Case Study: How to make a bike taxi ride safe?
Bike taxi’s by Ola, Uber and other players in Asia and South East Asian countries are quite popular.
Due to rising population, traffic jams, rising cost of living, bike taxi’s are cheaper alternative to car taxi’s.
You can board a bike taxi in minutes and it cost fraction of a car taxi ride.
However the bike taxi has its own disadvantages. Primarily it is the safety issue which plaques many people.Â
Two wheeler accidents are on the rise and it is terrifying while you are riding a bike taxi.
We asked product managers and professionals about their experience with bike taxi and how they can make it safer.
We got tons of replies and here are some of the best ones.
I feel this problem has a immediate, short and long term solution from traveller’s perspective.
Immediate solution (How can traveller benefit from product solution at the time of his ride ?)
1. Continuously monitor bike ride speed (distance covered through GPS tracking/ time) and give an in-app notification (Red colored warning) to driver signalling over speed
2. Give an option to traveller in app to notify ‘in danger’ signal due to speed which again is notified to driver as a warning.
3. If driver is found to break rules as per point 2-3 mentioned above, charge a fine to him or cut short his incentive/ fee.
Short term solution –
1. Give feedback to drivers on their navigation on daily and weekly basis. Kind of leaderboard for all drivers around in locality which tells which driver has satisfied most customers today.
2. in tha case of heavy traffic, find a different route for travelling.
Long term solution –
1. Training, incentivising drivers on navigation, driving etc.
The problem of rash driving could be managed using a simple automated solution in place.
As a PM, I would gather data from the driver’s mobile accelerometer.
In certain traffic conditions, at certain speeds and basis the driver’s turning & acceleration patterns, I would be able to model & quantify the ‘rashness’.
We could use Supervised Machine Learning for training the model (on rash driving rating given by the customer) and then use it as a way to automatically grade the driving style of a driver in general and also offer warnings in real-time whenever their performance falls below standard.
Rashness was just one aspect of safety. More preventive.
The other aspect is protection & emergency response in an unfortunate event of collision.
Mandating helmets (let alone elbow and knee guards) sounds easier than done, especially given the current rule-following behaviors of most users & drivers alike.
1. I would equip all bikes with strong collision guard on the sides. At least prevent some injury by the virtue of a permanent physical protection on the bike. One time investment, high safety return.
2. In the case of a collision, the accelerometer reading should trigger an automated emergency call to the nearest hospital & police. Of course that means tying up with the government bodies to pull this off effectively but I’m sure that would be possible.
3. Preventive – I would enable ‘rashness alarms’ to warn drivers. If it matches with customer’s bad feedback, penalize strongly. If the customer gives a positive feedback regardless, we can assume the customer was ok with it and zero/minimal penalty could be applied.
Inversely, a small reward to reinforce good behavior would be great too. Could be useful for the insurance claims process too.
Even if we have checks in place, there is going to be rash driving.
Also the definition of rash driving differs from individual to individual.
We can have some quick rules like Mandatory Helmets for pillion rider as well as some safety jacket or something for the rider as well as passenger.
Post that we can think of putting some controls in place before onboarding the driver. Once that is placed, we can have some controls that can regulate the riders and rides if they are still compliant every month or quarter.
Also, looking at ratings, we can have some checks placed to get the safe riding. Or a simple solution can be to add an option to report Rash driving. More the votes, more likely the driver is to lose Ola license.
If the customer is driving the bike then he will feel way safer and comfortable then any other driver whom he doesn’t trust. it’s a basic human tendency. So, an option which I feel suitable is to give the customer a chance to ride the bike if he wants.Â
Rash driving means sudden brakes.
Three (or 5) sudden brakes in a km and:
1. he does not get paid for the particular ride ( along with rating going down).
2. Use app based locking to turn off the bike and disable it from starting. Or easier to do is lock the gear at neutral (requires physical lock integration instead of changing the electronics of the bike.
3. He doesn’t get to pick another ride for that day ( sometimes they just have a bad day)
4. Immediately send another ride to the user. Alternatively book a cab for him (expensive but can be additional penalty on the rider)
5. All this with consent of the user. Allow user to share cab expenses etc. users are the best to tell whose fault it was. Also user only consents to unlock the bike (otp?)
6. Preempt rash behavior by providing insurance to user and bike through premiums paid by rider.
(With no-claim benefits every month) This way neither the company suffers loss (maybe a little but that’s opex) nor user suffers from anxiety or accident.
I think to solve the problem one could have a defined metrics according to which the driver is graded and penalized accordingly.
