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Freeway Crash Prediction Models for Long-Range Urban Transportation Planning

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Freeway Crash Prediction Models for Long-Range Urban Transportation Planning. Arun Chatterjee (UT) Joe Hummer (NCSU) Vasin Kiattikomol (UT) Mary Sue Younger (UT). Objective.

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freeway crash prediction models for long range urban transportation planning

Freeway Crash Prediction Models for Long-Range Urban Transportation Planning

Arun Chatterjee (UT)

Joe Hummer (NCSU)

Vasin Kiattikomol (UT)

Mary Sue Younger (UT)

objective
Objective
  • Develop crash prediction models to assess safety impacts for urban freeway networks, which can be used by state DOT and MPO planners for long-range planning
  • The independent variables should not be too difficult to predict
how to create freeway segments
How to Create Freeway Segments?
  • No previous work recognized the influence of interchanges on crashes explicitly
impact of interchanges on crashes
Impact of Interchanges on Crashes
  • Since traffic flow characteristics near interchanges are different from areas away from them, interchanges are likely to have impact on crashes
  • No previous studies examined this issue
  • This research compared crashes in interchange areas with those in areas away from it, and found a significant difference
crash rates for nc tn
Crash Rates for NC & TN

Unit: crashes per Million Vehicle Miles Traveled

crash rates for nc tn1
Crash Rates for NC & TN

Unit: crashes per Million Vehicle Miles Traveled

1 2000-2002 statewide crash report, NCDOT

proportion of crash types
Proportion of Crash Types

12000-2002 statewide crash report, NCDOT

21988-2003 crash data, National Highway Traffic Safety Admin.

model development scheme
Model Development Scheme
  • Develop separate models for:
    • Segments away from interchanges
      • Segments with 4 lanes
      • Segments with more than 4 lanes
    • Segments near interchanges
      • Segments with 4 lanes
      • Segments with more than 4 lanes
  • 3 Modeling Approaches
    • Analysis of Variance (ANOVA)
    • Regression Analysis
    • Classification Tree Analysis
crash and freeway data
Crash and Freeway Data
  • Counties with medium to large sized urban areas
    • NC: 6 counties
    • TN: 4 counties
  • Freeway data
    • NC: 73 non-interchange segments (137 mi.), 65 interchange segments
    • TN: 142 non-interchange segments (145 mi.), 65 interchange segments
  • Crash data by severity (2000-2002)
    • Fatal
    • Injury
    • Property-damage-only (PDO)
counties
Counties
  • NC Counties:
    • Alamance, Buncombe, Cumberland, Durham, Gaston, and Wake
  • TN Counties:
    • Davidson (Nashville), Hamilton (Chattanooga), Knox (Knoxville), Shelby (Memphis)
crash prediction models
Crash Prediction Models
  • NC
  • TN
  • Non-interchange segments
  • Interchange segments

Model classification for each modeling approach:

  • By state
    • By type of segments
      • By number of lanes
        • By type of crash severity
  • 4 lanes
  • >4 lanes
  • Injury
  • PDO
  • Fatal + injury
anova models
ANOVA Models
  • Response variable: Crash rate (by severity)
  • Factors:
    • Type of segments
    • Number of lanes
    • Traffic volume
  • Number of models
    • 3 for NC
    • 3 for TN
total crash rates for nc
Total Crash Rates for NC

Total = Fatal + Injury + PDO

total crash rates for tn
Total Crash Rates for TN

Total = Fatal + Injury + PDO

regression models
Regression Models
  • Response: No. of Crashes (by severity)
  • Predictors:
    • Segment length
    • Traffic volume (AADT)
  • Use Generalized Linear Modeling technique with Negative Binomial Distribution assumption for crashes
  • Estimate model parameters using maximum likelihood technique in ‘GENMOD’ procedure in SAS
regression models contd
Regression Models (Contd.)
  • Common model forms:
    • Crashes = a (Segment Length)b1 (AADT)b2

for non-interchange segments

    • Crashes = a (AADT)b

for interchange segments

  • Number of models
    • 12 for NC
    • 12 for TN
findings of graphical analysis
Findings of Graphical Analysis
  • For NC, ‘interchange crashes’ on 4-lane freeways are higher and also increased more rapidly at higher volumes than others
  • For TN, ‘interchange crashes’ on >4-lane freeways are higher at higher volumes than others
  • The pattern of increase of ‘fatal and injury’ crashes is similar to that of ‘PDO’ crashes in both NC and TN
classification tree models
Classification Tree Models
  • Predict: Crash Rate (by severity)
  • Predictors:
    • Traffic volume (AADT)
    • Number of Lanes
  • Develop in 2 steps:
    • Develop tree models using CART algorithm in SPSS AnswerTree
    • Manually calculate crash rate using VMT information and predicted crashes from tree models and develop crash rate tables
classification tree models contd
Classification Tree Models (Contd.)
  • Number of models
    • 2 for NC
    • 2 for TN
findings for classification tree models
Findings for Classification Tree Models
  • Each tree has unique structure (split pattern) for each state and for each type of segments
  • Tree models classified segments into groups with different threshold values of AADT from those specified in ANOVA models
assessment and comparison of models
Assessment and Comparison of Models
  • Assess model performance using ‘relative measure’ in terms of ‘percent error’, which is calculated in 2 steps:
    • Calculate difference between predicted crashes and actual crashes in terms of Root Mean Squared Error (RMSE) for each model
    • Calculate Percent Error of each model
conclusions
Conclusions
  • Crash rates for non-interchange segments and those for interchange segments are different
  • Developing separate models for non-interchange segments and interchange segments is practical
  • Crash prediction models developed based on different modeling approaches performed fairly similar except for
    • Non-interchange segments with >4 lanes for NC
    • Interchange segments with 4 lanes for TN
future directions
Future Directions
  • Develop models for urban areas in other states
  • Develop models for other types of roadway facilities such as expressways
  • Use classification tree technique to fine-tune the grouping of traffic volume in ANOVA models
thanks
THANKS
  • THANKS FOR YOUR HELP
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