixed object. The PTW collision partner is not necessarily the OV. Single vehicle accidents (e.g., running off the roadway) are also included in this distribution.

Table 3.4: PTW collision partner

 

Frequency

Percent

Passenger car

553

60.0

Another PTW

64

6.9

Truck/SUV/bus/

77

8.4

Bicycle/pedestrian

19

2.1

Fixed object

74

8.0

Roadway

83

9.0

Parked vehicle

25

2.7

Animal

3

0.3

Other

23

2.5

Total

921

100.0

      Table 3.5 indicates that the majority of the accidents collected during this study involved a collision with an OV (80.2%). One hundred and forty-three of the cases (15.5%) involved only the PTW and PTW rider (e.g., a single vehicle accident).

Table 3.5: Number of OVs involved in the accident

 

Frequency

Percent

None (single vehicle accident)

143

15.5

One

738

80.2

Two

36

3.9

Three

4

0.4

Total

921

100.0

Table 3.6 indicates that the majority of accidents involved only the PTW operator and that 8.6% of all cases involved a PTW passenger. There were no cases collected with more than one PTW passenger. Table 3.7 shows the number of fatal PTW accidents in which a passenger was present. Please note that the value presented in Table 3.7 does not represent the number of passengers who were killed, but rather the number of cases in which there was a fatality involved in the crash. There were only five cases in which the passenger was reported as the fatality.

       Table 3.6: Number of passengers on PTW

 

Frequency

Percent

None

842

91.4

One

79

8.6

Total

921

100.0

 

Table 3.7: Number of passengers on PTW (fatal accidents only)

 

Frequency

Percent

None

90

87.4

One

13

12.6

Total

103

100.0

 

Table 3.8 indicates that approximately three-quarters of all accidents occurred within an urban area. Approximately three quarters of all collected accidents took place in an urban area. An urban area was defined as a built up area with a population of 5,000 or more inhabitants. Similarly, a community was defined as rural if its population density is less than 150 people per square kilometre (OECD, 2001).

When distributed according to PTW legal category, the data shows that more of the L1 vehicles were involved in accidents which took place in an urban area than L3 vehicles. The distribution of accidents is directly related to the demographic characteristics of the sampling area for each research team.

 Table 3.8: Accident scene, type of area

  L1 vehicles   L3 vehicles   Total  

Frequency

Percent of L1

Frequency

Percent of L3

Frequency

Percent

urban

342

85.9

324

62.0

666

72.3

rural

43

10.8

186

35.6

229

24.9

other

13

3.3

13

2.4

26

2.8

Total

398

100.0

523

100.0

921

100.0

 

 The distribution of the PTW collision partners by type of area is presented in Figure 3.1. The data indicates that in an urban area, the most frequent collision partner is a passenger car. This finding was certainly expected, as was the finding that the majority of truck/SUV/bus collisions occur in an urban area because that is where most vehicles circulate.

            In a rural area, PTW to passenger car collisions decrease (64.1% to 46.7%) while PTW to PTW collisions increase (6.3% to 9.6%). There is an increase in the number of collisions between a PTW and a fixed object (4.2% to 19.7%) as well as collisions with the roadway (7.7% to 12.2%). The data shown in Figure 3.1 may be found in the datatables page, Table C.1.

Figure 3.1: PTW collision partner by type of area

img1.gif

 

The MAIDS data indicates that half of all PTW accidents were found to take place at an intersection (Table 3.9). An intersection was defined as any on-grade crossing or intersection of two public roadways (OECD, 2001).

Table 3.9: Accident location

 

Frequency

Percent

Intersection

500

54.3

Non-intersection

358

38.9

Other

63

6.8

Total

921

100.0

 

            Figure 3.2 shows the time of day in which both the fatal and non-fatal accidents occurred. The data indicates that most accidents occurred between 17h01 and 18h00, with the most accidents taking place from 14h01 to 20h00. It is not possible to state whether a given time of day is "more dangerous" than any other time since PTW rider exposure data (i.e., number of riders on the roadway at all hours of the day) is not available. Most of the fatal accidents occurred between 12h01 and 22h00, with the most frequent number of cases taking place between 19h01 and 20h00. The data shown in Figure 3.2 may be found in datatables page, Table C.2.

 Figure 3.2: Time of day accident occurred

img8.gif

 

  Table 3.10 shows that the most accidents took place on Tuesday (159 cases, 17.3%), followed closely by Monday (152 cases, 16.5%). Since the exposure data was not collected at accident-related times (i.e., they were collected during petrol station operating hours), it was not possible to determine if one day of the week was more dangerous for riding a PTW than any other day of the week.

Table 3.10: Day of week accident occurred

 

Frequency

Percent

Monday

152

16.5

Tuesday

159

17.3

Wednesday

134

14.5

Thursday

140

15.2

Friday

139

15.1

Saturday

76

8.3

Sunday

121

13.1

Total

921

100.0

          

Figure 3.3 indicates that PTW accidents were more frequent during the spring and summer months, decreasing during the month of August. The frequency of accidents also decreases after the month of September, probably due to decreases in temperature and presence of adverse riding conditions in the northern parts of Europe.

Because the exposure data was not collected at accident-related times, it is not possible to determine if any given month is more dangerous than any other. Therefore, this data is presented for information on frequency only. The data shown in Figure 3.3 may be found in datatables page, Table C.3.

A general accident typology was determined for the 921 accidents. Since PTW accidents are complex events that often involve multiple collisions, the investigators often had to choose the accident typology that best fitted the accident being investigated. Investigators were asked to describe an accident using one of twenty-five specific accident typologies generated by the OECD Technical Experts Group (see the report "MAIDS Report on Methodology and Process").

Figure 3.3: Month in which accident occurred

img9.gif

The data presented in Figure 3.4 indicates that there is a wide diversity of accident types. When the data is partitioned according to PTW legal category the data shows that more L3 vehicles are involved in collisions where the PTW and the OV are travelling in opposite directions, with the OV turning in front of the L3 vehicle (10.5% versus 6.0%). The data shown in Figure 3.4 may be found in datatables page, Table C.4.

Figure 3.4: PTW accident configuration by legal category img12.gif


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