Skip to main content

Currently Skimming:


Pages 127-137

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 127...
... First, the limitations of planning level safety forecasting models are described. The data requirements for such a model are then discussed, followed by software requirements and required expertise.
From page 128...
... DATA REQUIREMENTS OF PLANNING LEVEL SAFETY FORECASTING MODEL Both the development and use of the prediction model requires data by traffic analysis zone (TAZ)
From page 129...
... DETAILED DEVELOPMENT OF A SAFETY PREDICTION MODEL AT THE TAZ LEVEL Exhibit 99 depicts the process that the analyst follows to develop the planning level safety prediction model. The process consists of three basic steps: • data collection, • development of a dataset containing variables used in modelling, and • development/estimation of the statistical models used for forecasting.
From page 130...
... Determining the significance of each of the variables in the model b) Determine whether the relationship provided by the model can be logically explained Repeat process and estimate a number of candidate models using variations of variables and by adding, maintaining or dropping variables based on tests required in previous step Exhibit 99: Process followed to develop PLANSAFE by TAZ for planning level safety prediction Appendix D: Developing a Planning Level Safety Forecasting Model (PLANSAFE)
From page 131...
... Typical data per TAZ area considered for inclusion into the model are: • road network mileage by federal functional classification, • accident data: a variety of variables can be generated varying from degree of injuries sustained in the accidents, number of injuries and fatalities, or accident types, • census data: population, age distribution within a TAZ (e.g., number of individuals age 17 and younger) , employment, housing units: vacant and occupied, persons with disabilities, etc., and • traffic volume data: vehicle miles traveled.
From page 132...
... The ArcGIS environment is used but similar processing can be performed in other GIS environments as the description is intended to provide the sequence for processing operations in command line or graphical user interface environments; or for scripted batch processing. Refer to the section titled Using GIs in the Development of the Planning Level Safety Forecasting Model for a discussion of the GIS processing procedures.
From page 133...
... Appendix D: Developing a Planning Level Safety Forecasting Model (PLANSAFE)
From page 134...
... Often times coding and transcription errors can be detected during this process so as to avoid negative influences on the modelling results. Development of Crash Prediction Model The researchers of NCHRP 8-44 developed a safety prediction model by using the following approach and assumptions: • Accident count distribution.
From page 135...
... Knowledge of transportation safety is used to derive a model that is consistent and in agreement with current knowledge of motor vehicle crashes and safety. U.S.ING GIS IN THE DEVELOPMENT OF THE PLANNING LEVEL SAFETY FORECASTING MODEL The Planning Level Safety Prediction Model requires the analyst to perform various calculations within the GIS environment.
From page 136...
... Calculate population for unioned polygon feature class 8. Summarize counts by TAZ for output unioned feature class polygon Assignment of total road mileage to each TAZ Some of the variables considered during the development of a planning level safety prediction model and subsequently required during the application of the model, includes the length of roads within a particular TAZ with a particular functional classification or characteristic.
From page 137...
... with the TAZ In the planning level safety prediction model, the analyst uses the frequency of accidents or severity of accidents or any other related events per TAZ. The analyst therefore has to develop a data set that summarizes the particular data points within each TAZ.


This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More information on Chapter Skim is available.