Predictive analysis of traffic violations

Predictive Analysis of Traffic violations
Group 1

Introduction
Road accidents - big worldwide threat:
● Up to 1.27 million deaths/year [2].
● Up to 50 million injuries/year [2].
● Over 2.5 millions/year involved in US [2].
● Huge economic and social impact.
Data mining is arduous for traffic violations:
● Huge data size and high dimensions [2-4].
● Popularity of classification methods [3-5].
● High dependence on collected data [3-5].
● Testing multiple to choose the best [3-5].

Flow of Project
Project followed CRISP-DM model and provided instructions
A. Selecting a dataset from any free source on web.
B. Study the variables in the selected dataset; draw
preliminary conclusions; and develop at least three
initial research questions/hypotheses.
C. Develop a Business Use Case.
D. Use the visualization (min of 5) to explore dataset;
present research hypotheses, based on the
visualizations.
E. Produce a dataset satisfying all of the criteria in part c.
F. Present 3 modeling techniques for hypotheses.
G. Develop models using 3 algorithm for each model.
H. Provide recommendations based on modeling results.

Dataset Chosen
Traffic Violations of MCP: records from 2012 to 2018
from https://catalog.data.gov/ (Jan 17, 2018)
Attributes:
● Date of Stop
● Time of Stop
● Belts
● Contribution Accident
● Description
● Phone Used
● Fatal
● Property damage
● Violation Type
● Hazmat

Data Preprocessing
Missing Data Handling
● Two attributes i.e. “Agency” and “Accident” are removed from the dataset for being
single-valued attributes with value 'No' for every one of the records.
● Records with Null values are evacuated utilizing XL miners missing data treatment.
New Attributes
● Three new binary-valued variables are presented to be specific, "Phone Usage", "
Contributed to accident " and "Fatal" in light of the visualizations made for the
dataset.
Outliers detection
● Outliers are identified for the attribute "Year" and treated.
Filtering Datasets
● For each search question separate datasets were created for modeling.
● R programming language is utilized to accomplish balanced dataset with an
equivalent weight of target variables.

Visualizations - Violations based on hours

Visualizations - Child violations with year & month

Visualizations - Phone violations with year & month

Visualizations - Violations with Personal injury & seat belt

Search Hypothesis
1
Whether a violation
occurred contributed to
an accident or not
Whether the violation
that led to an accident
was fatal or not
2
The geolocation, at
which a violation is
likely to occur based on
several factors
3

Modeling
Model 1
Predictors:
● Belt
● Alcohol
● Vehicle Type
● Year
● Phone Usage
Target variable:
Contributed to accident
Model 2
Predictors:
● Belt
● HAZMAT
● Alcohol
● Phone Usage
Target variable:
Fatal
Model 3
Predictors:
● Belt, Personal Injury
● Property Damage
● Alcohol
● Violation Type
Target variable:
Cluster ID (geo-coordinates)

Model I: contrib. to accident
Algorithm Single Tree Random Trees Naïve Bayes
Precision 0.626 0.503 0.621
Sensitivity 0.275 0.996 0.266
Specificity 0.836 0.023 0.838
F1-Score 0.383 0.669 0.373
Belts
Phone usage
Alcohol
Type:
Bus
Best model: Random Trees due to highest
sensitivity and F1-score
Belts and phone usage are the top predictors
for contribution to accident

Model II: Fatality of accident
Alcohol
Phone usage
HAZMAT
Algorithm Single Tree Random Trees Naïve Bayes
Precision 0.579 0.639 0.579
Sensitivity 0.79 0.383 0.543
Specificity 0.404 0.776 0.59
F1-Score 0.668 0.478 0.561
Best model: Single Tree due to highest
sensitivity and F1-score
Alcohol and phone usage are the top predictors for fatal accidents

Model III: geolocation for “likely” violations
● K-means clustering and
Single Tree classification are
best algorithms due to
unbiased results
● Cluster 5 has the highest
number of alcohol violations
and personal injuries
● Cluster 9 has the highest
number of belt violations

Recommendations
> Recommend MCP to increase their attention and increase enforcement of rules
for belts and phone usage as the main reason for accident contribution
> Recommend MCP to pay an extra attention and take extra measures to drunk
drivers and phone usage when driving
> Alert Maryland police about the major areas violations are likely to be caused:
>> high number of alcohol violations with personal injuries in cluster 5
>> multiple belt violations in cluster 9
> Perform similar recommendations to the insurance companies in the state of
Maryland

Conclusion
● 5 visualizations have been produced and 3 research hypotheses developed;
● Data preprocessed in several datasets;
● 3 modeling technique is performed for hypotheses;
● Several classification, clustering and regression models have been considered for
modeling: Single Tree, Random Trees, K-Means clustering, Multiple regression, etc.;
● Random Trees and Single Tree are the best algorithms for models 1 and 2 due to an
importance of high sensitivity and a high F1-score;
● K-means clustering and Single Tree classification have been considered as the best
algorithms for model 3 providing numbers of different types of violations along with the
number of injuries for various clusters;
● XLMiner, Tableau, and R are used for analysis.

References
● Discovering Knowledge in Data: An Introduction to Data Mining, Daniel T. Larose and Chantal D. Larose,
Wiley, 2nd edition: Wiley ISBN 978-0-470-90874-7
● Abellan, J., Lopez, G., & De O~na, J. (2013). Analysis of traffic accident severity using Decision Rules via
Decision Trees. Expert Systems with Applications, 40, 6047–6054.
● Chang, L.-Y., & Chien, J.-T. (2015). Analysis of driver injury severity in truck-involved accidents using a non-
parametric classification tree model. Safety Science, 51(1), 17–22.
● Chen, W. H., & Jovanis, P. P. (2012). Method for identifying factors contributing to driver injury severity in
traffic crashes. Transportation Research Record, 1717, 1–9.
● Kashani, A. T., Rabieyan, R., & Besharati, M. M. (2014). A data mining approach to investigate the factors
influencing the crash severity of motorcycle pillion passengers. Journal of Safety Research, 51, 93–98.
● Kwon, O. H., Rhee, W., & Yoon, Y. (2015). Application of classification algorithms for analysis of road safety
risk factor dependencies. Accident Analysis and Prevention, 75, 1–15.
● Xie, Y., Zhang, Y., & Liang, F. (2009). Crash injury severity analysis using Bayesian ordered probit models.
Journal of Transportation Engineering ASCE, 135(1), 18–25.
● Mujalli, M. O., & de O~na, J. (2011). A method for simplifying the analysis of traffic accidents injury severity
on two-lane highways using Bayesian networks. Journal of Safety Research, 42, 317–326.
● De O~na, J., Lopez, G., & Abellan, J. (2013). Extracting decision rules from police accident reports through
decision trees. Accident Analysis & Prevention, 50, 1151–1160.

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