The aim of this project is to, Analyze and capture driving behaviors that are hazardous, Develop a predictive model for predicting Unsafe Trips and Improve the overall Safety of the ride-hailing services

1. Improving the Safety of
Ride Hailing Services
- Using IOT Analytics

2. Project
Overview
The Industry
• Ride-hailing Taxi Service Market to Garner $126.52 Billion by
2025 at 16.5% CAGR.
• Primary reason for such a growth rate being rising trend of on-
demand transportation services, high-end employment
opportunities and lower rate of car ownership among
millennials
The Challenge
• Even since the emergence of these Ride hailing companies,
there has been an increase in 3 to 5 % of accidents.
• Many Research concludes that fleet or company drivers have an
increased crash risk relative to that of privately registered
vehicle drivers.
The Objective
• Analyze and capture driving behaviors that are hazardous
• Develop a predictive model for predicting Unsafe Trips
• Improve the over all Safety of the ride hailing services

3. IOT & Analytics – a
Powerful Combination
• Internet of Things (IOT) –
• By 2025, there will be 116 million IOT enabled cars in the
U.S. And each connected car will upload 25 GB of data per
hour (~ 219 TB / Yr.)
• IOT – a costly piece of technology, is it?
• Well, no it isn’t anymore
• All average smartphones today are equipped with basic
embedded low-cost telemetry sensors such as
accelerometers, gyroscopes, GPS etc.
• Role of IOT Analytics
• Large sets of driving data from GPS and telemetry sensors
allows for exciting new research possibilities using advance
Analytics and Machine Learning techniques
• Insights from IOT Analytics help address key concerns
facing industries

4. IOT Sensors – Accelerometer
& Gyroscope
• What is an Accelerometer?
• Accelerometer sensor reports the acceleration of the device along the 3 sensor
axes (X, Y, Z)
• The measured acceleration includes both the physical acceleration (change of
velocity) and the gravity
• All values are in SI units (m/s^2)
• What is a Gyroscope?
• A gyroscope sensor reports the rate of rotation of the device around the 3 sensor
axes (X, Y, Z)
• Rotations can be of 3 different types
• Pitch – Pitch is for Y axis rotational rate in (rad/s)
• Roll – Roll is for X axis rotational rate in (rad/s)
• Yaw – Yaw is for Z axis rotational rate in (rad/s)

5. Solution Architecture Diagram

6. Exploratory Data
Analysis
Acceleration X, Acceleration Z,
Gyro Y show a significant
difference when the trip is
classified as Unsafe. These are
initial pointers that these features
may play significant role in
determining the trip type

7. EDA (Contd.)
• Speed
• Lower speed bins have higher
percentage of unsafe rides.
Reasons could be
• Driving below speed limits
• Talking or Texting while driving
• Duration
• Longer durations Trips have
higher percentage of unsafe rides.
Reason could be
• Drowsy driving due to longer driving
hours
• Customers tendency to rate longer
drivers as uncomfortable
• Clearly both Speed and Trip
Duration have a significant role
in classifying a Trip Quality

8. Telemetry Data Processing Methods
• Removing Noise component from Raw Accelerometer and Gyroscope Data using Low Pass Filter
alpha = 0.8
Filter BEGIN
Low_X = alpha × Prev_Low_X + (1 − alpha) × Curr_X
Low_Y = alpha × Prev_Low_Y + (1 − alpha) × Curr_Y
Low_Z = alpha × Prev_Low_Z + (1 − alpha) × Curr_Z
Filter END
• Removing Gravity component from the Accelerometer data
𝑎ℎ𝑜𝑟 = 𝐴𝑐𝑐𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 − 𝐴𝑐𝑐𝑔
Where 𝐴𝑐𝑐𝑔 is the average of acceleration over a window size of 100 seconds
• Calculate Magnitude or Total Acceleration / Total Angular Velocity
𝑎𝑐𝑐𝑅 = 𝑎𝑥2 + 𝑎𝑦2
+ 𝑎𝑧2
𝑎𝑐𝑐𝐻 = 𝑎𝑥ℎ𝑜𝑟
2
+ 𝑎𝑦ℎ𝑜𝑟
2
+ 𝑎𝑧ℎ𝑜𝑟
2
𝑣𝑒𝑙𝑅 = 𝑣𝑥2 + 𝑣𝑦2
+ 𝑣𝑧2
𝑣𝑒𝑙𝐹 = 𝑣𝑥𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑
2
+ 𝑣𝑦𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑
2
+ 𝑣𝑧𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑
2
Where,
ax, ay, az are raw accelerations along x, y, z axis respectively
𝑎𝑥ℎ𝑜𝑟 , 𝑎𝑦ℎ𝑜𝑟 , 𝑎𝑧ℎ𝑜𝑟 are filtered accelerations with gravity treatment done
vx, vy, vz are raw angular velocity along x, y, z axis respectively
𝑣𝑥𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 , 𝑣𝑦𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 . 𝑣𝑧𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 are filtered angular velocity
Data Processing (2)
Noise
Treatment
• (2A) Treating Raw data through LPF
Gravity
Treatment
• (2B) Remove Gravity from
Accelerometer Raw data
Calculate
Magnitude
• (2C) Calculate Magnitude of
Acceleration and of Angular Velocity

9. Raw Vs. Filtered Telemetry Data

10. Significant Driving Events and
Patterns
• STEP 1 – Aggressive Turn Events
• Turn events is based on filtered gyroscope energy (𝑣𝑒𝑙𝐹)
• velF >= 0.025
• STEP 2 – Aggressive Acceleration / Breaking Events
• Acceleration (braking/accelerating) events identification,
from vehicle acceleration component information (accH)
• accH >= 0.002
• STEP 3 – Zig-Zag Events
• Zig-zag events are identified as two or more change lanes
from the significant measurements of the accelerometer
(accH) with very less angular Velocity (velR)
• velF <= 0.0025 & accH >= 0.0013
• STEP 4 – Normal Events
• Any instance that are not classified as one of the above 3 are
classified as normal events
Thresholds For Event Identification
Thresholds are identified using a combination of mean, Outliers and trial
and error method

11. Predictive Modeling & Validation
Model Precision Recall F1-Score Accuracy AUC
GNB 0.46 0.28 0.35 0.7483 0.656
CART 0.34 0.36 0.35 0.6781 0.569
LOGIT 0.70 0.15 0.24 0.7810 0.683
RF 0.48 0.20 0.29 0.7576 0.627
KNN 0.42 0.25 0.31 0.7387 0.618
ANN 0.75 0.14 0.23 0.7828 0.691
XGB 0.75 0.15 0.25 0.7844 0.691

12. Conclusion &
Recommendations
Predicted Unsafe Rides
• Alerted on real time basis to respective drivers giving them
opportunity to correct it
• Alert the drivers on driving behaviors that are Unsafe
Performance Evaluation
• Drivers can be evaluated based on their driving behaviors thus
encouraging better driving practices among drivers
Organizing Mandatory Trainings
• With specific importance on Feature’s relative importance from
Models
• Going slower that designated speed limits seems to have caused
more Trips unsafe
• Higher Trip Duration tend to be more unsafe. Encourage drivers
to take short / traffic less routes
• Advised to avoid Zig-Zag movements as they cause more trips
unsafe

13. Appendix

14. Appendix (Contd.)

15. Appendix (Contd.)