![Aggregation of driver celeration behavior data: Effects on stability and accident prediction [An article from: Safety Science]](https://www.ebooknetworking.net/books/B00/0PD/bigB000PDT7EC.jpg)
Aggregation of driver celeration behavior data: Effects on stability and accident prediction [An article from: Safety Science]
Book Details
Author(s)A.E. af Wahlberg
PublisherElsevier
ISBN / ASINB000PDT7EC
ISBN-13978B000PDT7E5
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Safety Science, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Predictions about effects of aggregating driver celeration data were tested in a set of data where bus drivers' behavior had been measured repeatedly over three years in a city environment. For drivers with many measurements, this data was correlated with the drivers' accident record at various levels of aggregation over measurements. A single measurement (one sample) was seldom a significant predictor, but for each drive added to a mean, the variation explained in accident record was increased by about 1%. Also, correlations between measurements increased when these were aggregated, and the association with number of passengers (a proxy for traffic density) decreased somewhat, all as predicted. These results show that although driver celeration behavior is only semi-stable across time and environments, aggregating measurements increases both stability and predictive power versus accidents considerably. The celeration variable is therefore promising as a tool for identifying dangerous drivers, if these can be measured repeatedly, or, even better, continuously.
Description:
Predictions about effects of aggregating driver celeration data were tested in a set of data where bus drivers' behavior had been measured repeatedly over three years in a city environment. For drivers with many measurements, this data was correlated with the drivers' accident record at various levels of aggregation over measurements. A single measurement (one sample) was seldom a significant predictor, but for each drive added to a mean, the variation explained in accident record was increased by about 1%. Also, correlations between measurements increased when these were aggregated, and the association with number of passengers (a proxy for traffic density) decreased somewhat, all as predicted. These results show that although driver celeration behavior is only semi-stable across time and environments, aggregating measurements increases both stability and predictive power versus accidents considerably. The celeration variable is therefore promising as a tool for identifying dangerous drivers, if these can be measured repeatedly, or, even better, continuously.
