Here is my one page proposal for a joint geography and transport PhD on
road traffic accident incidence submitted on 23/02/00:
Explaining Geographical Road Traffic Accident Patterns
Analysing road traffic accident (RTA) patterns is of considerable practical
importance. RTAs account for approximately 300000 fatalities worldwide,
4000 in the UK and 100 in West Yorkshire annually, the number of injuries
is around 50 to 100 times higher. Perhaps the most worrying thing
of all is that despite attempts to improve safety, all these figures are
rising. Spatially referenced data concerning RTAs has been collected
and stored as a mandatory requirement in the UK for many years. This
data is compiled in a database or data structure called STATS19 which contains
grid references (giving the approximate location of accidents to the nearest
metre), variables describing the time of the accident, the people and vehicles
involved, and the road and weather conditions. Further information
in the database includes a classification for the severity of the accident
and a written description of the event. From STATS19 different types
of accidents can be selected and the density of these accidents can be
compared with those from a model that tries to predict these accident frequencies
or rates. Such models have been developed based on road network characteristics
and other geographical information but there is likely to be a geographical
residual or observable difference between what is predicted by the model
and what is observed or recorded in the incidence data. The Geographical
Analysis Machine (GAM) can be used to map this residual error and identify
where model predictions differ greatly from the observed incidence rates
over a given period of time. A geographical residual is very likely
to exist because of exogenous geographical factors that affect RTA incidence
that are not included in the models. What are these exogenous geographical
factors and can they be measured or used to develop better models and road
safety information? What are the different types of geographical
factors that are related to RTA incidence? What geographical information
can be used to predict RTA rates? How much of the observed RTA incidence
can be explained by geographical information? How much are RTA rates
affected by the geographical environment? What additional geographical
variables can improve transport models that predict RTA rates or frequency?
What geographical information can help target road safety campaigns?
These kinds of questions will be addressed in the PhD and an introduction
and extensive literature review will ground the research.