Topic: CO2 emissions and electric cars
Due 10 hours, Report and Presentation
I attached a similar type of report
DS 408: Project Proposal Report
Simulating Ridesharing Apps (Supply & Demand)
Ridesharing apps have grown exponentially in the last 15 years, even dominating over
traditional taxi markets. One major issue with these apps that can create detractors is long
waiting times. This is a problem we personally experienced when using these platforms.
Waiting times can lead to a drop in revenue, consumer satisfaction and user retention. Since
there are multiple causes behind them and we don’t have access to their backend, we’ve
decided to simulate them and see if we can make improvements or resolve bottlenecks.
We are setting up a simulation model that matches supply & demand (i.e. drivers & riders)
based on an approximate location, combined with open-source traffic data from a ridesharing
giant. Our goal is to identify the bottleneck and implement a continuous solution that will
create a better user experience for both the rider and the driver. We can use Excel to collect,
organize and process data and SIGMA to design a model with varying parameters.
If successful, we can attempt to generalize and extrapolate our supply & demand matching
algorithm to other use cases, and possibly sharing it in the open-source community.
How Ridesharing Operates
When a rider submits a request, it’s submitted in the back-end and a matchmaking process
begins between all currently active drivers within a reasonable distance. All active drivers will
receive a notification and have the option to accept or deny (or ignore) the request. To make
things more complicated, riders can choose between different driver options; carpooling,
single car, luxury cars, large vans, EV’s or even cars with disability provisions. So these types
of drivers, along with who’s currently on the road, will create a supply distribution that we’ll
need to simulate in SIGMA. Once en route, it may be possible for the driver to accept another
rider, and may make one or more stops before reaching the final destination.
Our Simulation Approach
We will start with the basic model of accepting a request from a driver and matching it with
the current supply of drivers. Once we set this up, we can add more factors, such as different
driver options, roadblocks or traffic jams, surge pricing and disruptions caused by events.
We will assess where drivers are affected by high demand, and where consumers run the
problem of not having a driver. The main objective is to identify the factors which cause
extended wait times.
Type of vehicle
1. Using Excel
We will use the Uber Movement portal to obtain open-source traffic data in CSV format, which
can be imported and organized into Excel. We can also use Excel to create random
distributions for the riders’ demand and drivers’ supply. This will help us determine best and
worst case scenarios, as well as peak times and bottlenecks which cause long wait times.
2. Using Sigma
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