Journal of Transport Geography

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1 Journal of Transport Geography 19 (2011) Contents lists available at ScienceDirect Journal of Transport Geography journal homepage: The impact of the school run on road traffic accidents: A spatio-temporal analysis Simon Kingham a,, Clive E. Sabel b, Phil Bartie a a Department of Geography, University of Canterbury, Christchurch, New Zealand b School of Geography, University of Exeter, Exeter, UK article info abstract Keywords: Spatio-temporal data trends school run road traffic accidents GIS New Zealand Engineering and improved road safety education has resulted in an overall decrease in road traffic accident numbers in Christchurch, New Zealand. The temporal trends of crash data from 1980 to 2004 reveal that lowering of crash rates is not occurring at a uniform rate throughout the day, with comparative increases in crash rates occurring during morning rush hour, and during the school run. No spatial clustering around schools was identified. This suggests that policies to reduce school travel related road accidents need to be focused on reducing overall traffic levels rather than focusing geographically on areas in the immediate vicinity of schools. Ó 2010 Elsevier Ltd. All rights reserved. 1. Introduction Road traffic accidents are a major cause of premature mortality, ranking tenth globally in terms of numbers and ninth for Disability Adjusted Life Years (DALYs), while in high income countries it is the third major cause of mortality (WHO, 2000). Despite this, road traffic accident rates whether calculated by population or distance travelled are declining consistently in developed countries as roads and vehicles become safer, although in some cases actual numbers increase as a result of increased populations and distances travelled (Dora and Phillips, 2000; ECMT, 2004). Road traffic accidents do not affect all groups equally. Young people are overly represented in accident statistics (MoT, 2006a,b; USDoT, 2005). In New Zealand over 30% of total mortality among the age range and 20% for those aged 1 14 is from road accidents (MoT, 2006a). In addition, the mode of travel is also a significant factor, with road accident and associated serious injuries over twice as great for motorcyclists, cyclists and pedestrians as they are for motor vehicle occupants (Dora and Phillips, 2000; USDoT, 2005). There is clear evidence in developed countries of an increasing trend for children to be driven to and from school in private motor vehicles, at the expense of other modes, such as walking, cycling and public transport (Pooley et al., 2005). Research has shown that from the mid 1980s to the late 1990s the proportion of children taken to school by car doubled in the UK (Hillman, 2002), the USA (Martin and Carlson, 2005; McDonald, 2005) and New Zealand (O Fallon, 2002). In Christchurch, a survey of primary schools in 1999 found that over half the children travelled to school by car (Cottam, 2001b). This increase in car use for the school run is of Corresponding author. Tel.: +64 (0) x7936; fax: +64 (0) addresses: (S. Kingham), exeter.ac.uk (C.E. Sabel), (P. Bartie). significant concern for a number of health, developmental and environmental reasons (Wilkinson and Marmot, 1998). Decreases in levels of physical activity (DoH, 2000; SPARC, 1999; Sustrans, 2003) have been shown to lead to reductions in educational performance (DoH, 2000; Ross, 2000; VTPI, 2003) and increases in obesity, especially amongst young people (Bundred et al., 2001; Prentice and Jebb, 1995; Watson, 2001). Air quality on roads near schools can be worse than other streets due to the high number of vehicles on short journeys when cars operate less efficiently (Sustrans, 2003) while it is widely accepted that car passengers are exposed to higher levels of pollutants than other roads users, including pedestrians (ETA, 1997; Kingham et al., 1998). In addition, it has been suggested that the shift away from walking and cycling can reduce a child s independence (Hillman, 1999, 2002). Research has shown that parents cite safety from strangers and traffic, along with time constraints as key factor for the shift away from walking and towards car travel (Black et al., 2001). In Christchurch, there has been a four and a half fold increase in the numbers of children travelling to school by car compared to how their parents travelled to school when they were school children. The main reasons cited by parents for this trend were related to road safety issues (34%) and the distance (32%). It should be noted that for 68% of the primary school aged children who travel by car, the trip is less than 2 Kms (CCC, 2005). This of course leaves nearly a third travelling further than 2 Kms, while for secondary school aged children a greater proportion will travel longer distances. The decline in the number of road traffic accidents including those involving pedestrian and cyclists (Dora and Phillips, 2000; MoT, 2006a) seems on the face of it to suggest an improvement in the safety of pedestrian and cycle travel. However, it has been suggested that this is more a result of less pedestrian and cycling activity by children than due to increasing safety of roads (DiGuiseppi et al., 1997; Roberts, 1993) with evidence showing /$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi: /j.jtrangeo

