Bayesian multiple testing procedures for hotspot identification
Comparison of spatial methods for measuring road accident ‘hotspots’: a case study of London
Effects of low sample mean values and small sample size on the
estimation of the fixed dispersion parameter of Poisson-gamma models
for modeling motor vehicle crashes: a Bayesian perspective
Efficient map-matching of large GPS data sets -
Tests on a speed monitoring experiment in Zurich
http://www.strc.ch/pdf_2005/STRC05_D2_Marchal.pdf
http://pubsindex.trb.org/document/view/default.asp?lbid=781000
New Compress Sampling Algorithm for FFT-based GPS Signal Acquisition
Asset tracking system employing reduced order GPS with compressed GPS
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HSID Methods
Wen Cheng
,
and Simon P. Washington1,
Accident Analysis & Prevention Volume 37, Issue 5, September 2005, Pages 870-881
The objective of hot spot identification (HSID)is to identify transportation system locations(road segments, intersections, interchanges, ramps, etc.) that possess underlying correctable safety problems, and whose effect will be revealed through elevated crash frequencies relative to similar locations.
McGuigan, D.R.D., 1982. Non-junction accident rates and their use in “black-spot” identification. Traffic Eng. Contr. 23 (2), 60–65.
potential for accident reduction, as the difference between the observed and expected number of crashes at a site
Hakkert, A.S., Mahalel, D., 1978. Estimating the number of accidents at intersections from a knowledge of the traffic flow on the approaches. Accident Anal. Prevent. 10, 69–79.
proposed that blackspots should be defined as those sites whose accident frequency is significantly higher than expected at some prescribed level of significance
Mahalel, D., Hakkert, A.S., Prashker, J.W., 1982. A system for the allocation of safety resources on a road network. Accident Anal. Prevent. 14 (1), 45–56.
proposed the road sites selected for treatment should maximize the expected total accident reduction by treatment.
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