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2016-2015
2016
03-9685802
03-9685801
msher@police.gov.il
2016
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1998
2014 2013
http www.rsa.gov.il siteCollectioDocuments D7%9E%D7%
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2016-201539
The International Traffic Safety and Analysis Grope (IRTAD), (2015), Road
Safety Annual Report.
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Cameron M., Newstead S., Diamantopoulou K., Oxley P. 2003. The
interaction between speed camera enforcement and speed-related
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between the speed of traffic and the number of accidents and accident
victims. Accident Analysis and Prevention, 50(1), 854-860.
2016-2015 50
Data:
P - number of poles (p= 1,..,P)
Q - number of groups of poles (i.e. cities, hot spots) (q = A,B,C,D…,Q)
STq - poles located in group q (STq P)
J - number of different types of punishments (j = 1,…J)
I - number of traffic courts (i=1,..,I)
E - number of tickets (for all types) that the encoder unit can handle
K - types of speed enforcement levels (function of speed limit) (k=1,..,K)
– expected number of tickets per type j produced at pole p at
speed enforcement level k during planning period
– expected % tickets per type j produced at pole p at speed enforcement
level k that go to court (the owner of the vehicle can ask for a court
summons instead of a fine)
– maximum number of court summons that Traffic Court i can handle
per planning period, i = 1,.., I
– expected utility of % of enforcement time at pole p at enforcement level k
during planning period (i.e. maximum reduction in road accidents
according to Power Model)
2016-201551
Decision variables
Xpk - % of time a camera at pole p is operated at speed enforcement level
k during planning period
Maximum Utility The
Power Model
1) Encoder unit capacity
2) Court unit capacity
3) Spatial constraints
4) Equality constraints
k=1,..,K5) The maximum is100%
≤1
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BI=Business intelligence
Elbashir et al., 2008Big Data
. Nenortaitė and Butleris, 2009
Negash, 2004
Cody et al., 2002
OLAP=Online) Data mining
Business performance analytical processing
Predictive analytics Benchmarking management
2012 ) 3
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Cody WF., Kreulem JT., Krishna V., Spangler WS. 2002. The integration of
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Carney, C., McGehee, D., Harland, K., Weiss, M., Raby, M. 2016. Using
Naturalistic Driving Data to Examine Teen Driver Behaviors Present in
Motor Vehicle Crashes, 2007-2015. AAA Foundation for Traffic Safety.
CARRS (Centre for Accident Research & Road Safety-Queensland)-Mobile
phone use & distraction while driving .Research & Road Safety(2012).
Queensland “State of the Road Centre of Accident
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while driving – effects on road safety. SWOV - Institute for road
safety research.
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driving but only five percent blame on texting. Mail on line news
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Factors Influencing Intensions to Use a Mobile Phone While Driving.
Accidents Analysis and Prevention. 40(6), 1893-1900.
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ETSC (2016). Lithuania to trial interlock on school busses.
ETSC (2016). Austria ti introduce alcohol interlock progamme next year.
ETSC (2016). France - road deaths increase as speeds creep up. ETSC 1.
IRTAD (2016). Road Safety Annual Report.
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Authority, Ireland.
WHO (2015). World Health Organization web site.
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M=0.22, SD=0.58)
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t p>0.05]
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The wisdom of the crowd, 2know .2008
http://www.kmrom.com/Site/Articles/ViewMagazineMonth.aspx?Year=200
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2016-2015105
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2013
2014 2014
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WHO, 2015
Schieber and Thompson 1996, AAP 2009, WHO 2013a
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