2 706 S. Kingham et al. / Journal of Transport Geography 19 (2011) that increased motor vehicle traffic on roads has led to roads being less safe, especially for the young (Roberts et al., 1992, 1995; Thomson, 1996). There are a number of factors which influence the crash rate, including safety of motor vehicles, safety of the road network, level of risk accepted by drivers, enforcement, and safety culture; and those which influence the risk exposure including the price of fuel, price of motor vehicles, quality and coverage of the road network, the degree of saturation of the network, and the convenience of car travel versus other modes (Keall and Frith, 1999). A significant body of research has been devoted to identifying spatial patterns of road traffic accidents in urban areas to help identify factors causing such accidents (Nicholson, 1999; Turner and Nicholson, 1998). Some research has examined the relationship between school travel and traffic accident rates at a local scale. In the US, each year 800 school aged children are killed in road traffic accidents during school hours; 14% of the total child road deaths (TRB, 2002). Focusing on pedestrian accidents, it has been shown that nearly one-fifth of all traffic fatalities and over a quarter of all traffic injuries were among pedestrians under the age of 16 years (USDoT, 1998). More school-age pedestrians were killed in the afternoon than in the morning, with 41% of these fatalities taking place during the single hour between 3 pm and 4 pm (USDoT, 2000). Other research focusing on pedestrians using a spatial approach found that injuries were greater in areas with higher youth population densities, more unemployment, fewer high-income households, and greater traffic flow, while during school term time were greater in areas containing middle schools and greater population densities of youth (LaScala et al., 2004). Research in Austria found that the majority of casualties among school aged children in school run accidents are among pedestrians, with cyclists the second largest group from aged ten (Mailer and Schopf, 2001). However there appears to be no published research that has specifically examined the impact of travel to and from schools on spatio-temporal patterns of road traffic accidents. This research aims to examine these trends in road traffic accidents from 1980 to 2004 in Christchurch, New Zealand, and to see whether they are occurring at a uniform rate relative to times when children are being transported to and from school. In addition it will attempt to identify any spatial patterns in road traffic accidents. Finally the findings will be related to policies directed at road accidents that are associated with school travel. 2. Method 2.1. Study area The study area for this research is Christchurch, New Zealand, a city of approximately 350,000 situated on the east coast of the South Island (Fig. 1). The Land Transport New Zealand annual report of road accidents reports some interesting findings (LTNZ, 2006). In 2005, there were 2502 reported crashes resulting in 1017 injuries with an estimated annual social cost in the region of $200 m. Nearly 80% of the injuries sustained were among drivers or passengers of motor vehicles with the rest pedestrians and cyclists. Over half of accidents in Christchurch were at urban intersections; nearly half of them were the fault of year olds with being the peak time for such crashes. Most crashes occurred on weekdays in the morning and evening rush hours, with a sudden rise in February (corresponding with the start of the school year) and declining thereafter. The majority of injured cyclists were under the age of thirty, with the greatest number aged years. Pedestrian injuries were also focused in the younger ages and peaked from Data and analysis Fig. 1. Study area, Christchurch, NZ. Road traffic accidents in New Zealand are recorded by the police and manually coded and entered into the Land Transport New Zealand Crash Analysis System (CAS) and Accident Investigation System (AIS). In CAS there