The document summarizes research analyzing factors that predict wrong-way biking violations at a university campus. Data was collected over 14 hours and analyzed using logistic regression. The final model identified gender, having a bag while wearing sportswear, having a bag with higher speed, and biking within 5 minutes of class ending times as significant predictors. The model achieved an accuracy of 81.3% but suffered from low R2 values and significant overlap between predicted probabilities for violators and non-violators. Other classifiers like Parzen windows achieved slightly better accuracy.

1. Maisam Shahid Wasti and Dennis Hartono

2. Biking wrong way at Trousdale
We found that it is possible to predict wrong way
violations at Trousdale Parkway, USC Campus

3. Data Collection
Collected 14 hours of data with total sample size of 2837

4. Decision Rule to classify violators
Established a consistent decision rule for sample
validation

5. Overview of the observation site
Five minutes slot following class ending times
Observed higher proportion of violators for few minutes
after the classes end

6. Interpretation of important variables
Name: 5min_after
Five minutes slot following class ending times
Name
Type
'5min_after'
Binary
Description
Counted '1' if sample observed within 5 minutes slot following the class ending times

7. Interpretation of important variables
Used ‘bodyweight’ as a binary measure for speed

8. Approach to model selection
All non-interaction terms
Backward-LR
LOGISTIC REGRESSION
Initial Model
with selective non interaction terms
Backward-LR
+ (n,C,2) second order
terms
Intermediate Model
Filtration
Refined our model
in three stages
Final Model
after removing terms causing
Multicollinearity

9. Variables in final model
Variables
Significance
Gender
.003
Bag * Sportswear
.010
Bag * Bodyweight
.002
Bag * 5min_after
.001
Non-Interaction Terms
Interaction Terms
Found significant independent variables
We interpret bag as an indicator for student on campus

10. Model evaluation
Test
Statistic
Significance
Omnibus
33.518
0.000
Cox and Snell R2
0.015
Nagelkerke R2
0.024
• Observed significant improvement in Log-Likelihood
through Omnibus test
• Model suffered from low R2 values

11. Correlation Matrix
Gender
Bag * Sportswear
Bag * Body_weight
Bag * 5min_after
Multicollinearity Test 1
Gender
Bag * Sportswear
Bag * Body_weight
Bag * 5min_after
1.000
-.085
-.186
.041
-.085
1.000
-.028
-.015
-.186
-.028
1.000
-.007
.041
-.015
-.007
1.000
Found no serious multi-collinearity issues (>0.3)
with highest correlation coefficient of magnitude 0.186

12. Multicollinearity Test 2
Variables
B
S.E.
Wald
Sig.
Exp(B)
Gender
-.335
.112
8.936
.003
.715
Bag * Sportswear
.667
.258
6.671
.010
1.949
Bag * Bodyweight
.798
.258
9.605
.002
2.222
Bag * 5min_after
.409
.120
11.533
.001
1.506
Observed Standard Errors to be bounded by maximum
of 0.258

13. Residual Analysis
Observed no residuals lying above 2 standard deviation

14. Challenges with Classification Accuracy
- Have a skewed class distribution
2500
81.7 %
2000
2315
1500
1000
500
522
18.3 %
0
Violators
Non-Violators
- Resulting in high baseline accuracy
- Difficult to improve much from the high baseline accuracy

15. Predicted Probabilities Histograms
Violators
Non-Violators
- Observed significant overlap
- The default 0.5 gave a bad cut-off threshold

16. Classification Tables
Ground Truth
Baseline Classification Table
Prediction
Wrong Way Violation
Not Violating Violating
Percentage
Correct
Not Violating
465
0
100.0
Violating
107
0
0.0
Total
572
0
81.3
Ground Truth
Classification Table with 0.35 Cut-off
Prediction
Wrong Way Violation
Not Violating Violating
Percentage
Correct
Not Violating
463
2
99.6
Violating
105
2
1.9
Total
568
4
81.3
The overall classification accuracy remains the same with
increased prediction power for violations

17. The ROC Graph
Observed to be better at predicting violations than the
baseline at Cut-off = 0.35

18. Multiway Cross-tabulation tests
Wrong_Way_Violation * Formal_Dressing Crosstabulation
Count
Formal_Dressing
Wrong_Way_Violation
Not Violating
Violating
47
Total
2315
510
12
522
59
2837
16
Total
2315
Wrong_Way_Violation * Helmet Crosstabulation
Count
Count
Wrong_Way_Violation
Not Violating
Violating
Total
In formal
dress
2778
Total
Wrong_Way_Violation * Food_or_Beverages Crosstabulation
Not in formal
dress
2268
Food_or_Beverages
Without food
With food or
or beverage
beverage
2278
37
511
2789
11
48
Helmet
Total
2315
Wrong_Way_Violation
Violating
Wearing
helmet
Total
520
2
522
2819
522
2837
Not Violating
Not wearing
helmet
2299
18
2837
29
Total
2315
Wrong_Way_Violation * Hoodie Crosstabulation
Count
Hoodie
Wrong_Way_Violation
Not Violating
Violating
Total
Not wearing
hoodie
2286
Wearing
hoodie
517
5
522
2803
34
2837
Lacking significant number of violators for few cases

19. Other classifiers
Classifier
Accuracy %
Baseline
81.3
Logistic Regression
81.3
Parzen windows
81.64
Linear Perceptron
81.29
K-Nearest Neighbors
81.29
Experimented other classifiers to achieve a slight increase
in overall accuracy

20. Logistic Regression vs. Parzen Windows
Ground Truth
Parzen Window
Prediction
Wrong Way Violation
Not Violating Violating
Percentage
Correct
Not Violating
463
0
100
Violating
105
2
1..9
Total
570
2
81.64
Ground Truth
Logistic Regression (0.35)
Prediction
Wrong Way Violation
Not Violating Violating
Percentage
Correct
Not Violating
463
2
99.6
Violating
105
2
1.9
Total
568
4
81.30
Achieved slightly improved TPR/FPR and overall
classification accuracy using Parzen Windows

21. Questions…