1. LOGO ITE Presentation June 27, 2012 Presenter:Jung-Han Wang

2. Contents 1. Introduction 2. Literature Review 4. Data Description 3. Methodology 5. Result 7. Q&A 6. Conclusion

3.  Speed limit should be set realistically for the majority of drivers on the road  Previous researches have treated the predictor variable for a certain speed limit as exogenous  Single equation modeling techniques used by previous researches have resulted in widely variable data  Research was delivered by running single equation models individually involving crash counts, speed limits and then comparing them with a simultaneous equation model (SEM) 1.Introduction1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description 4. Data Description 5. Results 6. Conclusions 7. Q & A

4.  Single equation modeling techniques used by previous researches have resulted in widely variable data Single equation modeling techniques will result in widely Variable data It is anticipated to obtain less biased estimators by using simultaneous equation models. 1.Introduction1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description 4. Data Description 5. Results 6. Conclusions 7. Q & A

5. 1973 1. NMSL speed limit is 55 mph, but actual speed limit varied from state to state. 1987 1995 National Maximum Speed Law 2. Congress permitted states to raise speed limits to 65 mph (105 km/h) on rural Interstate highways 3. Repeal of federal limits. Federal returns all speed limit determination authority to the states. 1.Introduction 2.LiteratureReview2.LiteratureReview 3. Methodology 4. Data Description 4. Data Description 5. Results 6. Conclusions 7. Q & A

6. Speed Limit Increment and Accidents California 55–65 mph increase in collision 65-70 mph not significantly change 55-65 mph not significantly change 65-70 not significantly change North Carolina urban interstate highway increase in acc counts Rural interstate highway not significantly change Utah 1.Introduction 2.LiteratureReview2.LiteratureReview 3. Methodology 4. Data Description 4. Data Description 7. Q & A 5. Results 6. Conclusions

7. 1.Introduction 2.LiteratureReview2.LiteratureReview 3. Methodology 4. Data Description 4. Data Description 5. Results 6. Conclusions 7. Q & A  AZ Department of Transportation i.Speed zoning in Arizona is based on 85 percentile of the drivers are traveling. ii.This speed is subject to downward revision based upon such factors as: accident experience, roadway geometrics, and adjacent development

8. 1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description 4. Data Description 7. Q & A 5. Results 6. Conclusions  Model Selection Poisson distribution restricts the mean and the variance to be equal: (E[y i ] = VAR[y i ]). When this equality does not hold, the data are said to be under dispersed (E[y i ] > VAR[y i ]) or over dispersed (E[y i ] < VAR[y i ]). So Negative Binomial Model was chosen

9.  Traditional Model for Crash Counts Negative Binomial Model λi = EXP (βxi +εi = EXP (βxi) * EXP (εi) where λ i = Accident Counts x i = Speed Limit ε i = Error Term β = coefficient of x i 1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description 4. Data Description 7. Q & A 5. Results 6. Conclusions

10.  Simultaneous Equation Model Negative Binomial Model λi = EXP (β 1 xi +ε 1 i = EXP (β 1 xi) * EXP (ε 1 i) Multiple Linear Regression Model x i = β 2 λ i +ε 2i where λ i = Accident Counts x i = Speed Limit ε 1i, ε 2i = Error Term 1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description 4. Data Description 7. Q & A 5. Results 6. Conclusions

11. Collect Data Set up Model In R Set up Model In R Single Equation Model (NB) Simultaneous Equation Model (NB+MLR) Compare Results Summary Research Procedure Model for Minor Road Model for Minor Road Model for Major Road Model for Major Road Simultaneous Equation Model (NB+MLR) Single Equation Model (NB) Compare Results 1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description 4. Data Description 7. Q & A 5. Results 6. Conclusions

12. Data Retrieved  City of Corona Locations: 298 intersections Duration: 2000 to 2009 Crash types: Rear end, head on, side swipe, broad side, hit object, over turn, vehicle vs. pedestrian, etc. 10 different types total. Crash severities: fatal, severe injury, other visible injury, complaint of pain, and non-injury  Crash Type  Severity 1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description Description 7. Q & A 5. Results 6. Conclusions