are around 600 different crash cause codes (although in any data set not all will necessarily be used). Only Minor, Serious injury and Fatal accidents are recorded in the dataset. For this study, 25 years ( ) of data was extracted from CAS. This resulted in 28,645 geocoded crash points with associated attributes. It soon became apparent that the time of the accidents was frequently rounded to the nearest 15 min by the recording police officer. Consequently all data was aggregated into 15 min blocks. The main focus of this study was from to Data for the 25 years was grouped into 5 year periods ( , , , and ) and comparisons were made. To statistically test for any temporal patterns we used the statistical package R to fit a linear model, including a categorical hourly factor, centred on the halfhour, from until As an aim of this research was to identify the impact of school travel, it was therefore essential to differentiate between school term and non-term time. To this end, published school term times, from 1979 to 2003 collated from the Ministry of Education s Education Gazette, were used so that crash data could be attributed to term time and non-term times. Public holidays and weekends were omitted from the analysis. It should also be noted that New Zealand schools usually start between and and end between and Geographical approaches and more specifically GIS have been identified as useful tools in crash analysis (Joly et al., 1991; Miller, 1999). In this wider study, kernel density estimation has been used to show temporal patterns of accidents to identify hotspots at specific times of day and changing patterns over the study period (Sabel et al., 2005; Silverman, 1986). In this paper clustering analysis has been carried out in ArcGIS attempting to identify accident hotspots using the Getis-Ord Gi statistic (Getis and Ord, 1992). This was done for the whole study region for the period and also for the final 5 year period ( ). Identified

3 S. Kingham et al. / Journal of Transport Geography 19 (2011) hotspots were compared with the location of schools. The spatial analysis used 3113 accidents that occurred during school term time and These were aggregated to census area units (CAUs), and accident rates calculated per 1000,000 vehicle kilometres travelled (vkm). From these, the Getis-Ord Gi * statistic was calculated. This is a measure of the concentrations of high or low values for an entire study area. A Z-score was calculated to determine if the index value was significant. 3. Results 3.1. Long term trends in road traffic accidents The Land Transport New Zealand (LTNZ) accident system reports 28,645 minor, serious and fatal accidents in Christchurch from 1980 to This is likely to be a gross under-estimate as it has been well documented that under reporting of accidents occurs within New Zealand (Alsop and Langley, 2001). The past 20 years have seen a consistent increase in population and a decrease in vehicle accidents in Christchurch (Fig. 2). This can be attributed to improvements in driver education, road network and vehicle engineering. Fig. 3. Daily crash counts for Christchurch city, (aggregated to 15 min periods) h temporal trends Fig. 4. Daily crash counts for Christchurch city, , , , and 2004, between the hours of (aggregated to 15 min periods). The total number of accidents, for all modes, in the period by time of day (to the nearest 15 min) is presented in Fig. 3. Accidents start to climb from and decline for the day from Two clear peaks can be seen; one between and 08.30, the morning rush hour; and a second larger one between and 17.00, the early evening rush hour. This is unsurprising as these are times of greatest traffic movement. Of perhaps greater interest is to look at how these temporal patterns have changed over time focusing on the hours between and (Fig. 4). This shows a clear reduction in the number of accidents occurring across all times of day. However, what is interesting is that the peak between and has declined rapidly, but now there seems to be two distinct afternoon peaks, the first at about and the second at The former coincides with the time that schools close, and the latter with a slightly later period of commuter travel. The commuter rush hour is perhaps unexpected, but the school one is worth further investigation. It is perhaps particularly worth noting the magnitude of reduction between and is markedly less pronounced around the school opening and closure times compared to other times within the two peaks, providing evidence that the rate of Fig. 2. Road traffic accident and population trends, Christchurch

