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Prioritization Techniques to Evaluate Sites for Improving Winter Safety in Iowa

Mohammad Shaheed

Zach Hans

Konstantina Gkritza

Inya Nlenanya

Neal Hawkins

2013 Mid-Continent Transportation Research Symposium

Ames, Iowa

16th August, 2013

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Acknowledgement

• Tina H. Greenfield (Iowa DOT)• Annette Dunn (Iowa DOT)• Bob Younie (Iowa DOT)• Mid-America Transportation Center (MATC)

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Presentation Outline

• Background• Objective• Methodology• Analysis Results• Conclusions

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Background• Winter-weather related crashes

– Hundreds of fatalities/injuries on Iowa’s roadways– About 1/3 of the total crashes occurred during

2004/2005 to 2011/12 winter seasons– Half of the rural interstate crashes related to

weather• Transportation agencies spend millions for winter

weather maintenance and operations• No systematic method to identify critical winter

weather crash locations

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Objective

• Identify candidate locations for improving winter weather safety in Iowa– Using winter weather crash data from 2002-2009– For different types of roadways– Using different metrics for prioritization

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Methodology

• Evaluation of Winter-weather prone sites based on– Crash density– Crash proportion– Crash severity– Combined metric– Standard deviation based analysis– Moving average analysis

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Crash Density

• Primarily crash frequency per mile per year• Derived for each road segment

– Dividing total crash frequency by total road segment and number of years

• Normalized Crash density– Each individual density divided by the maximum

value– For common type of roadway

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Crash Proportion• Derived for each road segment

– Total winter crashes divided by the total crashes on a road segment for the analysis period

– Already normalized with a maximum value of 1.0– No further normalization necessary

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Crash Severity

• Takes severity into consideration• A total score assigned to each road network

• Normalized score used for each road segment

Injury severity Points each occurrence Fatality 200, with the first fatality at a

site treated as a major injury Major 100 Minor 10 Possible/Unknown 1

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Combined Metric

• Computed the total score– Normalized scores each given a one-third (1/3)

weight

• Less possibility of misrepresentations of winter crash experience for a single road segment

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Categorization of Each metric• Each metric categorized within the appropriate

road type and analysis period• The derive ranges include five categories

Category Metric value is among percentage of system mileage

1 Lowest 40 percent 2 Next 25 percent 3 Next 20 percent 4 Next 10 percent 5 Highest 5 percent

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Standard Deviation(STDEV) Based Analysis

• Standard deviation of the metrics were computed– For each road type and analysis period

• Divide the total score by the standard deviation– To examine if the combined metric was within a

certain range of the standard deviation

• Rank the segments based on the values

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Moving Average Analysis• Moving average of the combined metric

– 3 mile road segment– Conducted on I-29, I-35, I-80, I-380, US-20– To consider the spatial proximity of the road

segments in the ranking process

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Results: Crash Density (Freeway)

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Results: Crash Density STDEV based Analysis

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Results: Crash Proportion (Freeway)

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Results: Crash Proportion STDEV based Analysis

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Results: Crash Severity (Freeway)

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Results: Crash Severity STDEV based Analysis

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Results: Combined Metric (Two-Lane)

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Results: STDEV Based Combined Metric (Freeway)

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Results: STD Based Combined Metric (I-35 N)

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Results: Standard Deviation Based Combined Metric (I-80 E)

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Results: STD Based Combined Metric (Two Lane)

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Results: STDEV Based Combined Metric (Two Lane)

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Ranking Based on Combined Metric (Two Lane)

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Results: Moving Average Analysis

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Results: Moving Average Analysis

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Results: Moving Average Analysis (I-35 N)

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Results: Moving Average Analysis (I-80 E)

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Conclusions

• Results can be helpful to identify problematic locations during winter weather conditions

• Useful for screening road networks for winter safety improvement

• Moving average analysis was performed to take into account the spatial proximity of the road segments

• Combined metric is useful to offset the limitations of each individual metric

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Future Research• Research underway to develop safety

performance functions for winter weather related crashes– Incorporation of winter weather conditions– Empirical-Bayes adjusted crash frequency can be

used to screen road networks