BackgroundDrivers at fault cause approximately 40% of all motorcycle crashes in New Zealand.AimTo identify relative speed of cars and motorcycles in an urban setting.MethodsFive urban, uncontrolled T-intersections known to be motorcycle crash ‘black spots’ were monitored using instrumentation and a roadside observer.Two sets of 12-h observations were collected for each site (N≈100 000). Instrumentation recorded the ‘events’ of vehicles passing to measure, speed, direction, lane position, vehicle type (broadly characterised) and headway. Observers further recorded times of bicycle events, type of motorcycle (scooters or motorcycles), the behaviour of motorcycles and the use of ‘high conspicuity’ gear such as clothing or helmets.The data was analysed for mean speeds and influences such as the time of day, the presence of a car at the T-intersection, and the influence of free headway. The results were compared for robustness across locations and days.ResultsResults establish that motorcycles travel around 10% faster than other traffic, with motorcycles travelling on average 3.3 kph faster than cars. Motorcycles are 3.4 times more likely to be exceeding the speed limit than cars. It is concluded that in urban areas motorcycles are travelling significantly faster than other traffic.Contribution to the FieldThese findings are discussed against a concern to reduce motorcycle crashes by improving conspicuity and previous research that implicates a ‘looked-but-failed-to-see’ effect for car drivers. These results suggest a ‘looked but-failed-to-judge-the-approach-speed’ effect, and this may have little to do with motorcycle conspicuity.
This research determines the size of the location-specific component of motorcycle crashes by comparing the degree of overlap between car and motorcycle crash rates at the same or similar locations drawn from the NZ crash record contained in the CAS database. Around 50 000 crash records are considered from 1980 to 2011. There is a strong correlation between the total number of car crashes and motorcycle crashes at the same locations, r (250)=0.567 p<0.0001. The usual method of ‘ranking’ and ‘selecting’ the worst sites is developed by using rate of car crashes to estimate the expected rates motorcycle crashes within a regression analysis (estimated Motorcycle crashes=5.4+0.051) (car crashes). Rates of motorcycle crashes are produced for specific sites and contrasted with the actual observed rates of motorcycle crashes at the worst-ranked 250 locations. Sites were rank-ordered by the degree of departure from the expected rates by calculating the standardised adjusted residual, and those outside 1.96 (ie, the 95th percentile) were group as the ‘worst sites’. These sites are significantly different from the remaining sites with increased odds of a motorcycle crash around 1.65–1.95 greater than the remaining sites. This analysis detects that there are site-specific features that elevate the risk of a motorcycle crash by around 80% when considering 300 m sections of road. The technique develops opportunities for additional analyses that might reveal the unique features of sites that especially elevate the risk of motorcycle crashes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.