4 708 S. Kingham et al. / Journal of Transport Geography 19 (2011) reduction of accidents from 1980 to 2004 is least around school opening and closing times. Approximately 80% (range 77 84% ) of road traffic accidents take place on school term time days, which compares with approximately 74% of days as being term time days. Therefore a greater proportion of accidents occur on term time days relative to the number of days; an unsurprising finding bearing in mind the additional traffic volume occurring on those days. It was also possible to examine the total number of crashes which occurred in 5- year periods during weekdays in school term and non-term times (figures not shown here). The most obvious difference was the accident counts were much higher in term time, reflecting the higher traffic flows. The afternoon peak was higher than the morning peak in both term time and non-term time, although the difference was more pronounced in non-term time. There is a midday peak in both figures, which was larger than the morning peak in the non-term data but less significant in the term time data. There was an obvious peak in the 1980s and early 1990s from around until Since the second half of the 1990s this peak dropped significantly and became two distinct peaks at and again at Term time accidents in the morning and afternoon/evening rush hours merit closer examination and this was done by focusing on these times (Fig. 5). It can clearly be seen that at most times there has been a decline in the number of crashes; this is particularly noticeable between and The most distinct exception to this is at The number of accidents occurring at this time has hardly declined over the period of this study. The relative change from the period to (Fig. 6) re-emphasises this point shows a 20% increase in accidents from to A similar increase occurs at with all other time periods showing declines in the number of accidents. To test the statistical significance of these peaks, we fitted a linear model as outlined in Section 2.2. When comparing the intercept term (percentage change from the hour starting 07:30) with the peaks, the only significant ones were 8:30 9:15 (p = ) and 14:30 15:15 (p = ). These two peaks are therefore highly statistically significant. It is worth noting that the trough from is also statistically significant (p = ). We investigated the overall percentage change at a given time of day between term and non-term time for the entire study period (Fig. 7); a positive value indicates proportionally more accidents in term time than non-term and vice versa for that specific 15 min interval. It can be seen that there were proportionally more crashes Fig. 6. Percentage change in accidents from to , and (aggregated to 15 min periods; expressed as a percentage of crashes in that time). Fig. 7. Percentage change in crash rates for time of day during term and non-term times, , (aggregated to 15 min periods; expressed as a percentage of total crashes in period). around and from to in term time. This is unsurprising given the increased traffic at these times, but serves to emphasise the impact of education travel on accident numbers. Of interest was the magnitude of the difference occurring between and This probably reflects the concentrated nature of the morning rush hour, with both work and education travellers moving whereas the afternoon rush hour, is divided between an earlier school peak and a later work peak Spatial clustering of accidents near schools Having reported temporal analysis we now move to investigate any evidence for spatial patterning of accidents during term time. Fig. 8 shows spatial clustering of accidents using the Getis-Ord Gi statistic Z-scores, with the location of schools also marked, for the whole study period. The values above 1.96 represent those areas where there are statistically significant clusters of accidents, using a search radius of 1500 m. This analysis was also carried out for the 5 year period, As can be seen from Fig. 8, there was no apparent relationship between crash hotspots and schools, whereas statistically significant clusters were revealed in the north, and north-east sectors of the city (following two of the major arterial routes). 4. Discussion Fig. 5. Daily crash counts for Christchurch city in school term time, and , (aggregated to 15 min periods). The results of this study show that while there has been an overall increase in traffic volume over the 30 years, there has been

