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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 Transportation Planning

  • 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? Transportation Planning

  • No previous work recognized the influence of interchanges on crashes explicitly


Impact of interchanges on crashes
Impact of Interchanges on Crashes Transportation Planning

  • 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


Definition of segments used in this research

Non-interchange segments Transportation Planning

Interchange segments

1,500’

1,500’

Definition of Segments Used in this Research


Crash rates for nc tn
Crash Rates for NC & TN Transportation Planning

Unit: crashes per Million Vehicle Miles Traveled


Crash rates for nc tn1
Crash Rates for NC & TN Transportation Planning

Unit: crashes per Million Vehicle Miles Traveled

1 2000-2002 statewide crash report, NCDOT


Proportion of crash types
Proportion of Crash Types Transportation Planning

12000-2002 statewide crash report, NCDOT

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


Model development scheme
Model Development Scheme Transportation Planning

  • 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 Transportation Planning

  • 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 Transportation Planning

  • NC Counties:

    • Alamance, Buncombe, Cumberland, Durham, Gaston, and Wake

  • TN Counties:

    • Davidson (Nashville), Hamilton (Chattanooga), Knox (Knoxville), Shelby (Memphis)


Model classification
Model Classification Transportation Planning


Crash prediction models
Crash Prediction Models Transportation Planning

  • 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 Transportation Planning

  • 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 Transportation Planning

Total = Fatal + Injury + PDO


Example pdo crash rates for tn
Example: PDO Crash Rates for TN Transportation Planning


Total crash rates for tn
Total Crash Rates for TN Transportation Planning

Total = Fatal + Injury + PDO


Regression models
Regression Models Transportation Planning

  • 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.) Transportation Planning

  • 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


Example models for non interchange segments of nc
Example: Models for Non-Interchange Segments of NC Transportation Planning

Note: R2 is special type (Rk2)


Example models for interchange segments of nc
Example: Models for Interchange Segments of NC Transportation Planning

Note: R2 is special type (Rk2)



Predicted pdo crashes for nc models
Predicted PDO Crashes for NC Models Transportation Planning


Predicted total crashes for nc models
Predicted Total Crashes for NC Models Transportation Planning



Predicted pdo crashes for tn models
Predicted PDO Crashes for TN Models Transportation Planning


Predicted total crashes for tn models
Predicted Total Crashes for TN Models Transportation Planning


Findings of graphical analysis
Findings of Graphical Analysis Transportation Planning

  • 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 Transportation Planning

  • 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.) Transportation Planning

  • Number of models

    • 2 for NC

    • 2 for TN






Findings for classification tree models
Findings for Classification Tree Models Transportation Planning

  • 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 Transportation Planning

  • 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 Transportation Planning

  • 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 Transportation Planning

  • 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 Transportation Planning

  • THANKS FOR YOUR HELP


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