13.  Comparison Coefficient for Major Road Approach Single Equation Model for Crashes Estimated Coefficient t-statisticp-value Constant-3.5021-3.010 Log of AADT on Major Road0.42153.570 Log of AADT on Minor Road0.26153.210 SPDLIMAJ0.35431.850.03 SPDLIMIN0.21121.780.35 PEDMAJ0.45184.210.04 α (dispersion parameter) 0.43874.530 1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description 4. Data Description 7. Q & A 5. Results 6. Conclusions Simultaneous Equation Models on Major Road Estimated Coefficient t-statisticp-value Equation1: Crashes (dependent variable) Constant-2.6035-5.430.000 Log of AADT on Major Road0.56926.910.000 Log of AADT on Minor Road0.11282.450.000 SPDLIMAJ0.46762.330.012 PEDMAJ0.55607.420.020 α (dispersion parameter) 0.43874.530.000 Equation2: Speed Limit on Major Road (dependent variable) Constant-2.7863-3.630.001 Log of AADT on Major Road-0.4216-2.340.001 Number of crashes2.03204.670.045 Number of lanes on major road0.02351.980.065 Number of driveways on the major road within 250 ft of the intersection center -0.1158-1.560.076

14.  Comparison Coefficient for Minor Road Approach Single Equation Model for Crashes Estimated Coefficient t-statisticp-value Constant-3.5021-3.010 Log of AADT on Major Road0.42153.570 Log of AADT on Minor Road0.26153.210 SPDLIMAJ0.35431.850.03 SPDLIMIN0.21121.780.35 PEDMAJ0.45184.210.04 α (dispersion parameter) 0.43874.530 1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description 4. Data Description 7. Q & A 6. Conclusions Simultaneous Equation Models on Minor Road Estimated Coefficient t-statisticp-value Equation1: Crashes (dependent variable) Constant-2.8033-4.670.000 Log of AADT on Major Road0.48955.640.001 Log of AADT on Minor Road0.19882.430.001 SPDLIMAJ0.28761.120.026 SPDLIMIN0.18871.230.282 PEDMAJ0.56417.530.035 α (dispersion parameter) 0.22662.670.001 Equation2: Speed Limit on Major Road (dependent variable) Constant4.22688.250.000 Log of AADT on Major Road-0.0231-0.930.006 Number of crashes1.45263.540.280 Number of lanes on major road0.17612.180.007 Number of driveways on the major road within 250 ft of the intersection center-3.6887-6.820.084 5. Results

15.  Comparison Coefficient for Major Road Approach 1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description 4. Data Description 7. Q & A 6. Conclusions 5. Results Simultaneous Equation Models on Major Road Estimated Coefficient t-statisticp-value Equation1: Crashes (dependent variable) Constant-2.6035-5.430.000 Log of AADT on Major Road0.56926.910.000 Log of AADT on Minor Road0.11282.450.000 SPDLIMAJ0.46762.330.012 PEDMAJ0.55607.420.020 α (dispersion parameter) 0.43874.530.000 Equation2: Speed Limit on Major Road (dependent variable) Constant-2.7863-3.630.001 Log of AADT on Major Road-0.4216-2.340.001 Number of crashes2.03204.670.045 Number of lanes on major road0.02351.980.065 Number of driveways on the major road within 250 ft of the intersection center -0.1158-1.560.076

16.  1. From all the 298 intersections that were analyzed, there was no significant difference in the results accounting and not accounting for endogeneity since all the signs associated with different coefficients remain the same.  2.The differences illustrated in the magnitude of the coefficients also suggest one might make erroneous judgment if the endogeneity between speed limit and accidents are totally ignored.  3.The study indicates crashes are endogenously related with a speed limit on major approach  4.Re-estimate the predictor variables by running the models with only the most significant variables 1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description 4. Data Description 7. Q & A 5. Results 6. Conclusions

17. LOGO Q & A 1.Introduction 2.Literature Review 2.Literature Review 3. Methodology 4. Data Description 4. Data Description 5. Conclusion 6. Recommen- dation 6. Recommen- dation 7. Q & A