5 S. Kingham et al. / Journal of Transport Geography 19 (2011) Fig. 8. Spatial clustering of road traffic accidents for Christchurch census area units for school travel hours (8 9 am and 3 4 pm) for school terms a general decline in the number of road traffic accidents. However during term time, this decline has not been uniform across the day. We provide convincing evidence that accidents have increased at and , but not from Spatially, we have not observed any evidence for accidents to occur in the immediate vicinity of schools but rather report a more complex pattern of raised accident rates across the city. This suggests that the increase coincides with the time when children are being dropped off and collected from school. It is clear that there has been a dramatic shift in modal choice away from active modes such as walking and cycling in favour of children being driven to school (Hillman, 2002; Martin and Carlson, 2005; MoT, 2007). In addition, it has been shown that the further from school children live, the greater the likelihood that they will be driven to school (McDonald, 2005; Schlossberg et al., 2006), and that there has been an increase in average distance travelled to school (DiGuiseppi et al., 1998). There is some debate over the actual contribution of school travel to total traffic volume. Some research in the UK has suggested that only 8% of the car miles driven during morning peak traffic is associated to the school run, and that the 70% reduction in mileage shown in school holidays is associated to a reduction in business traffic as a result of employees taking time off to coincide with the school holidays (Bradshaw and Jones, 2000). The argument follows that eliminating the school run would have a lower effect on reducing the traffic volumes at peak times, than is commonly assumed. Research in New Zealand has suggested that the school run contributes significantly to the rush-hour traffic congestion in developed countries, accounting for about 40% of all journeys (Thull and Lausterer, 2003, p. 1) and in Auckland, the largest city in the country, journeys to education contribute up to 40% of all morning peak trips (Auckland Sustainable Cities Programme, 2004). Part of the debate revolves around whether school run journeys that are part of chained trips (i.e. the driver goes onto to another destination rather than returning home) count as school run journeys. Trip chaining (Adler and Ben-Akiva, 1979) has become more common, with parents dropping off children en route to their workplace. Intra-urban journeys now involve more destinations, routes have become longer and with it the complexity of analysis has increased. A study in Edinburgh (Hall, 1998) found that twothirds of the fathers who dropped off a child at school went onto work. Whatever the exact contribution of the school run to overall traffic volumes, it can be said that, over the last few years, there has been an increase in the modal share of cars and that the distances travelled have increased, and that consequently school travel will have contributed to raised traffic volumes at and , and has contributed to the observed raised levels of accidents at those times. This logically leads onto some discussion of possible policies to reduce this relative increase in school run associated road accidents. There is an apparent link between the timing of the school run and relative peaks in road accidents, but no spatial clustering near the schools. This suggests one of two things. One possibility is that the temporal cluster of accidents at school opening and closing time has nothing to do with schools, and it is mere coincidence that accidents happen around school closing time. Alternatively, and very plausibly, we would argue that while the journey may originate or terminate at school, a lot of the school run is not taking place in the immediate vicinity of schools but rather between home or work to and from school. This suggests that any policies to target the school run should be broadly based and less geographically based on school location. One factor that has been shown to affect school run modal choice is distance from home to school with increasing distance resulting in a reduction in the number of pupils who travel independently and an increase in those travelling by car (Martin and Carlson, 2005; McDonald, 2005). It follows that a policy of children going to their local school is more likely to result in lower rates of car use for the school run, and therefore a lower rate of school run associated road accidents. School catchments or zoning are of value in this respect. Research in the UK has suggested that when given the choice about a third of 11 year old pupils moved to schools other than their catchment (Parsons et al., 2000). This is likely to result in more car use for the school run. A policy of enforced school zones of catchments could lead to less car use and reduced school run related accidents. However school zoning is a political topic and various countries have examined ways to overcome disparities in school access, which has implications for school zoning (DfCSF, 2007; Thrupp, 2007). Recent years have seen an increase in policies designed to make school travel safer. These include such things as Safe routes to school (Sustrans, 1996), which have been shown to lead to reductions in