The metrics could be:
1. Adhering the speed limits
2. Keeping a tab on the acceleration and declarations rates because the faster the rates are would clearly show signs of rash driving
3. Rating the driver on multiple metrics rather than simple star rating to understand about the quality of the ride better.
Lastly, Regular servicing of the vehicle and driver tests could be conducted to ensure quality.
Here’s how I will try and structure the solution.Â
The stakeholder is the driver and the pillion is the user.
Anything silly by the driver would be trouble for the user.
Therefore, using GPS/Accelerometer to find the current speed and check that with the average speed of the other vehicles on the similar route from Google Maps.
Using this metric, give the ride a score. Now, the user can be offered a small insurance-like product & if any mishap happens on the road, the insurance money will be paid from the salary of the driver, provided the score for the ride falls below a certain metric.Â
I think win-win for both parties.Â
Causes fear acceleration and deceleration(rate of speed changes through mobile location), zig zag driving (gyroscope in mobile), speed.
Tracking these metrics. Compare with the average speed on road(google maps, etc) for speed bench marking.
Full face use and throw covers rather than just the head.
User Personalisation : some might be concerned more about time than safety
user feedback, metrics tracked – can help normalizing and assignment of driver for better CSAT.
Driver training is altogether different, based on incentives – mostly based on existing success stories(ola, uber , swiggy etc), solved problem by people already, domain experts can share the best, tried and tested solution.
The driver app must monitor the speed and there must be a cap on that.
As soon as the driver goes beyond that limit, app will start to beep and will ask the driver to slow down.
Also, there must be a degradation in rating of the driver and an incentive cut if the driver keeps on over speeding for 5 times a month (this can be decided based on the events happening).
Fitting an over speeding (> set limit) sound and visible flash light alert on the bike.
This should be settable by driver, looking at customer’s preference before start of each ride. The speed limit can be set by passenger on ride sharing agrregator’s app (like acceptable urgent, moderate and safe, for which appropriate speed limit to be defined depending on time of ride and type of city).
Further, the driver’s should be monitored and managed based on number of instances he fails to follow the rules.Â
Problem 1 – Rash driving by bike drivers
Solution
A system to track and manage the driving performance, which can use sensors on smart phone to measure and improve driving behaviour.
It will do scoring based on how the drivers are driving giving a cumulative score as well as the scoring trends based on speeding and aggressive driving.
The total pay of driver should be dependent on this score. Also, there can be collision detection technology which will recognize vehicle crashes and alerts the emergency contact.
Problem 2 – Damaged helmets or other safety concerns
Solution
Weekly or monthly service where the helmets and bikes will be checked can be done to ensure safety.
Natural perspective for bike riding is that it is fast because it can zip through the traffic situation in our big cities.
The negatives can be addressed through multi pronged initiatives :
Driver Oriented :
1. Proper training and regular check follow ups to make sure that they are focused on the safety of the traveler along with themselves.
2. Speed limiters on bikes ( 50-60 kmph ), rash driving alerts through accelerometer/GPS.
3. Revisiting incentives based not just on number of trips but also the reviews that are going to be received or special incentives for better rated drivers.
4. Regular check-up of vehicles. and updating the safety equipment ( helmets mostly in current scenario)5. Instead of just helmet, maybe the riders and drivers can be equipped with elbow and knee protectors.
Rider Oriented :
1. Increase the safety awareness among people to make sure they wear helmets and also force the driver to wear the same.
2. Ride Insurance package for regular riders at cheap costs.
I think probably Giving scooters/scootys as an option to choose in bike taxis selection.
Scooters seem to be more reliable and safe and do not speed beyond a threshold.
Incentivizing drivers who promote and start using scooters and customers wont hesitate to pay a marginal premium if the right message is spread around.
Also scootys can be easily driven around at a very steady pace by women which can in turn bring in a different customer base (like middle aged ladies who would rely more on women drivers) and similarly scooters can attract more middle aged men.
The logic obviously has to be there in systems/technology to ensure that female customers only get female drivers and respectively.
Win win for everyone I believe.
Stringent rash driving alerts (accelerometer+GPS), speed limiters @60kph, weekly helmet servicing and cleaning, regular taxi-driver skill tests / monitoring, in-app optional affordable ride insurance (@INR 5 or something) before commencing ride (though not sure how that’ll pan out from a user’s perspective).
We can define a safety index.
It can be calculated from the combination of parameters like ride time, route, city, traffic density etc.
Every ride will come under 3 zones based on safety index : Red, Amber, Green.
Red zone is high traffic or accident prone route. This precautionary alert can be given to the bike rider & the customer during & after the booking.
This, when clubbed with actual Mobile sensor readings during the journey + actual customer feedback will help make bike ride more safer.
The objective is to consider the route dynamics also & not only bike ride.
