Build a system to predict if something can go wrong in a cab ride.

Question

Build a system to predict if something can go wrong in a cab ride.

Asked at Uber over a year ago

1.2k views

& 1 other company

Asked at

& 1 other company

1.2k views

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@Nag · Answer 1 · 2021-08-24T22:49:35+0000

This is a product design question

Step 1- Clarify qn

What does wrong mean? car breaking down, crime, cars refusing rides

Is there a specific region or platform under consideration

Specific geo, region?

Step 2- User group

1) Uber Monitoring/Call center agents

2) Emergency services, law enforcement

3) The cab drivers and riders

4) Business team at Uber(Legal, loss prevention etc.)

I will select Uber Monitoring/Call center agents are the target user group since they would be first point of interdiction for any issue/problem

Step 3- Pain points of this user group

1) Dashboard that shows that potential issues and type of issue that can take place

2) Understanding contributing factors and tracking progress

3) Communicating with various stakeholders

4) Automated workflow to address exceptions

5) What is the benchmark at which I need to escalate the issue(rather than being a false alarm)

6) Tracking progress on remediatory actions

7) Tracking competitors actions on the issue

Step 4- Prioritization of pain points

Priority 1- High relevance, High Impact

1) Dashboard that shows that potential issues and type of issue that can take place

2) Understanding contributing factors and tracking progress

3) Communicating with various stakeholders

4) Automated workflow to address exceptions

I will ignore the rest for the MVP

5) What is the benchmark at which I need to escalate the issue(rather than being a false alarm) -- > this can be secondary requirement as we are already caputuring contributing factors and tracking progress

6) Tracking progress on remediatory actions --> Secondary requirement as primary objective is a system that predict an issue

7) Tracking competitors actions on the issue--Same as above

Step 5- Solutions

1) Dashboard shows traffic in a given area along with events e.g. a reported traffic event, driver ratings, potential crime, upcoming weather scenario, airline delays, road construction etc.

2) Understanding contributing factors and tracking progress -->Related to (1)

3) Automated calling or message capability to various stakeholders e.g. police

4) Automated workflow to address exceptions -->Related to 3

Step 7: Solution Prioritization- What will be part of MVP?

High Priority

1) Dashboard shows traffic in a given area along with events e.g. a reported traffic event, driver ratings, potential crime, upcoming weather scenario, airline delays, road construction etc. --> This is the monitoring scope (Highest reach and impact, medium implementatio cost)

2) Understanding contributing factors and tracking progress --(Highest reach and impact, related to 1)

Medium Priority

3) Automated calling or message capability to various stakeholders e.g. police (Medium Impact, High/Medium cost) --> Need not be part of the immediate scope, components of this will already be done by existing systems

4) Automated workflow to address exceptions -->Related to 3

Simran Kohli · Answer 2 · 2024-09-19T08:32:54+0000

Problem Understanding
A successful system should predict incidents that could go wrong during a cab ride, including:

Driver issues (aggressive driving, drowsiness, poor behaviour)
Passenger issues (health issues, misconduct)
Vehicle issues (breakdowns, maintenance problems)
External factors (traffic, weather, unsafe areas)

Key Objectives:

Proactively detect and prevent issues by predicting risks in real time.
Alert relevant stakeholders (drivers, passengers, customer service) to take corrective action.
Improve overall ride safety and customer satisfaction.

User Groups

1. Drivers
2. Commuters
3. Local Authorities
4. Uber Risk & Customer Support Team

Since the first touchpoint for a user will be the Uber support team, we'll be taking that as our primary TG who we are building it for.

Solution
To collect data, devise an algorithm that identifies these risks and alerts the relevant stakeholders and also a UI that allows the team to monitor the rides to take preventive actions

1. Collect Data
The system must continuously collect and monitor data from various sources to identify risk factors

GPS Data: Monitor the cab’s speed, route, and location.
Telematics & Sensor Data: Track vehicle metrics like sudden acceleration, harsh braking, tyre pressure, and fuel levels.
In-App Driver and Passenger Feedback: Collect real-time feedback through the app to flag potential issues.
Weather and Traffic Data: Use external APIs to account for traffic conditions, road hazards, and weather disruptions.
Driver Behavior Data: Collect historical data on driving patterns (e.g., fatigue, speed violations, ride cancellations).
Driver’s Mobile Data: Monitor how often the driver is interacting with their phone, indicating potential distractions. [Not Sure if we can do that]
2. Potential Risks to Predict:

Aggressive or Unsafe Driving: Sudden acceleration, frequent lane changing, speeding, or harsh braking.
Vehicle Failure: Real-time data on engine health, tyre pressure, or fuel status to detect breakdown risk.
Driver Drowsiness or Fatigue: Track driver hours and patterns to identify when a driver may be tired.
Unsafe Route Selection: Using location data to detect dangerous routes, traffic accidents, or high-crime areas.
Passenger Health or Behavior Issues: Analyze historical ride data to flag risky passenger behaviour patterns (aggression, complaints).
External Disruptions: Predict traffic delays or weather-induced hazards (heavy rain, fog).

3. Predictive Algorithm:
The core of the system is a machine learning model that identifies patterns and predicts when something is likely to go wrong. The system should use both supervised learning and anomaly detection to flag potential issues.
- Input Variables:
  - Driver Profile: Past ride ratings, driving history, break time patterns, driving hours, age, and health status (if available).
  - Passenger Profile: Ride history, past complaints, cancellation behaviour, payment issues.
  - Vehicle Metrics: Age of the vehicle, maintenance history, mileage, real-time sensor data (fuel, engine health).
  - Route Data: Real-time traffic data, known accident-prone spots, and unsafe areas.
  - Environmental Data: Weather forecasts, time of day, road conditions
    
    The output here should be a risk score.

4. Risk Detection & Alerts:

Real-Time Alerts: The system should send real-time alerts if the risk score crosses a certain threshold.
- Driver Alerts: If the system detects unsafe driving (speeding, drowsiness), an alert is sent to the driver to take corrective action (slow down, pull over).
- Passenger Alerts: Notify passengers of potential disruptions (vehicle issues, unsafe routes) or prompt them to report concerns via the app.
- Support Team Alerts: Customer support teams are alerted for high-risk rides so they can monitor or take preemptive action.
Preventive Measures: Based on predictive insights, the system may automatically offer preemptive solutions, such as suggesting a safer route to the driver or providing the passenger with an option to cancel or switch the ride.

6. Post-Ride Analysis & Continuous Learning:

After each ride, the system should gather feedback and data to continuously update the predictive model.
Post-incident Review: If something goes wrong during a ride, the system records all contributing factors for future learning, refining the prediction accuracy.

7. User Interface (UI) & Experience:

Driver App Interface: The driver is shown real-time updates about their driving behavior, vehicle health, and any external risks (weather, traffic) with easy-to-read indicators and warnings.
Passenger App Interface: Passengers can see updates if any risks arise during the ride (e.g., vehicle issues, unsafe areas) and have the option to alert customer service or share feedback immediately.
Backend Dashboard for Admins: A real-time dashboard where operators can monitor high-risk rides, track flagged incidents, and proactively manage disruptions.

Key Metrics for Success:

Reduction in Ride Incidents: Measure the decrease in reported ride issues, such as breakdowns or driver complaints.
Accuracy of Predictions: Track how often the system correctly predicts potential ride risks versus false positives.
Customer Satisfaction: Monitor feedback ratings and NPS (Net Promoter Score) after implementing the prediction system.
Driver Satisfaction: Ensure that drivers feel supported by the system and that alerts help them drive safely rather than causing frustration.