6 710 S. Kingham et al. / Journal of Transport Geography 19 (2011) cycling and child pedestrian accidents (Appleyard, 2003; Delaney et al., 2004). Many of these policies are local and can include engineering safety work around schools. Research has shown that local transport changes can result in significant reductions in traffic (Sloman, 2003), and the introduction of reduced traffic speeds around schools (Cottam, 2001a; Osmers, 2001) has also made travelling to school safer. This may partially explain why there is no clustering of accidents in the vicinity of schools. However while these policies may have some local impact this research has shown no clustering of accidents in close proximity of schools but rather a more general increase in accident rates at the time the schools close at the end of the day. Hence it is concluded that measures to reduce car focused school travel might need to be implemented more widely than merely policies that focus on the immediate vicinity of schools. These might include policies that improve road safety. Perhaps more usefully, this should include policies that increase walking, cycling and public transport use, which bring with them wider public health benefits. 5. Conclusion Engineering and better road safety education has resulted in an overall decrease in road traffic accident numbers in Christchurch, New Zealand. The temporal trends of crash data from 1980 to 2004 reveal that this lowering of crash rates is not occurring at a uniform rate throughout the day, with comparative increases in crash rates occurring during morning rush hour, and during the school run. In addition no spatial clustering around schools was identified. This suggests that road traffic accidents that seem temporally associated with the school run affect a large area and are not focussed near schools themselves. A number of policies to reduce traffic accidents have focused geographically on areas in the immediate vicinity of schools. These include such as things as Safe Routes to School and reduced traffic speeds near schools. This research suggest that while these are clearly of use, any proposed measures introduced which are designed to reduce road traffic accidents should not exclusively be focussed on the immediate vicinity of schools, and should instead have a broader geographical focus, designed to reduce school overall traffic levels. Acknowledgements Thanks to the University of Canterbury for a grant to support this project. Thanks to Marney Brosnan and Paul Bealing of the Department of Geography, University of Canterbury, for producing Fig. 1, and to Prof. Trevor Bailey, Department of Mathematics, University of Exeter, for statistical advice. References Adler, T., Ben-Akiva, M., A theoretical and empirical model of trip chaining behavior. Transportation Research B 13B, Alsop, J., Langley, J., Under-reporting of motor vehicle traffic crash victims in New Zealand. Accident Analysis and Prevention 33, Appleyard, B., Planning safe routes to school; how will my child get to and from school? Planning 69.5, 34(4). Auckland Sustainable Cities Programme, Auckland School Travel Plans: Making School Journeys Active, Social, Safe and Sustainable. New Zealand Sustainable Development Programme of Action, Sustainable Cities Joint Action Plan for Auckland, Wellington, New Zealand. Black, C., Collins, A., Snell, M., Encouraging walking: the case of journey-toschool trips in compact urban areas. Urban Studies 38 (7), Bradshaw, R., Jones, P., The Family and the School Run: What Would Make a Real Difference? Report No. FDN28. AA Foundation for Road Safety Research. Bundred, P., Kitchiner, D., Buchan, I., Prevalence of overweight and obese children between 1989 and 1998: population based series of cross sectional studies. BMJ 322, CCC, Safe Routes to School. Christchurch City Council. Cottam, P., 2001a. Christchurch s 40 km/h Part Time School Speed Zone Trial: Community Perceptions and Attitudes, Road Safety Research. In: Policing and Education Conference, Melbourne. Cottam, P., 2001b. Safe Routes to School in Christchurch, New Zealand. In: Injury Prevention Network Conference, Wellington, New Zealand. Delaney, A., Newstead, S., Corben, B., Outcome Evaluation of the Effectiveness of the Safe Routes to Schools Program Monash University Accident Research Centre, Melbourne, Australia. DfCSF, New admissions code ensures fair and equal access press release 09/ 