Road traffic crash fatalities: An examination of national fatality rates and factors associated with the variation in fatality rates between nations with reference to the World Health Organisation Decade of Action for Road Safety 2011 - 2020

The decade 2011 – 2020 has been declared the Decade of Action for Road safety by the World Health Organisation (WHO). The WHO has published baseline road traffic crash (RTC) fatality data for all member nations. The WHO has also suggested five Pillars of Action, mechanisms that may be instigated at the national level within the decade to halt or reduce the national RTC fatality toll. This thesis demonstrates that the some of the baseline data published by the WHO and derived from RTC reports compiled by national police forces may not best reflect the number of RTC deaths within each nation. Moreover, this data may not be directly comparable between nations due to definitional and other issues identified. Vital registration (death certificate) data which is directly collated by the WHO may be a better measure for international comparative work. Contrary to some previous studies, no significant association was found between the level of national economic development (in terms of per capita Gross National Income (GNI)) and health-derived RTC fatality rates for the year 2002. In multiple regression analysis of factors associated with GNI and presumed causal in the international variation in RTC fatality rates, exposure to risk of crash, percent of the vehicle population which are 2- or 3-wheeled, percent of the population aged 15 – 24 years, per capita alcohol consumption and health spend as a percent of GDP were all significantly associated with RTC fatality rate. No evidence was found to support WHO Pillar 4 in that road user behaviour modification via enactment and enforcement of road safety legislation was not significantly associated with the variation in RTC fatality rate. The demographic and cultural factors identified may not be amenable to modification. This has implications for the globalisation of remedial actions as proposed by the WHO

Validation of travel times to hospital estimated by GIS

BACKGROUND: An increasing number of studies use GIS estimates of car travel times to health services, without presenting any evidence that the estimates are representative of real travel times. This investigation compared GIS estimates of travel times with the actual times reported by a sample of 475 cancer patients who had travelled by car to attend clinics at eight hospitals in the North of England. METHODS: Car travel times were estimated by GIS using the shortest road route between home address and hospital and average speed assumptions. These estimates were compared with reported journey times and straight line distances using graphical, correlation and regression techniques. RESULTS: There was a moderately strong association between reported times and estimated travel times (r = 0.856). Reported travel times were similarly related to straight line distances. Altogether, 50% of travel time estimates were within five minutes of the time reported by respondents, 77% were within ten minutes and 90% were within fifteen minutes. The distribution of over- and under-estimates was symmetrical, but estimated times tended to be longer than reported times with increasing distance from hospital. Almost all respondents rounded their travel time to the nearest five or ten minutes. The reason for many cases of reported journey times exceeding the estimated times was confirmed by respondents' comments as traffic congestion. CONCLUSION: GIS estimates of car travel times were moderately close approximations to reported times. GIS travel time estimates may be superior to reported travel times for modelling purposes because reported times contain errors and can reflect unusual circumstances. Comparison with reported times did not suggest that estimated times were a more sensitive measure than straight line distance

Speeding drivers' attitudes and perceptions of speed cameras in rural England

There is evidence that excessive speed leads to an increased frequency and severity of road traffic accidents, but it is not clear how speeds may be reduced. To increase understanding of why drivers exceed the speed limits, the views of a sample of road users who had been prosecuted for exceeding the speed limit in the rural county of Norfolk England were sought. Respondents were categorised into a four group driver typology comprising conformers (those who report they never exceed limits) deterred drivers (those put off speeding by the presence of cameras), manipulators (those who slow only at camera locations) and defiers (those who exceed limits regardless of cameras), and the consistency of opinions was compared between the groups. Speeding was perceived as widespread and normal, and many drivers resented camera enforcement. Indeed, some respondents considered that cameras in themselves caused dangerous driving. For many drivers, the prosecution experience resulted in distress, anger and anti-camera sentiments, predominantly because they expressed the belief that they were more skilled than other drivers. It was also apparent that many respondents displayed a lack of awareness of the link between speed and collisions. Conformers were the least likely to state that the prosecution had deterred them from further speeding behaviour, possibly because they perceived themselves as already law-abiding. The deterred drivers were most likely to express intentions to avoid further speeding and their speeding incident was found to be most likely to be accidental. Manipulators and defiers tended to report that they had deliberately chosen to infringe the speed limits. Manipulators often acknowledged that their style of driving was dangerous; however, they failed to link this to their own behaviour. It was evident that many defiers and manipulators did not perceive speeding as a serious traffic law violation. Possible implications for road safety initiatives are discussed and recommendations are given for specifically targeting different driver types