01/2007. In: Department for Children Schools and Families (Ed.). DiGuiseppi, C., Roberts, I., Li, L., Influence of changing travel patterns on child death rates from injury: trend analysis. British Medical Journal 314, DiGuiseppi, C., Roberts, I., Li, L., Allen, D., Determinants of car travel on daily journeys to school: cross sectional survey of primary school children. British Medical Journal 316 (7142), DoH, Guidance on Safe Active Travel to School in Primary School Teacher Training. UK Department of Health, London. Dora, C., Phillips, M., Transport, Environment and Health. No. 89. WHO, Copenhagen. ECMT, Trends in the Transport Sector In: European Conference of Ministers of Transport. OECD, Paris. ETA, Road User Exposure to Air Pollution: Literature Review, Environmental Transport Association. Getis, A., Ord, J., The analysis of spatial association by use of distance statistics. Geographical Analysis 24, Hall, C., The School Escort Journey as a Component in the Complex Trip Chain. University of Westminster, London, UK. Hillman, M., Curbing children s social and emotional development: an unrecognised outcome of parental fears. Journal of Contemporary Health 8, Hillman, M., Are we developing battery-reared or free-range children? WPAPC Seminar, Perth, Australia. Joly, M., Foggin, P., Pless, I., Geographical and socio-ecological variations of traffic accidents among children. Social Science & Medicine 33 (7), Keall, M., Frith, W., Measures of Exposure to Risk of Road Crashes in New Zealand. IPENZ Transactions. Kingham, S., Meaton, J., Sheard, A., Lawrenson, O., Assessment of exposure to traffic-related fumes during the journey to work. Transportation Research Part D-Transport and Environment 3 (4), LaScala, E.A., Gruenewald, P.J., Johnson, F.W., An ecological study of the locations of schools and child pedestrian injury collisions. Accident Analysis and Prevention 36 (4), LTNZ, Road Safety Issues: Christchurch City Land Transport, New Zealand. Christchurch, NZ. 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7 S. Kingham et al. / Journal of Transport Geography 19 (2011) th Annual Colloquium of the Spatial Information Research Centre. University of Otago, Dunedin, New Zealand. Schlossberg, M., Greene, J., Phillips, P.P., Johnson, B., Parker, B., School trips effects of urban form and distance on travel mode. Journal of the American Planning Association 72 (3), Silverman, B., Density Estimation for Statistics and Data Analysis. Chapman & Hall, London. Sloman, L., Less Traffic Where People Live: How Local Transport Schemes can Help Cut Traffic. Transport for Quality of Life, Machynlleth, UK. SPARC, Push Play Facts Summary. Sport and Recreation, New Zealand. Sustrans, Safety on the Streets for Children. FF10, Sustrans. Sustrans, The Health Benefits of Safe Routes to School. FS15, Sustrans. Thomson, J., Child pedestrian accidents: what makes children vulnerable? In: Graham, B., Thomson, J. (Eds.), Child Safety: Problem and Prevention from Preschool to Adolescence. A Handbook for Professionals. Routledge, London, pp Thrupp, M., School admissions and the segregation of school intakes in New Zealand cities. Urban Studies 44 (7), Thull, J.-P., Lausterer, H., Mobility management for high school students in Christchurch. In: Australian Transport Research Conference (ATRF), Wellington, New Zealand. TRB, The Relative Risks of School Travel. A National Perspective and Guidance for Local Community Risk Assessment. Special Report 269. Transportation Research Board, Committee on School Transportation Safety, Washington, DC. Turner, S., Nicholson, A., Intersection accident estimation: the role of intersection location and non-collision flows. Accident Analysis & Prevention 30 (4), USDoT, Traffic Safety Facts. 1998: Pedestrians, US Department of Transportation, National Highway Traffic Safety Administration. National Center for Health Statistics and Analysis, Washington, DC. USDoT, Fatality Analysis Reporting System (FARS) Web-based Encyclopedia. US Department of Transportation. National Highway Traffic Safety Administration, National Center for Health Statistics and Analysis, Washington, DC. USDoT, Transportation Statistics Annual Report. US Department of Transportation. Research and Innovative Technology Administration, Bureau of Transportation Statistics, Washington, DC. VTPI, Online TDM Encyclopedia School Transport Management, Victoria Transport Policy Institute. Watson, L., Kiwi Kids Losers in Battle of the Bulge. The Press, Christchurch. p. 1. WHO, World Health Organisation Report World Health Organisation, Geneva. Wilkinson, R., Marmot, M., Social Determinants of Health: The Solid Facts. WHO, Copenhagen.

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